# messages_fts_content

c0:
    [{"id": "chatcmpl-tool-9e9d45a37e42468c", "call_id": "chatcmpl-tool-9e9d45a37e42468c", "response_item_id": "fc_chatcmpl-tool-9e9d45a37e42468c", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/html-anything/package.json\"}"}}, {"id": "chatcmpl-tool-b3c6cead20bda7bd", "call_id": "chatcmpl-tool-b3c6cead20bda7bd", "response_item_id": "fc_chatcmpl-tool-b3c6cead20bda7bd", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/html-anything/next/package.json\"}"}}]


c0:
  {"content": "1|{\n2|  \"name\": \"@html-anything/workspace\",\n3|  \"version\": \"0.1.0\",\n4|  \"private\": true,\n5|  \"license\": \"Apache-2.0\",\n6|  \"packageManager\": \"pnpm@10.33.2\",\n7|  \"devDependencies\": {\n8|    \"tsx\": \"^4.22.1\"\n9|  }\n10|}\n11|", "total_lines": 10, "file_size": 194, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|{\n2|  \"name\": \"@html-anything/next\",\n3|  \"version\": \"0.1.0\",\n4|  \"private\": true,\n5|  \"license\": \"Apache-2.0\",\n6|  \"scripts\": {\n7|    \"dev\": \"next\",\n8|    \"build\": \"next build\",\n9|    \"start\": \"next start\",\n10|    \"typecheck\": \"tsc --noEmit\",\n11|    \"test\": \"vitest run\",\n12|    \"test:watch\": \"vitest\"\n13|  },\n14|  \"dependencies\": {\n15|    \"clsx\": \"^2.1.1\",\n16|    \"diff\": \"^9.0.0\",\n17|    \"dompurify\": \"^3.4.2\",\n18|    \"highlight.js\": \"^11.11.1\",\n19|    \"idb\": \"^8.0.3\",\n20|    \"jszip\": \"^3.10.1\",\n21|    \"juice\": \"^11.1.1\",\n22|    \"lucide-react\": \"^1.14.0\",\n23|    \"marked\": \"^18.0.3\",\n24|    \"modern-screenshot\": \"^4.7.0\",\n25|    \"next\": \"16.2.6\",\n26|    \"papaparse\": \"^5.5.3\",\n27|    \"pptxgenjs\": \"^4.0.1\",\n28|    \"react\": \"19.2.4\",\n29|    \"react-dom\": \"19.2.4\",\n30|    \"xlsx\": \"^0.18.5\",\n31|    \"zustand\": \"^5.0.13\"\n32|  },\n33|  \"devDependencies\": {\n34|    \"@tailwindcss/postcss\": \"^4\",\n35|    \"@types/dompurify\": \"^3.2.0\",\n36|    \"@types/node\": \"^20\",\n37|    \"@types/papaparse\": \"^5.5.2\",\n38|    \"@types/react\": \"^19\",\n39|    \"@types/react-dom\": \"^19\",\n40|    \"fake-indexeddb\": \"^6.2.5\",\n41|    \"happy-dom\": \"^20.9.0\",\n42|    \"tailwindcss\": \"^4\",\n43|    \"typescript\": \"^5\",\n44|    \"vitest\": \"^4.1.6\"\n45|  }\n46|}\n47|", "total_lines": 46, "file_size": 1091, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
    [{"id": "chatcmpl-tool-9f1fe41527f9172b", "call_id": "chatcmpl-tool-9f1fe41527f9172b", "response_item_id": "fc_chatcmpl-tool-9f1fe41527f9172b", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"find /home/ice/html-anything/next/src/app/api -maxdepth 2 -type f | sort\"}"}}]


c0:
  {"output": "/home/ice/html-anything/next/src/app/api/agents/route.ts\n/home/ice/html-anything/next/src/app/api/convert/route.ts\n/home/ice/html-anything/next/src/app/api/deploy/route.ts\n/home/ice/html-anything/next/src/app/api/draft/route.ts\n/home/ice/html-anything/next/src/app/api/marketplace/route.ts\n/home/ice/html-anything/next/src/app/api/templates/route.ts", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-86bdf2d56326e10c", "call_id": "chatcmpl-tool-86bdf2d56326e10c", "response_item_id": "fc_chatcmpl-tool-86bdf2d56326e10c", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 200, \"offset\": 1, \"path\": \"/home/ice/html-anything/next/src/app/api/convert/route.ts\"}"}}, {"id": "chatcmpl-tool-b29fa7d26a07e065", "call_id": "chatcmpl-tool-b29fa7d26a07e065", "response_item_id": "fc_chatcmpl-tool-b29fa7d26a07e065", "type": "function", "function": {"name": "search_files", "arguments": "{\"limit\": 5, \"output_mode\": \"files_only\", \"path\": \"/home/ice/html-anything/next/src/lib/agents\", \"pattern\": \"*.ts\"}"}}]


c0:
  {"content": "1|import { NextRequest } from \"next/server\";\n2|import { invokeAgent } from \"@/lib/agents/invoke\";\n3|import { loadSkill } from \"@/lib/templates/loader\";\n4|import { assemblePrompt } from \"@/lib/templates/shared\";\n5|\n6|export const runtime = \"nodejs\";\n7|export const dynamic = \"force-dynamic\";\n8|\n9|type Body = {\n10|  agent: string;\n11|  templateId: string;\n12|  content: string;\n13|  format?: string;\n14|  model?: string;\n15|  cwd?: string;\n16|  /**\n17|   * Optional absolute path to the agent binary. The Settings UI lets the\n18|   * user override auto-detection when their CLI lives somewhere our PATH\n19|   * scan doesn't cover (Scoop on Windows, custom installs, etc.).\n20|   */\n21|  binOverride?: string;\n22|  /** When the task already has a generated HTML, the client sends both the\n23|   *  prior HTML and the prior content. The agent is then asked for a\n24|   *  minimal-diff edit (preserve design, only change what the content diff\n25|   *  implies). Saves output tokens AND prevents creative drift between runs. */\n26|  editFromHtml?: string;\n27|  editFromContent?: string;\n28|};\n29|\n30|function buildEditPrompt(args: {\n31|  templateName: string;\n32|  templateAspect: string;\n33|  newContent: string;\n34|  oldContent: string;\n35|  oldHtml: string;\n36|  format: string;\n37|}): string {\n38|  return `你正在执行一次**最小化差异编辑** (diff-edit), 不是从 0 重新生成。\n39|\n40|模板风格: ${args.templateName} (${args.templateAspect})\n41|输入格式: ${args.format}\n42|\n43|【硬性规则】\n44|1. 仅输出完整的、修改后的 HTML。第一个字符必须是 \\`<\\`, 最后必须是 \\`</html>\\`。\n45|2. **不要**用 markdown 围栏包裹, 不要任何解释性文字。\n46|3. **禁止使用 Write / Edit / MultiEdit / Bash 等文件工具** — HTML 必须直接在助手回复正文里流式输出, 不要存到 \\`.html\\` 文件再回复\"已输出至 …\"。\n47|4. 保留原 HTML 的 \\`<head>\\` (CDN / 字体 / 样式 / meta), 保留所有不需要变化的 DOM 结构 — 字体、配色、布局、栅格、组件结构、动画都不许改。\n48|5. 仅根据 \"旧内容 vs 新内容\" 的差异, 替换或调整对应的文字 / 数据节点。\n49|6. 如果新内容增加了条目, 沿用原有的卡片 / 行 / slide / 章节结构添加; 如果删除了条目, 移除对应的元素。\n50|7. 如果新旧内容只差几个字, 也只改那几个字 — 不要顺手 \"优化\" 或 \"重排\"。\n51|8. 不要捏造数据。新内容里没有的就不要写。\n52|\n53|【旧内容】\n54|${args.oldContent}\n55|\n56|【新内容】\n57|${args.newContent}\n58|\n59|【已有 HTML — 请基于此修改, 输出完整的修改后版本】\n60|${args.oldHtml}\n61|`;\n62|}\n63|\n64|export async function POST(req: NextRequest) {\n65|  let body: Body;\n66|  try {\n67|    body = (await req.json()) as Body;\n68|  } catch {\n69|    return new Response(\"invalid JSON body\", { status: 400 });\n70|  }\n71|  const {\n72|    agent,\n73|    templateId,\n74|    content,\n75|    format = \"text\",\n76|    model,\n77|    cwd,\n78|    binOverride,\n79|    editFromHtml,\n80|    editFromContent,\n81|  } = body;\n82|  if (!agent || !templateId || !content) {\n83|    return new Response(\"missing required fields: agent, templateId, content\", {\n84|      status: 400,\n85|    });\n86|  }\n87|  const skill = loadSkill(templateId);\n88|  if (!skill) {\n89|    return new Response(`unknown template: ${templateId}`, { status: 400 });\n90|  }\n91|\n92|  let prompt: string;\n93|  if (editFromHtml && editFromContent) {\n94|    prompt = buildEditPrompt({\n95|      templateName: skill.zhName,\n96|      templateAspect: skill.aspectHint,\n97|      newContent: content,\n98|      oldContent: editFromContent,\n99|      oldHtml: editFromHtml,\n100|      format,\n101|    });\n102|  } else {\n103|    prompt = assemblePrompt({ body: skill.body, content, format });\n104|  }\n105|  const abortCtl = new AbortController();\n106|  req.signal?.addEventListener(\"abort\", () => abortCtl.abort(), { once: true });\n107|\n108|  const stream = invokeAgent({\n109|    agent,\n110|    prompt,\n111|    model,\n112|    cwd,\n113|    binOverride,\n114|    signal: abortCtl.signal,\n115|  });\n116|\n117|  const sse = new ReadableStream({\n118|    async start(controller) {\n119|      const enc = new TextEncoder();\n120|      let outClosed = false;\n121|      const send = (event: string, data: unknown) => {\n122|        if (outClosed) return;\n123|        try {\n124|          controller.enqueue(\n125|            enc.encode(`event: ${event}\\ndata: ${JSON.stringify(data)}\\n\\n`),\n126|          );\n127|        } catch {\n128|          outClosed = true;\n129|        }\n130|      };\n131|\n132|      const reader = stream.getReader();\n133|      try {\n134|        while (true) {\n135|          const { value, done } = await reader.read();\n136|          if (done) break;\n137|          if (!value) continue;\n138|          send(value.type, value);\n139|        }\n140|      } catch (err) {\n141|        send(\"error\", {\n142|          message: err instanceof Error ? err.message : String(err),\n143|        });\n144|      } finally {\n145|        outClosed = true;\n146|        try {\n147|          controller.close();\n148|        } catch {}\n149|      }\n150|    },\n151|    cancel() {\n152|      abortCtl.abort();\n153|    },\n154|  });\n155|\n156|  return new Response(sse, {\n157|    headers: {\n158|      \"Content-Type\": \"text/event-stream; charset=utf-8\",\n159|      \"Cache-Control\": \"no-cache, no-transform\",\n160|      Connection: \"keep-alive\",\n161|      \"X-Accel-Buffering\": \"no\",\n162|    },\n163|  });\n164|}\n165|", "total_lines": 164, "file_size": 4961, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  Resultado da análise:

  - Problema que resolve
    - Usuários precisam transformar conteúdo (Markdown/CSV/Excel/JSON/SQL/notas) em HTML final envioável para humanos/público, mas editar HTML/CSS/layout continua sendo tarefa de dev/designer. HTML Anything fecha o loop com agente local: o usuário fornece conteúdo bruto e recebe um HTML de página única, pronto para ser publicado/exportado.

  - Tema principal de atuação
    - IA aplicada a conteúdo/produtividade (agentic tooling), com forte capa de design engineering e DX de export multiplataforma.

  - Razão do hype/tendência
    - movimento "HTML final para humanos" vs Markdown intermediário;
    - local-first + sem API key, reaproveitando CLIs de agentes já autenticados;
    - modelo simples de skills em pastas (75 templates);
    - export um-clique para WeChat/X/Zhihu, com CSS inline e PNG high-DPI;
    - herança/hype do ecossistema `nexu-io/open-design`.

  - Tecnologias envolvidas
    - Frontend: Next.js 16 (App Router), React 19, Tailwind v4, Zustand.
    - Backend/runtime: Node runtime + `child_process.spawn` para integração com agentes + SSE streaming.
    - Segurança/preview: `iframe[sandbox="allow-scripts allow-same-origin"]`, DOMPurify, validação de Host header no middleware.
    - Agentes suportados: Claude Code, OpenAI Codex, Cursor Agent, Gemini CLI, Copilot CLI, OpenCode, Qwen Coder, Aider.
    - Dados/formatos: `marked` + `highlight.js`, `papaparse`, `xlsx`.
    - Export: `juice`, `modern-screenshot`, `ClipboardItem`, `pptxgenjs`, `jszip`.

  Resumo da execução
  - Clonado o repositório em `/home/ice/html-anything`.
  - Li a documentação principal (`README.md`) e `package.json` em `/home/ice/html-anything` e `/home/ice/html-anything/next/package.json`.
  - Inspecionei rapidamente as rotas em `/home/ice/html-anything/next/src/app/api/convert/route.ts` para confirmar o fluxo SSE e o promptagem.
  - Nenhum arquivo foi criado ou modificado; não houve bloqueios relevantes nesta análise.  


c0:
  Vou clonar e analisar o repositório `vercel-labs/zerolang` agora.  [{"id": "chatcmpl-tool-818613613646689c", "call_id": "chatcmpl-tool-818613613646689c", "response_item_id": "fc_chatcmpl-tool-818613613646689c", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice && git clone --depth=1 https://github.com/vercel-labs/zerolang.git zerolang && echo \\\"Clone completo\\\"\", \"timeout\": 60}"}}]


c0:
    [{"id": "chatcmpl-tool-a660c409d7adc7db", "call_id": "chatcmpl-tool-a660c409d7adc7db", "response_item_id": "fc_chatcmpl-tool-a660c409d7adc7db", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice/zerolang && ls -la && echo \\\"---\\\" && cat README.md 2>/dev/null | head -150\", \"timeout\": 15}"}}, {"id": "chatcmpl-tool-a19e0fa1dc7f29e1", "call_id": "chatcmpl-tool-a19e0fa1dc7f29e1", "response_item_id": "fc_chatcmpl-tool-a19e0fa1dc7f29e1", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\": \"/home/ice/zerolang\", \"pattern\": \"*.md\", \"target\": \"files\"}"}}]


c0:
  {"output": "total 22\ndrwxrwxr-x 17 ice ice     27 May 30 22:52 .\ndrwx------ 18 ice ice     23 May 30 22:52 ..\ndrwxrwxr-x  8 ice ice     14 May 30 22:52 .git\ndrwxrwxr-x  3 ice ice      3 May 30 22:52 .github\n-rw-rw-r--  1 ice ice    118 May 30 22:52 .gitignore\n-rw-rw-r--  1 ice ice      3 May 30 22:52 .node-version\n-rw-rw-r--  1 ice ice   3802 May 30 22:52 AGENTS.md\n-rw-rw-r--  1 ice ice   6316 May 30 22:52 CHANGELOG.md\n-rw-rw-r--  1 ice ice  11284 May 30 22:52 LICENSE\n-rw-rw-r--  1 ice ice   8889 May 30 22:52 README.md\ndrwxrwxr-x  4 ice ice      4 May 30 22:52 benchmarks\ndrwxrwxr-x  2 ice ice      3 May 30 22:52 bin\ndrwxrwxr-x 15 ice ice     17 May 30 22:52 conformance\ndrwxrwxr-x  8 ice ice     13 May 30 22:52 docs\ndrwxrwxr-x  3 ice ice      6 May 30 22:52 evals\ndrwxrwxr-x 14 ice ice     83 May 30 22:52 examples\ndrwxrwxr-x  3 ice ice      3 May 30 22:52 extensions\ndrwxrwxr-x  3 ice ice      3 May 30 22:52 native\n-rw-rw-r--  1 ice ice   4838 May 30 22:52 package.json\n-rw-rw-r--  1 ice ice 241394 May 30 22:52 pnpm-lock.yaml\n-rw-rw-r--  1 ice ice    127 May 30 22:52 pnpm-workspace.yaml\ndrwxrwxr-x  2 ice ice     27 May 30 22:52 scripts\ndrwxrwxr-x  2 ice ice     10 May 30 22:52 skill-data\ndrwxrwxr-x  3 ice ice      3 May 30 22:52 skills\ndrwxrwxr-x  2 ice ice      5 May 30 22:52 std\ndrwxrwxr-x  2 ice ice      4 May 30 22:52 tests\n-rw-rw-r--  1 ice ice   1014 May 30 22:52 turbo.json\n---\n# zerolang\n\nzerolang is an experimental graph-first programming language where agents work with semantic program structure instead of raw source text.\n\nSource code is still the source of truth. The graph is the compiler-derived interface agents use to inspect and change programs with less guessing.\n\nThe current model:\n\n- Human-readable `.0` source stays reviewable, auditable, and durable.\n- The compiler derives a checked ProgramGraph from source.\n- Agents inspect graph facts such as node IDs, graph hashes, types, effects, ownership facts, capabilities, helper use, and module edges.\n- Agents can submit checked graph edits instead of only patching text ranges.\n- Where source rewriting is supported, the compiler validates the edit before writing source.\n\nDesign goals:\n\n- Token efficiency\n- Low memory usage\n- Fast startup\n- Fast builds\n- Low runtime latency\n- Zero dependencies\n\n> **Safety status**\n>\n> Security vulnerabilities should be expected. zerolang is not ready for production systems, sensitive data, or trusted infrastructure. Run and develop it in isolated, disposable environments.\n\n## Why Graph\n\nAgents can edit source text, but source text is a lossy interface for program understanding. A text patch has to guess which references are related, whether a range is stale, whether a call resolves to the intended function, and whether an edit preserved ownership, fallibility, effects, imports, and target constraints.\n\nThe ProgramGraph is zerolang's compiler-owned structure for that work. It is meant to give agents a map they can navigate in slices: start from a symbol, diagnostic, call, capability, module, or node ID, then ask for the surrounding semantic facts instead of loading unrelated source. That keeps context gathering focused while leaving source code as the durable artifact humans review.\n\nThe edit loop is also different. A graph edit can target `node #610c78bf` instead of a line range, require the inspected `graphHash`, require an expected field value, and let the compiler validate, lower, write, format, reparse, and check the result as one path. Refactors can be expressed as semantic operations such as renaming a function node or replacing a resolved callee, rather than search-and-replace over text followed by separate cleanup commands.\n\n## Source Text\n\n`.0` source is intentionally regular. The goal is source that behaves like durable data: easy to index, compare, format, audit, and regenerate, while still reading like normal code.\n\nA small program shows typed signatures, infix expressions, fallibility, and explicit capability passing:\n\n```zero\nfn answer() -> i32 {\n    return 40 + 2\n}\n\npub fn main(world: World) -> Void raises {\n    if answer() == 42 {\n        check world.out.write(\"math works\\n\")\n    }\n}\n```\n\nSource code remains the stored representation. ProgramGraph artifacts are derived inspection and interchange data, not the primary project files.\n\n## ProgramGraph\n\nAgents do not have to infer every fact from text. The compiler can expose the checked structure of a program directly:\n\n```bash\nzero graph dump examples/hello.0\n```\n\nExample output:\n\n```text\nzero-graph v1\norigin source-text\nmodule \"hello\"\nhash \"graph:b8a019041020df03\"\n\nnode #ea5ea1ca Function name:\"main\" type:\"Void\" public:true fallible:true\nnode #f9ce8b3e Param name:\"world\" type:\"World\"\nnode #421a4d4b MethodCall name:\"write\" type:\"Void\"\nnode #610c78bf Literal type:\"String\" value:\"hello from zero\\n\"\nedge #421a4d4b arg #610c78bf order:0\nedge #ea5ea1ca body #6c48dda8\n```\n\nThe graph gives agents explicit handles such as node IDs, graph hashes, resolved types, effects, ownership facts, capability facts, helper use, and module edges. The hash is a stale-context check; node IDs are edit targets for the graph that was inspected.\n\n## Checked Graph Edits\n\nFor supported canonical `.0` source, `zero graph patch` applies checked edits to the graph and rewrites source only after validation. The command is intended to collapse the normal agent loop of edit, format, reparse, check, and fix into a compiler-mediated operation:\n\n```bash\nzero graph patch examples/hello.0 \\\n  --expect-graph-hash graph:b8a019041020df03 \\\n  --op 'set node=\"#610c78bf\" field=\"value\" expect=\"hello from zero\\n\" value=\"hello graph\\n\"'\n```\n\nThis is different from a source-text patch. The operation targets a checked semantic node and field. The graph hash rejects stale context, and `expect` rejects the edit if the current field value is different from what the agent inspected.\n\n## Agent Workflow Interfaces\n\nThe compiler exposes the workflow through CLI commands with stable structured output.\n\n### Load Version-Matched Rules\n\nThe compiler ships skill text that matches the binary being used:\n\n```bash\nzero skills list\nzero skills get language\nzero skills get diagnostics\nzero skills get stdlib\n```\n\nPrint the language guide bundled with the compiler:\n\n```bash\nzero skills get language\n```\n\n### Inspect Compiler Facts\n\nCompiler state is exposed through structured command output instead of prose-only output. The important contract is the stable fields and repair identifiers; today the CLI exposes those fields with `--json`:\n\n```bash\nzero tokens --json examples/hello.0\nzero parse --json examples/hello.0\nzero check --json examples/hello.0\nzero graph dump examples/hello.0\nzero graph --json examples/systems-package\nzero size --json examples/point.0\n```\n\nThe structured contracts include diagnostic codes and spans, public symbols, import edges, target readiness, compile-time sandbox facts, retained helpers, graph hashes, node IDs, and size retention reasons.\n\n### Compiler-Native Contracts\n\nMost language ecosystems expose some of these facts through separate tools, editor protocols, or library APIs. zerolang keeps the agent-facing inspection and repair path in the compiler CLI.\n\nThe inspection and repair surfaces are compiler commands, not editor-only features or a separate analysis service:\n\n| Command | Contract |\n| --- | --- |\n| `zero skills get language` | Version-matched language rules bundled with the compiler binary. |\n| `zero check --json` | Diagnostics with code, span, expected/actual fields, fix safety, repair metadata, compile-time sandbox facts, target readiness, and safety facts. |\n| `zero parse --json` | A stable parse summary with declarations, function signatures, and body node kinds. |\n| `zero graph --json` | Modules, imports, public symbols, capabilities, effects, ownership facts, safety facts, helper use, and interface fingerprints. |\n| `zero graph dump` | Deterministic ProgramGraph text with graph hashes, node IDs, nodes, and edges. |\n| `zero graph patch` | Checked graph edits with graph-hash and field-value preconditions. |\n| `zero fix --plan --json` | Typed repair plans that describe proposed fixes without editing files. |\n| `zero size --json` | Retained helpers, size reasons, profile policy, safety facts, backend facts, and artifact budget data. |\n\n### Repair With Diagnostics\n\nA failing fixture reports a diagnostic with stable fields:", "exit_code": 0, "error": null}

  [Subdirectory context discovered: zerolang/AGENTS.md]
  # Contributor Notes

  Zero is a pre-1 experiment in building an agent-first programming language.
  Keep public-facing changes honest about what works today without weakening that
  positioning.

  ## Project Direction

  Zero is still being shaped around the needs of agents. Breaking changes are
  acceptable when they move the language, standard library, compiler, or tooling
  closer to that goal.

  Do not preserve legacy behavior by default. Prefer the clearer agent-facing
  design over compatibility shims, migration layers, or carrying old paths
  forward. Keep examples, docs, tests, and command contracts aligned with the new
  behavior so the repository describes one coherent current system.

  This does not mean broad churn for its own sake. Make direct changes that
  advance Zero's agent-first goals: on-the-fly learnability, deterministic
  inspection and repair, strong standard-library coverage, exceptional developer
  experience, and regular patterns over syntactic convenience.

  ## Safety Expectations

  Security vulnerabilities should be expected. Zero is not ready for production
  systems, sensitive data, or trusted infrastructure.

  Run and develop Zero in safe environments: isolated workspaces, disposable
  inputs, and systems where compiler crashes, malformed output, or unsafe runtime
  behavior cannot damage production state. Treat generated artifacts and examples
  as experimental unless they have been reviewed for the specific environment
  where they will run.

  ## Development

  - Build the local compiler with `make -C native/zero-c`.
  - Use `bin/zero` for focused checks; it execs the local native compiler at
    `.zero/bin/zero`.
  - Keep examples runnable and docs copyable.
  - Prefer small, direct changes over broad refactors.
  - Use direct emitters for compiler output.

  ## Useful Checks

  ```sh
  pnpm run docs:test
  pnpm run conformance
  pnpm run native:test
  pnpm run command-contracts
  ```

  For focused compiler work:

  ```sh
  bin/zero check --json <file-or-package>
  bin/zero graph --json <file-or-package>
  bin/zero size --json <file-or-package>
  bin/zero explain <diagnostic-code>
  bin/zero fix --plan --json <file-or-package>
  ```

  ## Project Layout

  - `native/zero-c/`: native compiler implementation.
  - `examples/`: small runnable programs and packages.
  - `conformance/`: language and CLI fixtures.
  - `docs/`: public documentation site.
  - `scripts/`: validation and release support tooling.

  ## Public Docs Policy

  Docs should describe current user-facing behavior, not internal development
  history. Avoid release-planning language, validation-report narratives, and
  implementation diary details in pages intended for external readers.

  ## Releasing

  Releases are manual, single-branch affairs. The maintainer controls the
  changelog voice and format.

  To prepare a release:

  1. Create a release branch, such as `ctate/v0.1.1`.
  2. Bump the release version in `package.json`, `docs/package.json`,
     `extensions/vscode/package.json`, and `native/zero-c/src/main.c`.
  3. Update command-contract expectations that assert the compiler version.
  4. Write the `CHANGELOG.md` entry for the new version, wrapped in
     `<!-- release:start -->` and `<!-- release:end -->` markers.
  5. Remove the release markers from the previous release entry. Only the latest
     release entry should have markers.
  6. Include a `### Contributors` section in the marked changelog entry. Derive
     contributors from commit authors and `Co-authored-by` trailers since the
     previous tag.
  7. Run focused checks before opening or updating the PR:

  ```sh
  make -C native/zero-c
  bin/zero --version --json
  pnpm run test:zero
  pnpm run command-contracts:local
  pnpm run docs:test
  ```

  The release workflow reads the version from `package.json`, builds release
  assets, and uses the content between the changelog markers as the GitHub
  release body. terminal 


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--disable-warning=ExperimentalWarning scripts/mir-verifier-smoke.mts && node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/program-graph-smoke.mts && node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/canonical-text-smoke.mts && node conformance/run.mjs'\",\n18|    \"provenance:guardrails\": \"node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/provenance-guardrails.mts\",\n19|    \"type-core:test\": \"node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/type-core-smoke.mts\",\n20|    \"mir-verifier:test\": \"node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/mir-verifier-smoke.mts\",\n21|    \"program-graph:test\": \"node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/program-graph-smoke.mts\",\n22|    \"canonical-text:test\": \"node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/canonical-text-smoke.mts\",\n23|    \"conformance:sandbox\": \"node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/native-test-sandbox.mts -- pnpm run conformance:local\",\n24|    \"command-contracts\": \"pnpm run command-contracts:sandbox\",\n25|    \"command-contracts:local\": \"ZERO_NATIVE_TEST_ALLOW_LOCAL=1 node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/snapshot-command-contracts.mts\",\n26|    \"command-contracts:sandbox\": \"node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/native-test-sandbox.mts -- pnpm run command-contracts:local\",\n27|    \"docs:install\": \"pnpm install\",\n28|    \"docs:dev\": \"turbo run dev --filter=./docs\",\n29|    \"docs:build\": \"turbo run build --filter=./docs\",\n30|    \"docs:start\": \"turbo run start --filter=./docs\",\n31|    \"docs:test\": \"turbo run test --filter=./docs\",\n32|    \"extension:install:cursor\": \"pnpm --filter=./extensions/vscode run install:cursor\",\n33|    \"extension:test\": \"turbo run test --filter=./extensions/vscode\",\n34|    \"native:install\": \"make -C native/zero-c install-local\",\n35|    \"native:smoke\": \"node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/native-smoke.mts\",\n36|    \"native:sanitize\": \"bash scripts/sanitizer-smoke.sh\",\n37|    \"rosetta:local\": \"make -C native/zero-c && node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/rosetta.mts\",\n38|    \"compiler:metrics\": \"node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/compiler-metrics.mts\",\n39|    \"stdlib:contracts\": \"node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/stdlib-contracts.mts\",\n40|    \"reliability:smoke\": \"node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/reliability-smoke.mts\",\n41|    \"native:test\": \"pnpm run native:test:sandbox\",\n42|    \"native:test:local\": \"ZERO_NATIVE_TEST_ALLOW_LOCAL=1 bash scripts/test-native.sh\",\n43|    \"native:test:sandbox\": \"node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/native-test-sandbox.mts\",\n44|    \"bench\": \"bash scripts/bench.sh\",\n45|    \"bench:local\": \"ZERO_BENCH_MODE=local bash scripts/bench.sh\",\n46|    \"agent:demo\": \"node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/agent-repair-demo.mts\",\n47|    \"evals\": \"node scripts/run-evals.mjs\",\n48|    \"evals:build\": \"turbo run build --filter=./evals\",\n49|    \"evals:test\": \"turbo run test --filter=./evals\",\n50|    \"zls\": \"node --experimental-strip-types --disable-warning=ExperimentalWarning scripts/zls.mts\",\n51|    \"test:zero\": \"make -C native/zero-c && pnpm --silent run build:scripts && pnpm --silent run build:test && node --test .zero/test-js/*.test.js\",\n52|    \"test\": \"pnpm run conformance && pnpm run native:test && pnpm run test:zero && pnpm run docs:test && pnpm run zls -- --self-test && pnpm run agent:demo && pnpm run extension:test\",\n53|    \"test:packages\": \"turbo run test\",\n54|    \"zero\": \"bin/zero\"\n55|  },\n56|  \"devDependencies\": {\n57|    \"@types/node\": \"^25.9.0\",\n58|    \"@vercel/sandbox\": \"^1.10.2\",\n59|    \"turbo\": \"^2.9.14\",\n60|    \"typescript\": \"^6.0.3\"\n61|  }\n62|}\n63|", "total_lines": 62, "file_size": 4838, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|# Changelog\n2|\n3|## 0.2.0\n4|\n5|<!-- release:start -->\n6|\n7|- Makes canonical `.0` text the native source surface, with a parser, formatter, Program import path, diagnostics, docs, examples, stdlib sources, benchmarks, conformance fixtures, and command snapshots aligned around that format.\n8|- Promotes ProgramGraph from inspection output into an editable artifact workflow with deterministic dump, validate, view, check, roundtrip, import, patch, build, run, test, size, and package entrypoint support.\n9|- Adds source-backed graph edits so checked graph patches can rewrite canonical `.0` files while preserving package/import context, std helper context, metadata, and stable graph identities.\n10|- Expands direct backend coverage with darwin-x64 Mach-O executable support, shared AArch64 ELF/COFF emission, target-readiness reporting, byte-view ABI fixes, and stronger cross-target buildability checks.\n11|- Adds source-backed `std.path`, `std.str`, and `std.math` modules with docs, examples, Rosetta coverage, and direct target validation.\n12|- Exposes resolved call-resolution and stdlib helper facts in graph JSON, tightening generic return typing, member call facts, std helper validation, and package cache metadata.\n13|- Grows Rosetta and compiler smoke coverage, graph command contracts, metrics budgets, CLI docs, skills, and docs tests around the current agent edit loop.\n14|\n15|### Contributors\n16|\n17|- @ctate\n18|\n19|<!-- release:end -->\n20|\n21|## 0.1.4\n22|\n23|- Adopts canonical `.0` text as the current Zero source surface across parsing, import resolution, package manifests, command workflows, docs, fixtures, formatting, and artifact contracts.\n24|- Rebuilds checker internals around TypeCore, binder-aware unification, generic inference, static interface validation, provenance substitution, and shared call-resolution facts.\n25|- Hardens direct emitters and native artifacts through MIR verifier contracts, direct specialization metadata, target buildability checks, and shared x64, AArch64, ELF, Mach-O, and COFF emission helpers.\n26|- Adds clearer direct backend selection, target readiness, and buildability reporting so command JSON and failed native builds expose deterministic blocker facts.\n27|- Removes stale wasm, compiler-zero, and superseded parser surfaces while refreshing public docs, README guidance, and zerolang branding around the current system.\n28|- Strengthens compiler structure guardrails, metrics budgets, TypeScript-based repo scripts, docs build infrastructure, CI workspace commands, and eval harness isolation.\n29|- Renames bundled version-matched skills to the current flat skill names while keeping `zero skills` discovery focused on the native compiler.\n30|\n31|### Contributors\n32|\n33|- @ctate\n34|\n35|## 0.1.3\n36|\n37|- Adds hosted HTTP client runtime support, including runtime packaging fixes, JSON byte helpers for wasm, HTTP final-header parsing fixes, and direct array span lowering repairs.\n38|- Parses `use` declarations into syntax graph facts and reports import diagnostics, fix plans, and graph edges against the specific import source ranges.\n39|- Adds structured backend blocker and target-readiness facts to command JSON so checks, build previews, and direct backend failures explain unsupported targets more deterministically.\n40|- Expands agent-facing diagnostics with BOR001 borrow trace facts, target-neutral CGEN004 wording, and NAM004 generic type-name shadowing rejection.\n41|- Hardens native compiler allocation paths across shared buffers, parser, checker, IR lowering, emitters, source handling, targets, and driver state for deterministic allocation failures.\n42|- Updates the public site and docs with current guidance, docs chat rate limiting, and pnpm-based repository workflows.\n43|- Restores native versioned skill guidance, keeps the repo wrapper on the native compiler, and removes the legacy `zero skills path` command.\n44|\n45|### Contributors\n46|\n47|- @ctate\n48|- @PeterXMR\n49|- @h4ckf0r0day\n50|\n51|## 0.1.2\n52|\n53|- Rebuilds borrow provenance tracking across references, fields, subpaths, assignments, control-flow joins, receiver side effects, generic methods, return summaries, and unreachable paths.\n54|- Tightens borrow conflict checking by deriving conflicts from provenance and comparing concrete places, with additional conformance coverage for provenance joins and edge cases.\n55|- Fixes checker regressions around borrow origins, reassignment, aggregate values, explicit reference fields, unknown identifiers, and fallibility propagation through wrappers.\n56|- Fixes dynamic CLI strings and Darwin executable UUID emission.\n57|- Adds Apache-2.0 licensing and lockfile license metadata.\n58|- Updates the public site with an install toggle, GitHub star count, and a mobile header Docs link.\n59|\n60|### Contributors\n61|\n62|- @ctate\n63|- @badlogic\n64|- @chenrui333\n65|- @heylakatos\n66|- @onevcat\n67|\n68|## 0.1.1\n69|\n70|- Adds the public installer at `https://zerolang.ai/install.sh`, with platform selection, GitHub release downloads, checksum verification, and `$HOME/.zero/bin/zero` installation.\n71|- Adds `zero run` for the everyday edit loop: build a host executable, run it, pass program arguments after `--`, forward stdout/stderr, and return the program exit status.\n72|- Updates README, homepage, getting started, install, and CLI docs around the curl install path, copyable commands, and `zero run`.\n73|- Reworks public docs to be more scannable and current, including stronger language, diagnostics, testing, target, package, optimization, and standard library references.\n74|- Removes placeholder module docs that described surfaces not ready for users and adds current module docs for `std.crypto`, `std.http`, and `std.net`.\n75|- Adds version-matched agent guidance through `zero skills`, including focused workflows for Zero syntax, diagnostics, builds, packages, standard library use, testing, and agent edit loops.\n76|- Keeps the installable Zero skill as a thin bootstrap so external skill managers discover one Zero skill while the compiler serves the richer guidance for the installed version.\n77|- Updates the `zero skills` CLI contract to serve bundled flat skill data while preserving list, get, path, and JSON workflows.\n78|\n79|### Contributors\n80|\n81|", "total_lines": 88, "file_size": 6316, "truncated": true, "hint": "Use offset=81 to continue reading (showing 1-80 of 88 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  Análise concluída do repositório [`vercel-labs/zerolang`](https://github.com/vercel-labs/zerolang). Resumo final:

  ---

  ## 🔍 Problema que resolve

  **Limitações da edição semântica por texto pura quando agentes de IA modificam código.**

  Editar código tradicionalmente por diff/patch de texto é um processo *inseguro* para agentes:
  - referências que parecem relacionadas podem não ser;
  - intervalos de linha ficam obsoletos com facilidade;
  - edições não garantem preservação de ownership, efeitos, imports, tipos ou constraints de capacidade.

  **Solução proposta:** representar semanticamente a estrutura do programa como um grafo derivado pelo compilador (`ProgramGraph`), permitindo que agentes editem **nós e arestas com IDs estáveis**, ao invés de ranges de texto. O source continua sendo a fonte da verdade, mas o grafo é a interface entre agente e compilador.

  ---

  ## 🎯 Tema principal de atuação

  **IA (Inteligência Artificial)** — com foco em **Engenharia de Agentes / AI Software Engineering**.  
  É classificada como uma linguagem "agent-first", criada para ser entendida e mutável por modelos de linguagem de forma determinística, não para humanos em primeiro lugar.

  ---

  ## 🔥 Razão do hype / tendência

  - **Primeira linguagem pensada primariamente para agentes:** não é uma linguagem convencional com “novidades para devs”; ela inverte o problema: “como seres humanos + agentes leem e escrevem código?”
  - **Grafo semântico editável:** `zero graph dump` e `zero graph patch` resolvem diretamente o loop de *edit → formata → reparse → valida → corrige* que hoje gera muitos erros em agentes codificadores.
  - **Performance extrema:** meta declarada de zero dependências, baixo consumo de memória, startup rápido e build rápido.
  - **Vercel Labs por trás:** forte branding e acesso a ecossistema de ferramentas da Vercel.
  - **4.7k+ estrelas** em pouco tempo, refletindo interesse crescente em runtimes e linguagens otimizadas para IA.

  ---

  ## 🛠️ Tecnologias envolvidas

  | Camada | Tecnologias / Conceitos |
  |---|---|
  | **Compilador nativo** | C, MIR (Mid-level IR), verificador de tipos TypeCore, direct emitters |
  | **Backend emissor** | x64, AArch64, ELF, Mach-O, COFF |
  | **CLI / Tooling** | Node.js, TypeScript, pnpm workspace, Turbo |
  | **Representação do programa** | ProgramGraph, nós hasheados, arestas semânticas |
  | **Safety** | Verificador de tipos, rastreamento de borrow/provenance, sandbox compile-time |
  | **Ecossistema** | Extensões VS Code, cursores, benchmarks conformance |
  | **SKILL bundles** | `zero skills` (language, diagnostics, stdlib, packages, testing, graph) instaláveis junto ao compilador |
  | **Integração com agentes** | JSON schemas em todos os subcomandos (`check --json`, `graph --json`, `fix --plan --json`) para consumo por LLMs/agents |

  ---

  ### Observações adicionais úteis
  - A extensão de arquivo é `.0` (leia-se "zero"), não relacionado ao número.
  - V0.2.0 é a versão atual analisada e já suporta grafo editável com *source-backed* rewrite.
  - Projeto está **Apache-2.0** e os mantenedores enfatizam que ainda tem bugs de segurança; recomenda-se uso em ambientes isolados.  


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c0:
  {"content": "1|# bumblebee\n2|\n3|Bumblebee is a read-only inventory collector for package, extension,\n4|and developer-tool metadata on macOS and Linux developer endpoints.\n5|\n6|It answers a narrow supply-chain response question: when an advisory\n7|names a package, extension, or version, which developer machines show\n8|a match in their on-disk metadata right now?\n9|\n10|SBOMs help answer what shipped, and EDR helps answer what ran or\n11|touched the network, but supply-chain response often needs a different\n12|view: messy local state across lockfiles, package-manager metadata,\n13|extension manifests, and supported developer-tool configs.\n14|\n15|Bumblebee turns that scattered on-disk state into structured NDJSON\n16|component records and, when given an exposure catalog, flags exact\n17|matches for fast, read-only exposure checks when responders already\n18|know what they are looking for.\n19|\n20|## Scope\n21|\n22|- Single static binary, Go 1.25+, zero non-stdlib dependencies.\n23|- Three scan profiles (`baseline`, `project`, `deep`) for different\n24|  populations and cadences.\n25|- Reads only the lockfiles, package-manager install metadata,\n26|  extension manifests, and supported MCP JSON configs listed in\n27|  [docs/inventory-sources.md](docs/inventory-sources.md). No package\n28|  manager execution (`npm ls`, `pip show`, `go list`, ...) and no\n29|  source-file reads. MCP host configs can carry environment values\n30|  and credentials in their `env` blocks; Bumblebee parses these\n31|  configs for the server inventory it needs but does not emit those\n32|  values in its records.\n33|\n34|## Coverage\n35|\n36|| Family | Emitted `ecosystem` | Sources |\n37||---|---|---|\n38|| npm | `npm` | `package-lock.json`, `npm-shrinkwrap.json`, `node_modules/.package-lock.json`, `node_modules/<pkg>/package.json` |\n39|| pnpm | `npm` | `pnpm-lock.yaml`, `.pnpm/.../package.json` |\n40|| Yarn | `npm` | `yarn.lock` (Classic + Berry) |\n41|| Bun | `npm` | `bun.lock`; `bun.lockb` presence as diagnostic |\n42|| PyPI | `pypi` | `*.dist-info/METADATA`, `INSTALLER`, `direct_url.json`, `*.egg-info/PKG-INFO` |\n43|| Go modules | `go` | `go.sum`, `go.mod` |\n44|| RubyGems | `rubygems` | `Gemfile.lock`, installed `*.gemspec` |\n45|| Composer | `packagist` | `composer.lock`, `vendor/composer/installed.json` |\n46|| MCP | `mcp` | JSON host configs: `mcp.json`, `.mcp.json`, `claude_desktop_config.json`, `mcp_config.json`, `mcp_settings.json`, `cline_mcp_settings.json`, plus `~/.gemini/settings.json` (Gemini CLI / Code Assist) and `~/.claude.json` (Claude Code user- and project-scoped `mcpServers`). Non-JSON configs (Codex `config.toml`, Continue YAML) are not parsed in v0.1. |\n47|| Editor extensions | `editor-extension` | VS Code, Cursor, Windsurf, VSCodium manifests |\n48|| Browser extensions | `browser-extension` | Chromium-family (`manifest.json`) and Firefox (`extensions.json`) per profile |\n49|| Homebrew | `homebrew` | Formula `INSTALL_RECEIPT.json` files and cask `.metadata` install markers |\n50|\n51|Per-ecosystem detail: [docs/inventory-sources.md](docs/inventory-sources.md).\n52|\n53|## Install\n54|\n55|Requires Go 1.25+. Zero non-stdlib dependencies.\n56|\n57|```sh\n58|# Install the latest tagged release into $GOBIN.\n59|go install github.com/perplexityai/bumblebee/cmd/bumblebee@latest\n60|\n61|# Or pin a specific tag.\n62|go install github.com/perplexityai/bumblebee/cmd/bumblebee@v0.1.1\n63|```\n64|\n65|To build from a checkout:\n66|\n67|```sh\n68|go build -o bumblebee ./cmd/bumblebee\n69|go test ./...\n70|```\n71|\n72|Stamp an explicit version at build time:\n73|\n74|```sh\n75|go build -ldflags \"-X main.Version=v0.1.1\" -o bumblebee ./cmd/bumblebee\n76|```\n77|\n78|`bumblebee version` prints the version plus the VCS revision, build\n79|time, and Go runtime — so a record emitted in production can be traced\n80|back to a specific build. Version precedence: `-ldflags` override,\n81|module version recorded by `go install`, then the in-tree default\n82|tracked in `VERSION`.\n83|\n84|### Self-test\n85|\n86|After installing, run a built-in end-to-end check against embedded\n87|fixtures:\n88|\n89|```sh\n90|bumblebee selftest\n91|# selftest OK (2 findings in 1ms)\n92|```\n93|\n94|The fixtures live inside the binary, use deliberately fake package\n95|names (`bumblebee-selftest-evil@0.0.0`), and make no network calls. A\n96|non-zero exit means the local install can no longer detect what it\n97|should — a fast pre-deployment smoke test for fleet rollouts.\n98|\n99|## Profiles\n100|\n101|Bumblebee is a one-shot scanner: each invocation performs a single scan\n102|and exits. Cadence is the runner's responsibility (cron, launchd, systemd,\n103|MDM, etc.). Each record carries `profile` and a per-root `root_kind` so\n104|receivers can keep populations separate.\n105|\n106|| Profile | Scans | Use for |\n107||---|---|---|\n108|| `baseline` | Common global/user package roots, language toolchains, editor extensions, browser extensions, and MCP configs. | Recurring lightweight inventory via an external runner. |\n109|| `project` | Configured development directories, such as `~/code`, `~/src`, or `~/work`. | Recurring inventory for known project workspaces. |\n110|| `deep` | Explicit `--root` paths, including broad roots like `$HOME`. | On-demand incident or campaign checks, usually with `--ecosystem`, `--exposure-catalog`, and `--findings-only`. |\n111|\n112|`baseline` and `project` refuse bare-home roots; only `deep` walks them.\n113|\n114|## Quick start\n115|\n116|```sh\n117|# Baseline global inventory.\n118|bumblebee scan --profile baseline > inventory.ndjson\n119|\n120|# Daily project sweep with explicit roots.\n121|bumblebee scan --profile project \\\n122|  --root \"$HOME/code\" \\\n123|  --root \"$HOME/Developer\"\n124|\n125|# Limit a run to selected emitted ecosystems.\n126|bumblebee scan --profile baseline \\\n127|  --ecosystem npm,pypi \\\n128|  --ecosystem go\n129|\n130|# On-demand exposure scan against a published advisory.\n131|bumblebee scan --profile deep \\\n132|  --root \"$HOME\" \\\n133|  --exposure-catalog ./catalog.json \\\n134|  --max-duration 10m\n135|```\n136|\n137|Preview the resolved roots without scanning:\n138|\n139|```sh\n140|bumblebee roots --profile baseline\n141|# prints \"<root_kind>\\t<path>\" lines\n142|```\n143|\n144|`--root` is a filesystem path to scan; repeatable, required for `deep`,\n145|optional for the other profiles. `--ecosystem` is repeatable and\n146|comma-separated. `--exposure-catalog` accepts a JSON file or a directory\n147|of `*.json` catalogs (merged non-recursively, all files must share\n148|`schema_version`). `--findings-only` requires `--exposure-catalog` and\n149|suppresses package records while keeping findings. `bumblebee scan --help`\n150|lists every flag.\n151|\n152|## Output\n153|\n154|Records are NDJSON, one per line. Diagnostics go to stderr as NDJSON. Each\n155|run ends with a `scan_summary` record; receivers use it to decide whether\n156|to promote a run to current state. See [docs/transport.md](docs/transport.md)\n157|for HTTPS/file output and [docs/state-model.md](docs/state-model.md) for the\n158|receiver-side current-state model.\n159|\n160|Package record:\n161|\n162|<details>\n163|<summary>Example package record</summary>\n164|\n165|```json\n166|{\n167|  \"record_type\": \"package\",\n168|  \"record_id\": \"package:...\",\n169|  \"schema_version\": \"0.1.0\",\n170|  \"scanner_name\": \"bumblebee\",\n171|  \"scanner_version\": \"v0.1.1\",\n172|  \"run_id\": \"9b1f0c2e4d5a6b7c8d9e0f1a2b3c4d5e\",\n173|  \"scan_time\": \"2026-05-15T18:22:01.482Z\",\n174|  \"endpoint\": {\n175|    \"hostname\": \"alex-mbp\",\n176|    \"os\": \"darwin\",\n177|    \"arch\": \"arm64\",\n178|    \"username\": \"alex\",\n179|    \"uid\": \"501\",\n180|    \"device_id\": \"MDM-7F4A2B\"\n181|  },\n182|  \"profile\": \"project\",\n183|  \"ecosystem\": \"npm\",\n184|  \"package_name\": \"@tanstack/query-core\",\n185|  \"normalized_name\": \"@tanstack/query-core\",\n186|  \"version\": \"5.59.20\",\n187|  \"project_path\": \"/Users/alex/code/web-app\",\n188|  \"root_kind\": \"project_root\",\n189|  \"package_manager\": \"pnpm\",\n190|  \"source_type\": \"pnpm-lockfile\",\n191|  \"source_file\": \"/Users/alex/code/web-app/pnpm-lock.yaml\",\n192|  \"has_lifecycle_scripts\": false,\n193|  \"confidence\": \"high\"\n194|}\n195|```\n196|\n197|</details>\n198|\n199|`confidence`:\n200|\n201|", "total_lines": 289, "file_size": 10120, "truncated": true, "hint": "Use offset=201 to continue reading (showing 1-200 of 289 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|module github.com/perplexityai/bumblebee\n2|\n3|go 1.25\n4|", "total_lines": 3, "file_size": 50, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"output": "internal/ecosystem/browserext/browserext.go\ninternal/ecosystem/browserext/browserext_test.go\ninternal/ecosystem/bun/bun.go\ninternal/ecosystem/bun/bun_test.go\ninternal/ecosystem/composer/composer.go\ninternal/ecosystem/composer/composer_test.go\ninternal/ecosystem/editorext/editorext.go\ninternal/ecosystem/editorext/editorext_test.go\ninternal/ecosystem/gomod/gomod.go\ninternal/ecosystem/gomod/gomod_test.go\ninternal/ecosystem/homebrew/homebrew.go\ninternal/ecosystem/homebrew/homebrew_test.go\ninternal/ecosystem/mcp/mcp.go\ninternal/ecosystem/mcp/mcp_test.go\ninternal/ecosystem/npm/npm.go\ninternal/ecosystem/npm/npm_test.go\ninternal/ecosystem/pnpm/pnpm.go\ninternal/ecosystem/pnpm/pnpm_test.go\ninternal/ecosystem/pypi/pypi.go\ninternal/ecosystem/pypi/pypi_test.go\ninternal/ecosystem/rubygems/rubygems.go\ninternal/ecosystem/rubygems/rubygems_test.go\ninternal/ecosystem/yarn/yarn.go\ninternal/ecosystem/yarn/yarn_test.go\ninternal/endpoint/endpoint.go\ninternal/endpoint/endpoint_test.go\ninternal/exposure/exposure.go\ninternal/exposure/exposure_test.go\ninternal/model/model.go\ninternal/model/model_test.go\ninternal/normalize/normalize.go\ninternal/normalize/normalize_test.go\ninternal/output/httpsink.go\ninternal/output/httpsink_test.go\ninternal/output/output.go\ninternal/output/output_test.go\ninternal/scanner/findings_test.go\ninternal/scanner/scanner.go\ninternal/scanner/scanner_integration_test.go\ninternal/scanner/scanner_test.go\ninternal/walk/dirkey_other.go\ninternal/walk/dirkey_unix.go\ninternal/walk/walk.go\ninternal/walk/walk_test.go", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-9183fd96138fcc06", "call_id": "chatcmpl-tool-9183fd96138fcc06", "response_item_id": "fc_chatcmpl-tool-9183fd96138fcc06", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/bumblebee/internal/endpoint/endpoint.go\"}"}}, {"id": "chatcmpl-tool-94495ecdf88de378", "call_id": "chatcmpl-tool-94495ecdf88de378", "response_item_id": "fc_chatcmpl-tool-94495ecdf88de378", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/bumblebee/internal/scanner/scanner.go\"}"}}]


c0:
  {"content": "1|// Package endpoint collects host identity used in every record.\n2|package endpoint\n3|\n4|import (\n5|\t\"os\"\n6|\t\"os/user\"\n7|\t\"runtime\"\n8|\t\"strconv\"\n9|\n10|\t\"github.com/perplexityai/bumblebee/internal/model\"\n11|)\n12|\n13|// Current returns the host identity used in every emitted record.\n14|//\n15|// deviceID, when non-empty, is set on Endpoint.DeviceID verbatim.\n16|// Callers are expected to have already trimmed it and decided how to\n17|// handle empty / whitespace input from the configured env var.\n18|func Current(deviceID string) model.Endpoint {\n19|\tep := model.Endpoint{\n20|\t\tOS:       runtime.GOOS,\n21|\t\tArch:     runtime.GOARCH,\n22|\t\tDeviceID: deviceID,\n23|\t}\n24|\tif h, err := os.Hostname(); err == nil {\n25|\t\tep.Hostname = h\n26|\t}\n27|\tif u, err := user.Current(); err == nil {\n28|\t\tep.Username = u.Username\n29|\t\tep.UID = u.Uid\n30|\t} else {\n31|\t\tep.UID = strconv.Itoa(os.Getuid())\n32|\t}\n33|\treturn ep\n34|}\n35|", "total_lines": 34, "file_size": 820, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|// Package scanner orchestrates a read-only package inventory scan.\n2|//\n3|// It walks configured roots, dispatches matching files to ecosystem scanners,\n4|// applies a per-scan time bound, and emits NDJSON records through the\n5|// supplied Emitter. The orchestrator owns concurrency: ecosystem scanners\n6|// themselves are single-threaded per file.\n7|package scanner\n8|\n9|import (\n10|\t\"context\"\n11|\t\"errors\"\n12|\t\"fmt\"\n13|\t\"io/fs\"\n14|\t\"os\"\n15|\t\"path/filepath\"\n16|\t\"strings\"\n17|\t\"sync\"\n18|\t\"syscall\"\n19|\t\"time\"\n20|\n21|\t\"github.com/perplexityai/bumblebee/internal/ecosystem/browserext\"\n22|\t\"github.com/perplexityai/bumblebee/internal/ecosystem/bun\"\n23|\t\"github.com/perplexityai/bumblebee/internal/ecosystem/composer\"\n24|\t\"github.com/perplexityai/bumblebee/internal/ecosystem/editorext\"\n25|\t\"github.com/perplexityai/bumblebee/internal/ecosystem/gomod\"\n26|\t\"github.com/perplexityai/bumblebee/internal/ecosystem/homebrew\"\n27|\t\"github.com/perplexityai/bumblebee/internal/ecosystem/mcp\"\n28|\t\"github.com/perplexityai/bumblebee/internal/ecosystem/npm\"\n29|\t\"github.com/perplexityai/bumblebee/internal/ecosystem/pnpm\"\n30|\t\"github.com/perplexityai/bumblebee/internal/ecosystem/pypi\"\n31|\t\"github.com/perplexityai/bumblebee/internal/ecosystem/rubygems\"\n32|\t\"github.com/perplexityai/bumblebee/internal/ecosystem/yarn\"\n33|\t\"github.com/perplexityai/bumblebee/internal/exposure\"\n34|\t\"github.com/perplexityai/bumblebee/internal/model\"\n35|\t\"github.com/perplexityai/bumblebee/internal/output\"\n36|\t\"github.com/perplexityai/bumblebee/internal/walk\"\n37|)\n38|\n39|// Root pairs a filesystem path with the RootKind that explains why it\n40|// is being walked. The RootKind of the longest enclosing configured\n41|// root is stamped onto every record produced from the subtree so\n42|// receivers can tell global toolchain inventory apart from a\n43|// configured project tree apart from a deep incident sweep. Scanners\n44|// that pre-fill RootKind on their records (e.g. browser extensions,\n45|// MCP configs) are overridden by the enclosing configured root when\n46|// one matches; their value is used only as a fallback for records\n47|// that fall outside every configured root.\n48|type Root struct {\n49|\tPath string\n50|\tKind string\n51|}\n52|\n53|// isExpectedAccessError reports whether err is one of the routine\n54|// \"I can't read this\" errors that show up constantly when walking a\n55|// macOS home directory or any privacy-protected tree. These are not\n56|// scanner failures; they are the OS telling us a subtree is off-limits.\n57|// We surface them at debug level so operators who want to audit them\n58|// can, but they no longer make a healthy scan look broken.\n59|func isExpectedAccessError(err error) bool {\n60|\tif err == nil {\n61|\t\treturn false\n62|\t}\n63|\tif errors.Is(err, os.ErrPermission) || errors.Is(err, fs.ErrPermission) {\n64|\t\treturn true\n65|\t}\n66|\t// macOS TCC denials surface as EPERM (\"operation not permitted\")\n67|\t// rather than EACCES; cover both.\n68|\tvar errno syscall.Errno\n69|\tif errors.As(err, &errno) {\n70|\t\tswitch errno {\n71|\t\tcase syscall.EACCES, syscall.EPERM:\n72|\t\t\treturn true\n73|\t\t}\n74|\t}\n75|\n76|\treturn false\n77|}\n78|\n79|// isMissingPathError reports whether err is a \"no such file or directory\"\n80|// (ENOENT) — typical for a default-candidate root that the host does not\n81|// have. We surface these at info rather than warn so fleet pipelines do\n82|// not have to whitelist routine per-host absences.\n83|func isMissingPathError(err error) bool {\n84|\tif err == nil {\n85|\t\treturn false\n86|\t}\n87|\tif errors.Is(err, fs.ErrNotExist) {\n88|\t\treturn true\n89|\t}\n90|\tvar errno syscall.Errno\n91|\tif errors.As(err, &errno) {\n92|\t\treturn errno == syscall.ENOENT\n93|\t}\n94|\treturn false\n95|}\n96|\n97|type Config struct {\n98|\tProfile     string\n99|\tRoots       []Root\n100|\tExcludes    []string\n101|\tEcosystems  map[string]bool\n102|\tMaxFileSize int64\n103|\tMaxDuration time.Duration\n104|\tConcurrency int\n105|\n106|\t// Catalog, when non-nil, drives finding emission. Every accepted\n107|\t// package record is matched against it; matches produce a\n108|\t// model.Finding record on the same sink.\n109|\tCatalog *exposure.Catalog\n110|\t// FindingsOnly suppresses package records from the records sink while\n111|\t// still evaluating them for exposure matches.\n112|\tFindingsOnly bool\n113|\n114|\tBaseRecord model.Record\n115|\tEmitter    *output.Emitter\n116|}\n117|\n118|type Result struct {\n119|\tFilesConsidered          int\n120|\tRecordsEmitted           int\n121|\tPackageRecordsSuppressed int\n122|\tFindingsEmitted          int\n123|\tDuplicates               int\n124|\tDiagnostics              int\n125|\tTimedOut                 bool\n126|\tDuration                 time.Duration\n127|}\n128|\n129|// Run executes one scan and returns aggregate counters. It blocks until\n130|// the walker finishes or ctx is cancelled.\n131|func Run(ctx context.Context, cfg Config) (Result, error) {\n132|\tif cfg.Concurrency < 1 {\n133|\t\tcfg.Concurrency = 4\n134|\t}\n135|\tif cfg.MaxDuration > 0 {\n136|\t\tvar cancel context.CancelFunc\n137|\t\tctx, cancel = context.WithTimeout(ctx, cfg.MaxDuration)\n138|\t\tdefer cancel()\n139|\t}\n140|\n141|\tstart := time.Now()\n142|\tcfg.BaseRecord.Profile = cfg.Profile\n143|\n144|\trootKindFor := newRootKindLookup(cfg.Roots)\n145|\n146|\tvar findingsEmitted int\n147|\tvar findingsMu sync.Mutex\n148|\tvar packageRecordsSuppressed int\n149|\tvar suppressedMu sync.Mutex\n150|\tvar emitErr error\n151|\tvar emitErrMu sync.Mutex\n152|\tsetEmitErr := func(err error) {\n153|\t\tif err == nil {\n154|\t\t\treturn\n155|\t\t}\n156|\t\temitErrMu.Lock()\n157|\t\tif emitErr == nil {\n158|\t\t\temitErr = err\n159|\t\t}\n160|\t\temitErrMu.Unlock()\n161|\t}\n162|\temit := func(r model.Record) {\n163|\t\t// Prefer the root_kind derived from the configured Roots so we\n164|\t\t// keep populations consistent. If the lookup can't classify the\n165|\t\t// source file (e.g. an operator-supplied root with an unrelated\n166|\t\t// kind), fall back to a scanner-supplied RootKind on the record\n167|\t\t// before defaulting to unknown.\n168|\t\tif rk := rootKindFor(r.SourceFile); rk != model.RootKindUnknown {\n169|\t\t\tr.RootKind = rk\n170|\t\t} else if r.RootKind == \"\" {\n171|\t\t\tr.RootKind = model.RootKindUnknown\n172|\t\t}\n173|\t\tif r.Profile == \"\" {\n174|\t\t\tr.Profile = cfg.Profile\n175|\t\t}\n176|\t\tr, written := cfg.Emitter.ObservePackage(r)\n177|\t\tif cfg.FindingsOnly {\n178|\t\t\tif written {\n179|\t\t\t\tsuppressedMu.Lock()\n180|\t\t\t\tpackageRecordsSuppressed++\n181|\t\t\t\tsuppressedMu.Unlock()\n182|\t\t\t}\n183|\t\t} else {\n184|\t\t\tif written {\n185|\t\t\t\tif err := cfg.Emitter.EmitObservedPackage(r); err != nil {\n186|\t\t\t\t\tsetEmitErr(fmt.Errorf(\"emit package record: %w\", err))\n187|\t\t\t\t\treturn\n188|\t\t\t\t}\n189|\t\t\t}\n190|\t\t}\n191|\t\tif !written {\n192|\t\t\t// Dedup suppressed; an earlier identical record already\n193|\t\t\t// produced its finding (if any). Skip to avoid emitting\n194|\t\t\t// the same finding twice for the same source file.\n195|\t\t\treturn\n196|\t\t}\n197|\t\tif cfg.Catalog != nil {\n198|\t\t\t// Emit one finding per matching catalog entry. With one\n199|\t\t\t// catalog file per advisory, overlapping advisories on the\n200|\t\t\t// same (ecosystem, name, version) are realistic and must not\n201|\t\t\t// be silently masked. Finding.StableID includes catalog_id,\n202|\t\t\t// so per-entry findings have distinct record_ids.\n203|\t\t\tfor _, m := range cfg.Catalog.MatchAll(r) {\n204|\t\t\t\tentry, version := m.Entry, m.Version\n205|\t\t\t\tf := model.Finding{\n206|\t\t\t\t\tRecordType:     model.RecordTypeFinding,\n207|\t\t\t\t\tSchemaVersion:  cfg.BaseRecord.SchemaVersion,\n208|\t\t\t\t\tScannerName:    cfg.BaseRecord.ScannerName,\n209|\t\t\t\t\tScannerVersion: cfg.BaseRecord.ScannerVersion,\n210|\t\t\t\t\tRunID:          cfg.BaseRecord.RunID,\n211|\t\t\t\t\tScanTime:       cfg.BaseRecord.ScanTime,\n212|\t\t\t\t\tEndpoint:       cfg.BaseRecord.Endpoint,\n213|\t\t\t\t\tProfile:        cfg.Profile,\n214|\t\t\t\t\tFindingType:    model.FindingTypePackageExposure,\n215|\t\t\t\t\tSeverity:       entry.Severity,\n216|\t\t\t\t\tCatalogID:      entry.ID,\n217|\t\t\t\t\tCatalogName:    entry.Name,\n218|\t\t\t\t\tEcosystem:      r.Ecosystem,\n219|\t\t\t\t\tPackageName:    r.PackageName,\n220|\t\t\t\t\tNormalizedName: r.NormalizedName,\n221|\t\t\t\t\tVersion:        r.Version,\n222|\t\t\t\t\tRootKind:       r.RootKind,\n223|\t\t\t\t\tProjectPath:    r.ProjectPath,\n224|\t\t\t\t\tSourceType:     r.SourceType,\n225|\t\t\t\t\tSourceFile:     r.SourceFile,\n226|\t\t\t\t\tConfidence:     r.Confidence,\n227|\t\t\t\t\tEvidence:       \"exact name+version match (version=\" + version + \")\",\n228|\t\t\t\t}\n229|\t\t\t\tif err := cfg.Emitter.EmitFinding(f); err == nil {\n230|\t\t\t\t\tfindingsMu.Lock()\n231|\t\t\t\t\tfindingsEmitted++\n232|\t\t\t\t\tfindingsMu.Unlock()\n233|\t\t\t\t} else {\n234|\t\t\t\t\tsetEmitErr(fmt.Errorf(\"emit finding record: %w\", err))\n235|\t\t\t\t\tbreak\n236|\t\t\t\t}\n237|\t\t\t}\n238|\t\t}\n239|\t}\n240|\tdiag := cfg.Emitter.Diag\n241|\n242|\tnpmS := &npm.Scanner{MaxFileSize: cfg.MaxFileSize, Emit: emit, Diag: diag}\n243|\tpyS := &pypi.Scanner{MaxFileSize: cfg.MaxFileSize, Emit: emit, Diag: diag}\n244|\tpnpmS := &pnpm.Scanner{MaxFileSize: cfg.MaxFileSize, Emit: emit, Diag: diag}\n245|\tyarnS := &yarn.Scanner{MaxFileSize: cfg.MaxFileSize, Emit: emit, Diag: diag}\n246|\tbunS := &bun.Scanner{MaxFileSize: cfg.MaxFileSize, Emit: emit, Diag: diag}\n247|\tgoS := &gomod.Scanner{MaxFileSize: cfg.MaxFileSize, Emit: emit, Diag: diag}\n248|\trbS := &rubygems.Scanner{MaxFileSize: cfg.MaxFileSize, Emit: emit, Diag: diag}\n249|\tcmpS := &composer.Scanner{MaxFileSize: cfg.MaxFileSize, Emit: emit, Diag: diag}\n250|\tmcpS := &mcp.Scanner{MaxFileSize: cfg.MaxFileSize, Emit: emit, Diag: diag}\n251|\textS := &editorext.Scanner{MaxFileSize: cfg.MaxFileSize, Emit: emit, Diag: diag}\n252|\tbxS := &browserext.Scanner{MaxFileSize: cfg.MaxFileSize, Emit: emit, Diag: diag}\n253|\thbS := &homebrew.Scanner{MaxFileSize: cfg.MaxFileSize, Emit: emit, Diag: diag}\n254|\n255|\ttype job struct {\n256|\t\tkind        string\n257|\t\tpath        string\n258|\t\tprojectPath string\n259|\t\textra1      string // generic slot 1 (e.g., extRoot, name)\n260|\t\textra2      string // generic slot 2 (e.g., extDir, version)\n261|\t}\n262|\tjobs := make(chan job, 256)\n263|\n264|\tvar wg sync.WaitGroup\n265|\tfor i := 0; i < cfg.Concurrency; i++ {\n266|\t\twg.Add(1)\n267|\t\tgo func() {\n268|\t\t\tdefer wg.Done()\n269|\t\t\tfor j := range jobs {\n270|\t\t\t\tselect {\n271|\t\t\t\tcase <-ctx.Done():\n272|\t\t\t\t\treturn\n273|\t\t\t\tdefault:\n274|\t\t\t\t}\n275|\t\t\t\temitErrMu.Lock()\n276|\t\t\t\thasEmitErr := emitErr != nil\n277|\t\t\t\temitErrMu.Unlock()\n278|\t\t\t\tif hasEmitErr {\n279|\t\t\t\t\treturn\n280|\t\t\t\t}\n281|\t\t\t\tvar err error\n282|\t\t\t\tswitch j.kind {\n283|\t\t\t\tcase \"npm-lock\":\n284|\t\t\t\t\terr = npmS.ScanLockfile(j.path, cfg.BaseRecord)\n285|\t\t\t\tcase \"npm-pj\":\n286|\t\t\t\t\terr = npmS.ScanNodeModulesPackageJSON(j.path, j.projectPath, cfg.BaseRecord)\n287|\t\t\t\tcase \"py-dist\":\n288|\t\t\t\t\terr = pyS.ScanDistInfo(j.path, j.projectPath, cfg.BaseRecord)\n289|\t\t\t\tcase \"py-egg\":\n290|\t\t\t\t\terr = pyS.ScanEggInfo(j.path, j.projectPath, cfg.BaseRecord)\n291|\t\t\t\tcase \"pnpm-lock\":\n292|\t\t\t\t\terr = pnpmS.ScanLockfile(j.path, cfg.BaseRecord)\n293|\t\t\t\tcase \"pnpm-pj\":\n294|\t\t\t\t\terr = pnpmS.ScanStorePackageJSON(j.path, j.projectPath, j.extra1, j.extra2, cfg.BaseRecord)\n295|\t\t\t\tcase \"yarn-lock\":\n296|\t\t\t\t\terr = yarnS.ScanLockfile(j.path, cfg.BaseRecord)\n297|\t\t\t\tcase \"bun-lock\":\n298|\t\t\t\t\terr = bunS.ScanTextLockfile(j.path, cfg.BaseRecord)\n299|\t\t\t\tcase \"go-sum\":\n300|\t\t\t\t\terr = goS.ScanGoSum(j.path, cfg.BaseRecord)\n301|\t\t\t\tcase \"go-mod\":\n302|\t\t\t\t\terr = goS.ScanGoMod(j.path, cfg.BaseRecord)\n303|\t\t\t\tcase \"rb-lock\":\n304|\t\t\t\t\terr = rbS.ScanGemfileLock(j.path, cfg.BaseRecord)\n305|\t\t\t\tcase \"rb-spec\":\n306|\t\t\t\t\terr = rbS.ScanGemspec(j.path, j.projectPath, cfg.BaseRecord)\n307|\t\t\t\tcase \"composer-lock\":\n308|\t\t\t\t\terr = cmpS.ScanComposerLock(j.path, cfg.BaseRecord)\n309|\t\t\t\tcase \"composer-installed\":\n310|\t\t\t\t\terr = cmpS.ScanInstalledJSON(j.path, cfg.BaseRecord)\n311|\t\t\t\tcase \"mcp-config\":\n312|\t\t\t\t\terr = mcpS.ScanConfig(j.path, cfg.BaseRecord)\n313|\t\t\t\tcase \"mcp-claude-config\":\n314|\t\t\t\t\terr = mcpS.ScanClaudeConfig(j.path, cfg.BaseRecord)\n315|\t\t\t\tcase \"editor-ext\":\n316|\t\t\t\t\terr = extS.ScanExtension(j.path, j.extra1, j.extra2, cfg.BaseRecord)\n317|\t\t\t\tcase \"chromium-ext\":\n318|\t\t\t\t\terr = bxS.ScanChromiumExtension(j.path, j.extra1, j.extra2, j.projectPath, cfg.BaseRecord)\n319|\t\t\t\tcase \"firefox-ext\":\n320|\t\t\t\t\terr = bxS.ScanFirefoxExtensions(j.path, cfg.BaseRecord)\n321|\t\t\t\tcase \"homebrew-formula\":\n322|\t\t\t\t\terr = hbS.ScanFormulaReceipt(j.path, j.extra1, j.extra2, j.projectPath, cfg.BaseRecord)\n323|\t\t\t\tcase \"homebrew-cask\":\n324|\t\t\t\t\terr = hbS.ScanCaskMetadata(j.path, j.extra1, j.extra2, j.projectPath, cfg.BaseRecord)\n325|\t\t\t\t}\n326|\t\t\t\tif err != nil {\n327|\t\t\t\t\tcfg.Emitter.Diag(\"error\", j.path, err.Error())\n328|\t\t\t\t}\n329|\t\t\t}\n330|\t\t}()\n331|\t}\n332|\n333|\tvar filesConsidered int\n334|\texcludes := append([]string{}, walk.DefaultExcludes...)\n335|\texcludes = append(excludes, cfg.Excludes...)\n336|\twalkOpts := walk.Options{\n337|\t\tRoots:    rootPaths(cfg.Roots),\n338|\t\tExcludes: excludes,\n339|\t\tOnError: func(path string, err error) {\n340|\t\t\tlevel := \"warn\"\n341|\t\t\tswitch {\n342|\t\t\tcase isExpectedAccessError(err):\n343|\t\t\t\tlevel = \"debug\"\n344|\t\t\tcase isMissingPathError(err):\n345|\t\t\t\t// An absent default-candidate root is a benign,\n346|\t\t\t\t// expected outcome on machines that don't have that\n347|\t\t\t\t// path. Surface it so operators can audit, but at\n348|\t\t\t\t// info so fleet pipelines don't have to whitelist\n349|\t\t\t\t// a per-host warning.\n350|\t\t\t\tlevel = \"info\"\n351|\t\t\t}\n352|\t\t\tcfg.Emitter.Diag(level, path, err.Error())\n353|\t\t},\n354|\t}\n355|\n356|\tsend := func(j job) bool {\n357|\t\tselect {\n358|\t\tcase <-ctx.Done():\n359|\t\t\treturn false\n360|\t\tdefault:\n361|\t\t}\n362|\t\temitErrMu.Lock()\n363|\t\thasEmitErr := emitErr != nil\n364|\t\temitErrMu.Unlock()\n365|\t\tif hasEmitErr {\n366|\t\t\treturn false\n367|\t\t}\n368|\t\tselect {\n369|\t\tcase <-ctx.Done():\n370|\t\t\treturn false\n371|\t\tcase jobs <- j:\n372|\t\t\treturn true\n373|\t\t}\n374|\t}\n375|\tenabled := func(ecosystem string) bool {\n376|\t\tif len(cfg.Ecosystems) == 0 {\n377|\t\t\treturn true\n378|\t\t}\n379|\t\treturn cfg.Ecosystems[ecosystem]\n380|\t}\n381|\twalkErr := walk.Walk(walkOpts, func(path string, d fs.DirEntry) error {\n382|\t\tselect {\n383|\t\tcase <-ctx.Done():\n384|\t\t\treturn filepath.SkipDir\n385|\t\tdefault:\n386|\t\t}\n387|\t\temitErrMu.Lock()\n388|\t\thasEmitErr := emitErr != nil\n389|\t\temitErrMu.Unlock()\n390|\t\tif hasEmitErr {\n391|\t\t\treturn filepath.SkipDir\n392|\t\t}\n393|\t\tif d.IsDir() {\n394|\t\t\treturn nil\n395|\t\t}\n396|\t\tbase := d.Name()\n397|\t\t// Skip obvious credential-ish dotfiles even outside excluded dirs.\n398|\t\tif base == \".env\" || base == \".envrc\" {\n399|\t\t\treturn nil\n400|\t\t}\n401|\t\tfilesConsidered++\n402|\t\tswitch {\n403|\t\tcase enabled(model.EcosystemNPM) && npm.IsLockfile(base):\n404|\t\t\tsend(job{kind: \"npm-lock\", path: path})\n405|\t\tcase enabled(model.EcosystemNPM) && pnpm.IsLockfile(base):\n406|\t\t\tsend(job{kind: \"pnpm-lock\", path: path})\n407|\t\tcase enabled(model.EcosystemNPM) && yarn.IsLockfile(base):\n408|\t\t\tsend(job{kind: \"yarn-lock\", path: path})\n409|\t\tcase enabled(model.EcosystemNPM) && bun.IsTextLockfile(base):\n410|\t\t\tsend(job{kind: \"bun-lock\", path: path})\n411|\t\tcase enabled(model.EcosystemNPM) && bun.IsBinaryLockfile(base):\n412|\t\t\tbunS.NoteBinaryLockfile(path)\n413|\t\tcase enabled(model.EcosystemGo) && gomod.IsGoSum(base):\n414|\t\t\tsend(job{kind: \"go-sum\", path: path})\n415|\t\tcase enabled(model.EcosystemGo) && gomod.IsGoMod(base):\n416|\t\t\tsend(job{kind: \"go-mod\", path: path})\n417|\t\tcase enabled(model.EcosystemRubyGems) && rubygems.IsGemfileLock(base):\n418|\t\t\tsend(job{kind: \"rb-lock\", path: path})\n419|\t\tcase enabled(model.EcosystemRubyGems) && rubygems.IsGemspec(base):\n420|\t\t\tif ok, gemsDir := rubygems.IsInstalledGemspec(path); ok {\n421|\t\t\t\tsend(job{kind: \"rb-spec\", path: path, projectPath: gemsDir})\n422|\t\t\t}\n423|\t\tcase enabled(model.EcosystemPackagist) && composer.IsComposerLock(base):\n424|\t\t\tsend(job{kind: \"composer-lock\", path: path})\n425|\t\tcase enabled(model.EcosystemPackagist) && base == \"installed.json\" && composer.IsInstalledJSON(path):\n426|\t\t\tsend(job{kind: \"composer-installed\", path: path})\n427|\t\tcase enabled(model.EcosystemMCP) && mcp.IsKnownMCPConfig(base):\n428|\t\t\tsend(job{kind: \"mcp-config\", path: path})\n429|\t\tcase enabled(model.EcosystemMCP) && base == \"settings.json\" && mcp.IsGeminiSettingsJSON(path):\n430|\t\t\tsend(job{kind: \"mcp-config\", path: path})\n431|\t\tcase enabled(model.EcosystemMCP) && mcp.IsClaudeConfigJSON(path):\n432|\t\t\tsend(job{kind: \"mcp-claude-config\", path: path})\n433|\t\tcase enabled(model.EcosystemBrowserExtension) && base == \"manifest.json\":\n434|\t\t\tif ok, extID, verDir, profDir := browserext.IsChromiumExtensionManifest(path); ok {\n435|\t\t\t\tsend(job{kind: \"chromium-ext\", path: path, projectPath: profDir, extra1: extID, extra2: verDir})\n436|\t\t\t}\n437|\t\tcase enabled(model.EcosystemBrowserExtension) && base == \"extensions.json\":\n438|\t\t\tif browserext.IsFirefoxExtensionsJSON(path) {\n439|\t\t\t\tsend(job{kind: \"firefox-ext\", path: path})\n440|\t\t\t}\n441|\t\tcase enabled(model.EcosystemHomebrew) && base == \"INSTALL_RECEIPT.json\":\n442|\t\t\tif ok, name, version, cellarDir := homebrew.IsFormulaReceipt(path); ok {\n443|\t\t\t\tsend(job{kind: \"homebrew-formula\", path: path, projectPath: cellarDir, extra1: name, extra2: version})\n444|\t\t\t}\n445|\t\tcase enabled(model.EcosystemHomebrew) && homebrew.LooksLikeCaskMetadataMarker(path):\n446|\t\t\t// This one intentionally does a tiny sibling check in the walker\n447|\t\t\t// so a cask with .internal.json, .json, and .rb markers emits only\n448|\t\t\t// Homebrew's preferred installed-cask snapshot. That adds serial\n449|\t\t\t// I/O to cask marker dispatch, but typical Caskroom cardinality is\n450|\t\t\t// small and avoiding duplicate records keeps downstream state clean.\n451|\t\t\tif ok, token, version, caskroomDir := homebrew.IsCaskMetadataMarker(path); ok {\n452|\t\t\t\tsend(job{kind: \"homebrew-cask\", path: path, projectPath: caskroomDir, extra1: token, extra2: version})\n453|\t\t\t}\n454|\t\tcase base == \"package.json\":\n455|\t\t\t// Prefer extension match over node_modules.\n456|\t\t\tif enabled(model.EcosystemEditorExtension) {\n457|\t\t\t\tif ok, extRoot, extDir := editorext.IsExtensionPackageJSON(path); ok {\n458|\t\t\t\t\tsend(job{kind: \"editor-ext\", path: path, extra1: extRoot, extra2: extDir})\n459|\t\t\t\t\tbreak\n460|\t\t\t\t}\n461|\t\t\t}\n462|\t\t\tif !enabled(model.EcosystemNPM) {\n463|\t\t\t\tbreak\n464|\t\t\t}\n465|\t\t\tif ok, proj, name, ver := pnpm.IsPnpmStorePackageJSON(path); ok {\n466|\t\t\t\tsend(job{kind: \"pnpm-pj\", path: path, projectPath: proj, extra1: name, extra2: ver})\n467|\t\t\t\tbreak\n468|\t\t\t}\n469|\t\t\tif ok, proj := npm.IsNodeModulesPackageJSON(path); ok {\n470|\t\t\t\tsend(job{kind: \"npm-pj\", path: path, projectPath: proj})\n471|\t\t\t}\n472|\t\tcase enabled(model.EcosystemPyPI) && base == \"METADATA\":\n473|\t\t\tif ok, dir := pypi.IsDistInfoMetadata(path); ok {\n474|\t\t\t\tsend(job{kind: \"py-dist\", path: path, projectPath: dir})\n475|\t\t\t}\n476|\t\tcase enabled(model.EcosystemPyPI) && base == \"PKG-INFO\":\n477|\t\t\tif ok, dir := pypi.IsEggInfoPKGInfo(path); ok {\n478|\t\t\t\tsend(job{kind: \"py-egg\", path: path, projectPath: dir})\n479|\t\t\t}\n480|\t\t}\n481|\t\treturn nil\n482|\t})\n483|\n484|\tclose(jobs)\n485|\twg.Wait()\n486|\n487|\tres := Result{\n488|\t\tFilesConsidered: filesConsidered,\n489|\t\tRecordsEmitted:  cfg.Emitter.RecordsEmitted,\n490|\t\tFindingsEmitted: findingsEmitted,\n491|\t\tDuplicates:      cfg.Emitter.Duplicates,\n492|\t\tDiagnostics:     cfg.Emitter.Diagnostics,\n493|\t\tDuration:        time.Since(start),\n494|\t}\n495|\tsuppressedMu.Lock()\n496|\tres.PackageRecordsSuppressed = packageRecordsSuppressed\n497|\tsuppressedMu.Unlock()\n498|\tif errors.Is(ctx.Err(), context.DeadlineExceeded) {\n499|\t\tres.TimedOut = true\n500|\t}\n501|", "total_lines": 556, "file_size": 18790, "truncated": true, "hint": "Use offset=501 to continue reading (showing 1-500 of 556 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  ## Análise — perplxexityai/bumblebee

  ### Problema que resolve
  Em resposta a um advisory de supply-chain, equipes precisam saber **quais dev endpoints têm, agora, uma correspondência exata no disco** (lockfiles, manifests, metadados de pacotes, extensões e configs de ferramentas). Esse estado local é fragmentado e os meios tradicionais não cobrem bem esse cenário: SBOMs mostram o que foi empacotado, EDR mostra o que executou, mas a resposta a incidentes frequentemente precisa dessa visão de inventário “suja” e local.

  ### Tema principal
  Scanner read-only de inventário de endpoints de desenvolvedor, gerando registros NDJSON de pacotes, extensões e ferramentas locais, e oferecendo verificação de exposição contra catálogos de advisory.

  ### Razão do hype/tendência
  - **Escopo estreito e resolvível**: responde uma pergunta de incidente que SBOM/EDR não cobriam.
  - **Design minimalista**: Go 1.25+, binário estático, zero dependências além da stdlib.
  - **Perfis operacionais claros** (`baseline`, `project`, `deep`) para diferentes frequências e tipos de varredura.
  - **Read-only e seguro**: não executa gerenciadores de pacotes e não lê código fonte.
  - **Pronto para-uso em fleet**: selftest embutido, saída NDJSON, modelo de estado e suporte a descoberta de raízes.

  ### Tecnologias envolvidas
  - Go (stdlib apenas)
  - NDJSON para transporte de registros
  - Múltiplas fontes/metadados de ecossistemas: npm, pnpm, yarn, bun, Go modules, PyPI, RubyGems, Composer, MCP, extensões de editor/browser, Homebrew
  - HTTP sink opcional, catálogos JSON de exposição, dedup, timeouts e concorrência controlada  


c0:
    [{"id": "chatcmpl-tool-b504ab67734cd4bf", "call_id": "chatcmpl-tool-b504ab67734cd4bf", "response_item_id": "fc_chatcmpl-tool-b504ab67734cd4bf", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice && git clone https://github.com/vercel-labs/zero-native.git 2>&1\", \"timeout\": 120}"}}]


c0:
    [{"id": "chatcmpl-tool-a5a0e9ef1c09997c", "call_id": "chatcmpl-tool-a5a0e9ef1c09997c", "response_item_id": "fc_chatcmpl-tool-a5a0e9ef1c09997c", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\": \"/home/ice/zero-native\", \"pattern\": \"*\", \"target\": \"files\"}"}}]


c0:
  {"total_count": 50, "files": ["/home/ice/zero-native/tools/zero-native/skills.zig", "/home/ice/zero-native/tools/zero-native/main.zig", "/home/ice/zero-native/tools/zero-native/automation.zig", "/home/ice/zero-native/tools/cef/build-from-source.sh", "/home/ice/zero-native/third_party/cef/README.md", "/home/ice/zero-native/tests/README.md", "/home/ice/zero-native/src/window_state/root.zig", "/home/ice/zero-native/src/tooling/web_engine.zig", "/home/ice/zero-native/src/tooling/templates.zig", "/home/ice/zero-native/src/tooling/root.zig", "/home/ice/zero-native/src/tooling/raw_manifest.zig", "/home/ice/zero-native/src/tooling/package.zig", "/home/ice/zero-native/src/tooling/manifest.zig", "/home/ice/zero-native/src/tooling/doctor.zig", "/home/ice/zero-native/src/tooling/dev.zig", "/home/ice/zero-native/src/tooling/codesign.zig", "/home/ice/zero-native/src/tooling/cef.zig", "/home/ice/zero-native/src/tooling/assets.zig", "/home/ice/zero-native/src/security/root.zig", "/home/ice/zero-native/src/runtime/root.zig", "/home/ice/zero-native/src/root.zig", "/home/ice/zero-native/src/primitives/trace/root.zig", "/home/ice/zero-native/src/primitives/platform_info/root.zig", "/home/ice/zero-native/src/primitives/json/root.zig", "/home/ice/zero-native/src/primitives/geometry/root.zig", "/home/ice/zero-native/src/primitives/diagnostics/root.zig", "/home/ice/zero-native/src/primitives/assets/root.zig", "/home/ice/zero-native/src/primitives/app_manifest/root.zig", "/home/ice/zero-native/src/primitives/app_dirs/root.zig", "/home/ice/zero-native/src/platform/windows/webview2_host.cpp", "/home/ice/zero-native/src/platform/windows/root.zig", "/home/ice/zero-native/src/platform/windows/cef_host.cpp", "/home/ice/zero-native/src/platform/root.zig", "/home/ice/zero-native/src/platform/policy_values.zig", "/home/ice/zero-native/src/platform/macos/root.zig", "/home/ice/zero-native/src/platform/macos/cef_host.mm", "/home/ice/zero-native/src/platform/macos/automation_host.m", "/home/ice/zero-native/src/platform/macos/automation_host.h", "/home/ice/zero-native/src/platform/macos/appkit_host.m", "/home/ice/zero-native/src/platform/macos/appkit_host.h", "/home/ice/zero-native/src/platform/linux/root.zig", "/home/ice/zero-native/src/platform/linux/gtk_host.h", "/home/ice/zero-native/src/platform/linux/gtk_host.c", "/home/ice/zero-native/src/platform/linux/cef_host.cpp", "/home/ice/zero-native/src/js/root.zig", "/home/ice/zero-native/src/frontend/root.zig", "/home/ice/zero-native/src/extensions/root.zig", "/home/ice/zero-native/src/embed/root.zig", "/home/ice/zero-native/src/debug/root.zig", "/home/ice/zero-native/src/bridge/root.zig"]}

  [Subdirectory context discovered: zero-native/AGENTS.md]
  # Agent Rules

  ## Releasing

  Releases are manual, single-PR affairs. The maintainer controls the changelog voice and format.

  To prepare a release:

  1. Create a branch (e.g. `prepare-v1.2.0`)
  2. Bump the version in `packages/zero-native/package.json`
  3. Run `npm --prefix packages/zero-native run version:sync` to update all version references
  4. Write the changelog entry in `CHANGELOG.md`, wrapped in `<!-- release:start -->` and `<!-- release:end -->` markers
  5. Remove the `<!-- release:start -->` and `<!-- release:end -->` markers from the previous release entry; only the latest release should have markers
  6. Open a PR and merge to `main`

  CI compares the version in `packages/zero-native/package.json` to what's on npm. If it differs, it publishes the CLI package and creates the GitHub release automatically. If npm already has the version but the GitHub release is missing, CI creates the GitHub release from the marked changelog entry. search_files 


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c0:
  {"output": "total 120\ndrwxrwxr-x 14 ice ice    23 May 30 22:52 .\ndrwx------ 18 ice ice    23 May 30 22:52 ..\ndrwxrwxr-x  8 ice ice    13 May 30 22:52 .git\ndrwxrwxr-x  3 ice ice     3 May 30 22:52 .github\n-rw-rw-r--  1 ice ice   397 May 30 22:52 .gitignore\n-rw-rw-r--  1 ice ice   948 May 30 22:52 AGENTS.md\n-rw-rw-r--  1 ice ice  5747 May 30 22:52 CHANGELOG.md\n-rw-rw-r--  1 ice ice  3187 May 30 22:52 CONTRIBUTING.md\n-rw-rw-r--  1 ice ice 10174 May 30 22:52 LICENSE\n-rw-rw-r--  1 ice ice  4256 May 30 22:52 README.md\n-rw-rw-r--  1 ice ice  1410 May 30 22:52 app.zon\ndrwxrwxr-x  2 ice ice     6 May 30 22:52 assets\n-rw-rw-r--  1 ice ice 34637 May 30 22:52 build.zig\n-rw-rw-r--  1 ice ice   455 May 30 22:52 build.zig.zon\ndrwxrwxr-x  4 ice ice    11 May 30 22:52 docs\ndrwxrwxr-x 11 ice ice    13 May 30 22:52 examples\ndrwxrwxr-x  3 ice ice     3 May 30 22:52 packages\ndrwxrwxr-x  4 ice ice     4 May 30 22:52 skill-data\ndrwxrwxr-x  3 ice ice     3 May 30 22:52 skills\ndrwxrwxr-x 16 ice ice    17 May 30 22:52 src\ndrwxrwxr-x  2 ice ice     3 May 30 22:52 tests\ndrwxrwxr-x  3 ice ice     3 May 30 22:52 third_party\ndrwxrwxr-x  4 ice ice     4 May 30 22:52 tools", "exit_code": 0, "error": null} terminal 


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c0:
  {"content": "1|# zero-native\n2|\n3|Build native desktop apps with web UI. Tiny binaries. Minimal memory. Instant rebuilds.\n4|\n5|zero-native is a Zig desktop app shell for modern web frontends. Use the platform WebView when you want the smallest possible app, or bundle Chromium through CEF when rendering consistency matters.\n6|\n7|## Quick Start\n8|\n9|Install the CLI:\n10|\n11|```bash\n12|npm install -g zero-native\n13|```\n14|\n15|Create and run an app:\n16|\n17|```bash\n18|zero-native init my_app --frontend next\n19|cd my_app\n20|zig build run\n21|```\n22|\n23|The first run installs frontend dependencies, builds the generated native shell, and opens a desktop window rendering your web UI.\n24|\n25|Read the full guide at [zero-native.dev/quick-start](https://zero-native.dev/quick-start).\n26|\n27|## Why zero-native\n28|\n29|### Tiny and fast\n30|\n31|System WebView apps do not bundle a browser runtime, so the native shell stays small and starts quickly. Your app uses WKWebView on macOS and WebKitGTK on Linux.\n32|\n33|### Choose your web engine\n34|\n35|Pick the engine that fits the product. System WebView gives you a lightweight native footprint. Chromium through CEF gives you predictable rendering and a pinned web platform on supported targets.\n36|\n37|### Fast native rebuilds\n38|\n39|The native layer is Zig, so app logic, bridge commands, and platform integrations rebuild quickly. Your frontend can still use the web tooling you already know.\n40|\n41|### Native power without heavy glue\n42|\n43|Zig calls C directly, which keeps platform SDKs, native libraries, codecs, and local system integrations within reach when the WebView layer needs to do real native work.\n44|\n45|### Explicit security model\n46|\n47|The WebView is treated as untrusted by default. Native commands, permissions, navigation, external links, and window APIs are opt-in and policy controlled.\n48|\n49|## Status\n50|\n51|zero-native is pre-release. Desktop support now covers macOS 11+, Linux, and Windows build paths, with Chromium/CEF distributed as platform-specific runtimes.\n52|\n53|## Core Concepts\n54|\n55|`App` is the small Zig object that describes your application: name, WebView source, lifecycle hooks, and optional native services.\n56|\n57|`Runtime` owns the event loop, windows, bridge dispatch, automation hooks, tracing, and platform services.\n58|\n59|`WebViewSource` tells the runtime what to load: inline HTML, a URL, or packaged frontend assets served from a local app origin.\n60|\n61|`app.zon` is the app manifest. It declares app metadata, icons, windows, frontend assets, web engine selection, security policy, bridge permissions, and packaging inputs.\n62|\n63|`window.zero.invoke()` is the JavaScript-to-Zig bridge. Calls are size-limited, origin checked, permission checked, and routed only to registered handlers.\n64|\n65|## Configuration\n66|\n67|Most project-level behavior lives in `app.zon`:\n68|\n69|```zig\n70|.{\n71|    .id = \"com.example.my-app\",\n72|    .name = \"my-app\",\n73|    .display_name = \"My App\",\n74|    .version = \"0.1.0\",\n75|    .web_engine = \"system\",\n76|    .permissions = .{ \"window\" },\n77|    .capabilities = .{ \"webview\", \"js_bridge\" },\n78|    .security = .{\n79|        .navigation = .{\n80|            .allowed_origins = .{ \"zero://app\", \"http://127.0.0.1:5173\" },\n81|        },\n82|    },\n83|    .windows = .{\n84|        .{ .label = \"main\", .title = \"My App\", .width = 960, .height = 640 },\n85|    },\n86|}\n87|```\n88|\n89|Use `.web_engine = \"system\"` for the platform WebView. On supported macOS builds, use `.web_engine = \"chromium\"` with a `.cef` config when you want to bundle Chromium.\n90|\n91|## Documentation\n92|\n93|The full documentation is at [zero-native.dev](https://zero-native.dev).\n94|\n95|- [Quick Start](https://zero-native.dev/quick-start)\n96|- [Web Engines](https://zero-native.dev/web-engines)\n97|- [App Model](https://zero-native.dev/app-model)\n98|- [Bridge](https://zero-native.dev/bridge)\n99|- [Security](https://zero-native.dev/security)\n100|- [Packaging](https://zero-native.dev/packaging)\n101|\n102|## Examples\n103|\n104|Framework-specific starter examples live in `examples/`:\n105|\n106|- `examples/next`\n107|- `examples/react`\n108|- `examples/svelte`\n109|- `examples/vue`\n110|\n111|Each example is a complete zero-native app with `app.zon`, a Zig shell, and a minimal frontend project. Run one with `zig build run` from its directory.\n112|\n113|Mobile embedding examples are available too:\n114|\n115|- `examples/ios`\n116|- `examples/android`\n117|\n118|These show how an iOS or Android host app links the zero-native C ABI from `libzero-native.a`.\n119|\n120|For local framework development, see [CONTRIBUTING.md](./CONTRIBUTING.md).\n121|", "total_lines": 120, "file_size": 4256, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|.{\n2|    .name = .zero_native,\n3|    .fingerprint = 0x338d08a1e3dd81aa,\n4|    .version = \"0.1.0\",\n5|    .minimum_zig_version = \"0.16.0\",\n6|    .dependencies = .{},\n7|    .paths = .{\n8|        \"README.md\",\n9|        \"CHANGELOG.md\",\n10|        \"LICENSE\",\n11|        \"SECURITY.md\",\n12|        \"app.zon\",\n13|        \"assets\",\n14|        \"build.zig\",\n15|        \"build.zig.zon\",\n16|        \"docs\",\n17|        \"examples\",\n18|        \"src\",\n19|        \"templates\",\n20|        \"tests\",\n21|        \"tools\",\n22|    },\n23|}\n24|", "total_lines": 23, "file_size": 455, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|# Changelog\n2|\n3|All notable changes to zero-native will be documented in this file.\n4|\n5|## 0.2.0\n6|\n7|<!-- release:start -->\n8|\n9|### New Features\n10|\n11|- **Layered WebView runtime**: Model each native window as a stack of named WebViews, including the reserved startup `main` WebView and child WebViews with frame, layer, zoom, transparency, routing, resizing, reload, and close support across the native backends (#28).\n12|- **JavaScript WebView API**: Add typed `window.zero.webviews.*` helpers and `zero-native.webview.*` built-in bridge commands for create, list, setFrame, navigate, setZoom, setLayer, and close operations (#28).\n13|- **Isolated child WebViews**: Keep child WebViews bridge-isolated by default, allow trusted child chrome with `bridge: true`, enforce navigation policy on child URLs, and scope WebView commands to the calling native window (#28).\n14|- **Browser example**: Add a browser-style example that demonstrates layered WebViews, browser controls, isolated page content, frontend asset handling, and the root `zig build run-browser` command (#28).\n15|- **zero-native skills**: Ship CLI-served agent skills and reference material for building and automating zero-native apps (#38).\n16|\n17|### Improvements\n18|\n19|- **WebView and bridge documentation**: Document WebView APIs, built-in bridge commands, security boundaries, backend support, packaging, testing, and app model updates (#28, #38).\n20|- **WebView smoke coverage**: Extend automation smoke tests to exercise child WebView create, resize, navigate, and close operations for system WebView and macOS CEF builds (#28).\n21|- **CEF runtime builds**: Harden the CEF runtime workflows across macOS, Linux, and Windows, including Windows runtime build fixes (#25, #26).\n22|- **macOS compatibility**: Set the native app baseline to macOS 11 (#22).\n23|- **Contributor guidance**: Clarify signed commit requirements and contribution PR guidance (#10).\n24|\n25|### Bug Fixes\n26|\n27|- **Windows WebView builds**: Fix Windows WebView build failures before the layered WebView release.\n28|- **React example dependencies**: Include the missing React example type dependencies (#11).\n29|- **GitHub release notes**: Avoid duplicate contributor lists when creating GitHub releases (#24).\n30|- **macOS package permissions**: Preserve executable permissions for packaged macOS app binaries (#39).\n31|\n32|### Contributors\n33|\n34|- @Anshuman71\n35|- @PrathamGhaywat\n36|- @ctate\n37|<!-- release:end -->\n38|\n39|## 0.1.9\n40|\n41|### New Features\n42|\n43|- **Linux and Windows desktop support**: Add platform-aware CEF tooling, Linux and Windows desktop build paths, Windows native host plumbing, and cross-platform CEF runtime packaging/release coverage.\n44|\n45|### Contributors\n46|\n47|- @ctate\n48|\n49|## 0.1.8\n50|\n51|### Bug Fixes\n52|\n53|- **Install completion delay** - Drain redirected GitHub responses during postinstall so npm exits immediately after the native binary is installed.\n54|\n55|### Contributors\n56|\n57|- @ctate\n58|\n59|## 0.1.7\n60|\n61|### Improvements\n62|\n63|- **Install progress** - Show native binary download progress and checksum status during the npm postinstall step.\n64|\n65|### Contributors\n66|\n67|- @ctate\n68|\n69|## 0.1.6\n70|\n71|### Improvements\n72|\n73|- **Init next steps** - Print the follow-up commands after scaffolding so users can immediately run their new app.\n74|\n75|### Contributors\n76|\n77|- @ctate\n78|\n79|## 0.1.5\n80|\n81|### Bug Fixes\n82|\n83|- **macOS local asset loading** - Prefer current-directory asset roots during local `zig build run` so Vite-based examples render their production bundles instead of blank windows.\n84|\n85|### Contributors\n86|\n87|- @ctate\n88|\n89|## 0.1.4\n90|\n91|### Bug Fixes\n92|\n93|- **Scaffolded app builds** - Ship the framework source tree in the npm package and make `zero-native init` point generated apps at the installed package root so `zig build run` can resolve `src/root.zig`.\n94|- **Long scaffold names** - Keep generated Zig package names within Zig's 32-character manifest limit.\n95|- **Next scaffold builds** - Include the Node.js type package that Next expects for TypeScript projects.\n96|- **Frontend dependency versions** - Generate projects with current Next, React, Vite, Vue, Svelte, and plugin versions.\n97|- **Svelte scaffold builds** - Use the matching Svelte Vite plugin in generated Svelte projects.\n98|\n99|### Contributors\n100|\n101|- @ctate\n102|\n103|## 0.1.3\n104|\n105|### Bug Fixes\n106|\n107|- **CLI package homepage** - Point npm package metadata at `https://zero-native.dev`.\n108|- **Current-directory init** - Support `zero-native init --frontend <framework>` as shorthand for scaffolding into the current directory.\n109|- **CLI usage errors** - Exit cleanly for invalid CLI arguments instead of printing Zig stack traces for expected user input mistakes.\n110|\n111|### Contributors\n112|\n113|- @ctate\n114|\n115|## 0.1.2\n116|\n117|### Bug Fixes\n118|\n119|- **npm install fallback** - Do not fail package installation or point global shims at missing binaries when a native release asset is unavailable.\n120|- **Release asset ordering** - Upload the macOS arm64 native binary and `CHECKSUMS.txt` before publishing the npm package so postinstall downloads succeed immediately.\n121|\n122|### Contributors\n123|\n124|- @ctate\n125|\n126|## 0.1.1\n127|\n128|### Bug Fixes\n129|\n130|- **npm package homepage** - Add the zero-native repository homepage to the CLI package metadata.\n131|- **Chromium example launches** - Stage the CEF framework correctly for the `hello` and `webview` examples when running with `-Dweb-engine=chromium`.\n132|- **Linux WebKitGTK build** - Update navigation policy and external URI handling for current WebKitGTK and GTK4 headers.\n133|- **macOS WebView smoke test** - Use the emitted CLI binary and queue automation early enough for stable CI smoke tests.\n134|\n135|### Release Process\n136|\n137|- **GitHub releases** - Create missing GitHub releases from marked changelog entries when npm already has the version.\n138|- **CEF runtime release** - Publish the prepared macOS arm64 CEF runtime used by `zero-native cef install`.\n139|\n140|### Contributors\n141|\n142|- @ctate\n143|\n144|## 0.1.0\n145|\n146|### Initial Release\n147|\n148|- Initial pre-release development version.\n149|", "total_lines": 148, "file_size": 5747, "truncated": false, "is_binary": false, "is_image": false} read_file 


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  [Subdirectory context discovered: zero-native/docs/AGENTS.md]
  # Docs Site Conventions

  ## MDX Tables

  Always use HTML `<table>` syntax in MDX pages, never markdown pipe tables. This ensures consistent styling and avoids MDX parsing edge cases.

  ```html
  <table>
    <thead>
      <tr>
        <th>Column</th>
        <th>Description</th>
      </tr>
    </thead>
    <tbody>
      <tr>
        <td><code>field</code></td>
        <td>What it does</td>
      </tr>
    </tbody>
  </table>
  ``` terminal 


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c0:
  {"content": "1|const std = @import(\"std\");\n2|const web_engine_tool = @import(\"src/tooling/web_engine.zig\");\n3|\n4|const PlatformOption = enum {\n5|    auto,\n6|    null,\n7|    macos,\n8|    linux,\n9|    windows,\n10|};\n11|\n12|const TraceOption = enum {\n13|    off,\n14|    events,\n15|    runtime,\n16|    all,\n17|};\n18|\n19|const WebEngineOption = enum {\n20|    system,\n21|    chromium,\n22|};\n23|\n24|const PackageTarget = enum {\n25|    macos,\n26|    windows,\n27|    linux,\n28|    ios,\n29|    android,\n30|};\n31|\n32|const SigningMode = enum {\n33|    none,\n34|    adhoc,\n35|    identity,\n36|};\n37|\n38|pub fn build(b: *std.Build) void {\n39|    const target = b.standardTargetOptions(.{});\n40|    const optimize = b.standardOptimizeOption(.{});\n41|    const platform_option = b.option(PlatformOption, \"platform\", \"Desktop backend: auto, null, macos, linux, windows\") orelse .auto;\n42|    const trace_option = b.option(TraceOption, \"trace\", \"Trace output: off, events, runtime, all\") orelse .events;\n43|    _ = b.option(bool, \"debug-overlay\", \"Enable debug overlay output\") orelse false;\n44|    _ = b.option(bool, \"automation\", \"Enable zero-native automation artifacts\") orelse false;\n45|    _ = b.option(bool, \"webview\", \"Deprecated: WebView is the only runtime surface\") orelse true;\n46|    const web_engine_override = b.option(WebEngineOption, \"web-engine\", \"Override app.zon web engine: system, chromium\");\n47|    const cef_dir_override = b.option([]const u8, \"cef-dir\", \"Override CEF root directory for Chromium builds\");\n48|    const cef_auto_install_override = b.option(bool, \"cef-auto-install\", \"Override app.zon CEF auto-install setting\");\n49|    _ = b.option(bool, \"js-bridge\", \"Enable optional JavaScript bridge stubs\") orelse false;\n50|    const package_target = b.option(PackageTarget, \"package-target\", \"Package target: macos, windows, linux, ios, android\") orelse .macos;\n51|    const signing_mode = b.option(SigningMode, \"signing\", \"Signing mode: none, adhoc, identity\") orelse .none;\n52|    const package_version = packageVersion(b);\n53|    const optimize_name = @tagName(optimize);\n54|    const app_web_engine = web_engine_tool.readManifestConfig(b.allocator, b.graph.io, \"app.zon\") catch |err| {\n55|        std.debug.panic(\"failed to read app.zon web engine config: {s}\", .{@errorName(err)});\n56|    };\n57|    const resolved_web_engine = web_engine_tool.resolve(app_web_engine, .{\n58|        .web_engine = if (web_engine_override) |value| webEngineFromBuildOption(value) else null,\n59|        .cef_dir = cef_dir_override,\n60|        .cef_auto_install = cef_auto_install_override,\n61|    }) catch |err| {\n62|        std.debug.panic(\"invalid app.zon web engine config: {s}\", .{@errorName(err)});\n63|    };\n64|    const web_engine = buildWebEngineFromResolved(resolved_web_engine.engine);\n65|    const browser_web_engine: WebEngineOption = web_engine_override orelse .system;\n66|    const cef_auto_install = resolved_web_engine.cef_auto_install;\n67|    const selected_platform: PlatformOption = switch (platform_option) {\n68|        .auto => if (target.result.os.tag == .macos) .macos else if (target.result.os.tag == .linux) .linux else if (target.result.os.tag == .windows) .windows else .null,\n69|        else => platform_option,\n70|    };\n71|    const cef_dir = cef_dir_override orelse defaultCefDir(selected_platform, resolved_web_engine.cef_dir);\n72|    if (selected_platform == .macos and target.result.os.tag != .macos) {\n73|        @panic(\"-Dplatform=macos requires a macOS target\");\n74|    }\n75|    if (selected_platform == .linux and target.result.os.tag != .linux) {\n76|        @panic(\"-Dplatform=linux requires a Linux target\");\n77|    }\n78|    if (selected_platform == .windows and target.result.os.tag != .windows) {\n79|        @panic(\"-Dplatform=windows requires a Windows target\");\n80|    }\n81|    if (web_engine == .chromium and selected_platform == .null) {\n82|        @panic(\"-Dweb-engine=chromium requires -Dplatform=macos, linux, or windows\");\n83|    }\n84|\n85|    const geometry_mod = module(b, target, optimize, \"src/primitives/geometry/root.zig\");\n86|    const assets_mod = module(b, target, optimize, \"src/primitives/assets/root.zig\");\n87|    const app_dirs_mod = module(b, target, optimize, \"src/primitives/app_dirs/root.zig\");\n88|    const trace_mod = module(b, target, optimize, \"src/primitives/trace/root.zig\");\n89|    const app_manifest_mod = module(b, target, optimize, \"src/primitives/app_manifest/root.zig\");\n90|    const diagnostics_mod = module(b, target, optimize, \"src/primitives/diagnostics/root.zig\");\n91|    const platform_info_mod = module(b, target, optimize, \"src/primitives/platform_info/root.zig\");\n92|    const json_mod = module(b, target, optimize, \"src/primitives/json/root.zig\");\n93|    const debug_mod = module(b, target, optimize, \"src/debug/root.zig\");\n94|    debug_mod.addImport(\"app_dirs\", app_dirs_mod);\n95|    debug_mod.addImport(\"trace\", trace_mod);\n96|\n97|    const geometry_tests = testArtifact(b, geometry_mod);\n98|    const assets_tests = testArtifact(b, assets_mod);\n99|    const app_dirs_tests = testArtifact(b, app_dirs_mod);\n100|    const trace_tests = testArtifact(b, trace_mod);\n101|    const app_manifest_tests = testArtifact(b, app_manifest_mod);\n102|    const diagnostics_tests = testArtifact(b, diagnostics_mod);\n103|    const platform_info_tests = testArtifact(b, platform_info_mod);\n104|    const json_tests = testArtifact(b, json_mod);\n105|\n106|    const desktop_mod = module(b, target, optimize, \"src/root.zig\");\n107|    desktop_mod.addImport(\"geometry\", geometry_mod);\n108|    desktop_mod.addImport(\"app_dirs\", app_dirs_mod);\n109|    desktop_mod.addImport(\"assets\", assets_mod);\n110|    desktop_mod.addImport(\"trace\", trace_mod);\n111|    desktop_mod.addImport(\"app_manifest\", app_manifest_mod);\n112|    desktop_mod.addImport(\"diagnostics\", diagnostics_mod);\n113|    desktop_mod.addImport(\"platform_info\", platform_info_mod);\n114|    desktop_mod.addImport(\"json\", json_mod);\n115|    desktop_mod.export_symbol_names = &.{\n116|        \"zero_native_app_create\",\n117|        \"zero_native_app_destroy\",\n118|        \"zero_native_app_start\",\n119|        \"zero_native_app_stop\",\n120|        \"zero_native_app_resize\",\n121|", "total_lines": 607, "file_size": 34637, "truncated": true, "hint": "Use offset=121 to continue reading (showing 1-120 of 607 lines)", "is_binary": false, "is_image": false} read_file 


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c0:
  {"output": "b48c171 Merge pull request #40 from vercel-labs/ctate/v0.2.0\n1b0330c Expand 0.2.0 changelog\n5b86c81 Prepare 0.2.0 release\n058a4ad Merge pull request #39 from vercel-labs/ctate/fix-missing-x\nb57efc9 Fix macOS package executable permissions\n78b5682 Merge pull request #38 from vercel-labs/ctate/issue-36-ai-skill\n5b5482d Add zero-native skills command\n25ef7ac Adopt CLI-served skills layout\n16b9b77 Add zero-native app builder skill\ne05e188 Merge pull request #28 from vercel-labs/ctate/multi-webview\n6e5e37b Fix layered WebView blocker follow-ups\n618c10e Fix layered WebView backend blockers\n93bee55 Fix browser resize and reload handling\n838fe8e Fix layered WebView hover coverage\n84ac8fd fix double entries\nf3eb4a6 Fix AppKit menu WebView targeting\nbfd76e0 Fix NullPlatform WebView compaction\n204c45e Fix layered WebView blocker follow-ups\n361830b Fix layered WebView blocker issues\n621aac7 Fix layered WebView review issues", "exit_code": 0, "error": null} terminal 


c0:
  ## Análise do repositório vercel-labs/zero-native

  ### Problema que resolve
  Permite criar apps desktop e mobile usando Zig + interface web, funcionando como uma “camada nativa” para apps de frontend moderno.
  - Atua como shell que carrega WebView e expõe ponte com código nativo.
  - Torna apps compactos, com binários pequenos, memória reduzida e reconstrução rápida.

  ### Tema principal
  Framework/shell de aplicativo nativo Zig para frontends web modernos.
  - Conceitos principais: `App`, `Runtime`, `WebViewSource`, `app.zon`,
  - API `window.zero.invoke()` JS→Zig
  - Separação clara entre web e nativo, com segurança explícita.

  ### Razão do hype/tendência
  - Zig estamos sob crescente adoção por desempenho e compilação rápida.
  - Apoio do Vercel/Labs traz credibilidade e visibilidade.
  - Estratégia dupla web engine: WebView do sistema (leve) ou CEF/Chromium (rendering consistente).
  - Cobre desktop (macOS, Linux, Windows) e mobile (iOS, Android).
  - Foco em developer experience: CLI no npm, exemplos multi-framework, mobile embedding C ABI.
  - Ênfase em segurança: WebView trata-se como não-confiável, permissões e políticas explícitas.

  ### Tecnologias envolvidas
  - Linguagem: Zig 0.16+
  - Web engines:
    - Sistema: WKWebView (macOS), WebKitGTK (Linux), WebView2 (Windows)
    - CEF/Chromium
  - Integrações nativas: Objective-C/C++/C
    - macOS: AppKit, CEF, Automation host
    - Linux: GTK4
    - Windows: WebView2, CEF
  - Package/CLI: Node.js/npm (`zero-native`)
  - Frontend: exemplo para Next.js, React, Svelte, Vue
  - Mobile: C ABI (`libzero-native.a`) para host iOS/Android
  - Schema/app config: `app.zon`
  - Build/CI: Zig (`build.zig`), GitHub Actions e processo manual de release
  - Documentação: MDX no site `zero-native.dev`  


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c0:
  {"content": "1|# SkillOpt: Executive Strategy for Self-Evolving Agent Skills\n2|\n3|*Train agent skills like you train neural networks — with epochs, (mini-)batchsize, learning rates, and validation gates — but without touching model weights.*\n4|\n5|[![Project Page](https://img.shields.io/badge/Project%20Page-SkillOpt-8dbb3c)](https://microsoft.github.io/SkillOpt/) [![Paper](https://img.shields.io/badge/Paper-arXiv-b31b1b)](https://arxiv.org/abs/2605.23904) [![Project Video](https://img.shields.io/badge/Project%20Video-Watch%20Demo-ff0000)](https://youtu.be/JUBMDTCiM0M) [![Python 3.10+](https://img.shields.io/badge/Python-3.10%2B-blue.svg)](https://www.python.org/) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)\n6|\n7|## 🎬 SkillOpt Demo Video\n8|\n9|https://github.com/user-attachments/assets/eb12d3bc-371c-467f-904d-91b61f339ed7\n10|\n11|<p align=\"center\">\n12|  <a href=\"https://youtu.be/JUBMDTCiM0M\"><b>▶ Watch the full demo on YouTube</b></a>\n13|</p>\n14|\n15|---\n16|\n17|## Install\n18|\n19|**Requirements:** Python 3.10+\n20|\n21|```bash\n22|git clone https://github.com/microsoft/SkillOpt.git\n23|cd SkillOpt\n24|pip install -e .\n25|\n26|# For ALFWorld benchmark (optional):\n27|pip install -e \".[alfworld]\"\n28|alfworld-download\n29|```\n30|\n31|### Configure API Credentials\n32|\n33|```bash\n34|cp .env.example .env\n35|# Edit .env with your API credentials, then:\n36|source .env\n37|```\n38|\n39|**Azure OpenAI** (recommended):\n40|```bash\n41|export AZURE_OPENAI_ENDPOINT=\"https://your-resource.openai.azure.com/\"\n42|# Option 1: API key auth\n43|export AZURE_OPENAI_API_KEY=\"your-key\"\n44|# Option 2: Azure CLI auth (no API key needed)\n45|export AZURE_OPENAI_AUTH_MODE=\"azure_cli\"\n46|```\n47|\n48|> **Note:** `AZURE_OPENAI_ENDPOINT` is required for all three modes (`api_key`, `azure_cli`,\n49|> `openai_compatible`). Without it, all LLM calls will fail.\n50|\n51|**OpenAI-compatible endpoints**:\n52|```bash\n53|export AZURE_OPENAI_ENDPOINT=\"https://api.openai.com/v1\"\n54|export AZURE_OPENAI_API_KEY=\"sk-...\"\n55|export AZURE_OPENAI_AUTH_MODE=\"openai_compatible\"\n56|```\n57|\n58|This routes all calls through the plain OpenAI Python client (no Azure auth, no `api-version`\n59|header).\n60|\n61|> **Note:** SkillOpt reuses the `AZURE_OPENAI_*` env var names even in this mode — there is no\n62|> separate `OPENAI_API_KEY` knob.\n63|\n64|**Anthropic Claude**:\n65|```bash\n66|export ANTHROPIC_API_KEY=\"sk-ant-...\"\n67|```\n68|\n69|**Qwen (local vLLM)**:\n70|```bash\n71|export QWEN_CHAT_BASE_URL=\"http://localhost:8000/v1\"\n72|export QWEN_CHAT_MODEL=\"Qwen/Qwen3.5-4B\"\n73|```\n74|\n75|---\n76|\n77|## Data Preparation\n78|\n79|SkillOpt expects data in a **split directory** with `train/`, `val/`, `test/` subdirectories, each containing a JSON file (e.g., `items.json`).\n80|\n81|```\n82|data/my_split/\n83|├── train/items.json\n84|├── val/items.json\n85|└── test/items.json\n86|```\n87|\n88|Each JSON file is an array of task items. The required fields depend on the benchmark. For example, SearchQA items look like:\n89|\n90|```json\n91|[\n92|  {\n93|    \"id\": \"unique_item_id\",\n94|    \"question\": \"Who wrote the novel ...\",\n95|    \"context\": \"[DOC] relevant passage text ...\",\n96|    \"answers\": [\"expected answer\"]\n97|  }\n98|]\n99|```\n100|\n101|See `skillopt/envs/<benchmark>/dataloader.py` for the exact format each benchmark expects.\n102|\n103|> **Note:** Benchmark datasets are not included in this repository. Prepare your own data following the format above.\n104|\n105|### Supported Benchmarks\n106|\n107|| Benchmark | Type | Config |\n108||---|---|---|\n109|| SearchQA | QA | `configs/searchqa/default.yaml` |\n110|| ALFWorld | Embodied agent | `configs/alfworld/default.yaml` |\n111|| DocVQA | Document QA | `configs/docvqa/default.yaml` |\n112|| LiveMathematicianBench | Math | `configs/livemathematicianbench/default.yaml` |\n113|| SpreadsheetBench | Code generation | `configs/spreadsheetbench/default.yaml` |\n114|| OfficeQA | Tool-augmented QA | `configs/officeqa/default.yaml` |\n115|\n116|---\n117|\n118|## Quick Start\n119|\n120|### Training\n121|\n122|```bash\n123|# Minimal example — train on SearchQA:\n124|python scripts/train.py \\\n125|    --config configs/searchqa/default.yaml \\\n126|    --split_dir /path/to/your/searchqa_split \\\n127|    --azure_openai_endpoint https://your-resource.openai.azure.com/ \\\n128|    --optimizer_model gpt-5.5 \\\n129|    --target_model gpt-5.5\n130|\n131|# Train on LiveMathematicianBench:\n132|python scripts/train.py \\\n133|    --config configs/livemathematicianbench/default.yaml \\\n134|    --split_dir /path/to/your/livemath_split \\\n135|    --azure_openai_endpoint https://your-resource.openai.azure.com/ \\\n136|    --optimizer_model gpt-5.5 \\\n137|    --target_model gpt-5.5\n138|\n139|# Train on ALFWorld:\n140|python scripts/train.py \\\n141|    --config configs/alfworld/default.yaml \\\n142|    --split_dir /path/to/your/alfworld_split \\\n143|    --azure_openai_endpoint https://your-resource.openai.azure.com/ \\\n144|    --optimizer_model gpt-5.5 \\\n145|    --target_model gpt-5.5\n146|```\n147|\n148|Key CLI arguments:\n149|\n150|| Argument | Description | Example |\n151||---|---|---|\n152|| `--config` | Benchmark config YAML | `configs/searchqa/default.yaml` |\n153|| `--split_dir` | Path to data split directory | `/path/to/split` |\n154|| `--azure_openai_endpoint` | Azure OpenAI endpoint URL | `https://your-resource.openai.azure.com/` |\n155|| `--optimizer_model` | Optimizer model deployment name | `gpt-5.5` |\n156|| `--target_model` | Target model deployment name | `gpt-5.5` |\n157|| `--num_epochs` | Number of training epochs | `4` |\n158|| `--batch_size` | Batch size per step | `40` |\n159|| `--workers` | Parallel rollout workers | `8` |\n160|| `--out_root` | Output directory | `outputs/my_run` |\n161|\n162|### Eval Only\n163|\n164|Evaluate a trained skill on specific data splits without training:\n165|\n166|```bash\n167|# Evaluate the packaged GPT-5.5 SearchQA skill on the test split:\n168|python scripts/eval_only.py \\\n169|  --config configs/searchqa/default.yaml \\\n170|  --skill ckpt/searchqa/gpt5.5_skill.md \\\n171|  --split valid_unseen \\\n172|  --split_dir /path/to/searchqa_split \\\n173|  --azure_openai_endpoint https://your-resource.openai.azure.com/\n174|\n175|# Evaluate on all splits (train + val + test):\n176|python scripts/eval_only.py \\\n177|  --config configs/searchqa/default.yaml \\\n178|  --skill ckpt/searchqa/gpt5.5_skill.md \\\n179|  --split all \\\n180|  --split_dir /path/to/searchqa_split \\\n181|  --azure_openai_endpoint https://your-resource.openai.azure.com/\n182|```\n183|\n184|To evaluate a skill produced by a training run, replace `--skill` with that\n185|run's best-skill path, for example `outputs/my_run/best_skill.md`.\n186|\n187|| Split | Description |\n188||---|---|\n189|| `valid_unseen` | Test set |\n190|| `valid_seen` | Validation set |\n191|| `train` | Training set |\n192|| `all` | All splits combined (default) |\n193|\n194|### Output Structure\n195|\n196|Each run writes to a structured output directory:\n197|\n198|```\n199|outputs/<run_name>/\n200|├── config.json              # Flattened runtime config\n201|├── history.json             # Per-step training history\n202|├── runtime_state.json       # Resume checkpoint\n203|├── best_skill.md            # Best validated skill document\n204|├── skills/skill_vXXXX.md   # Skill snapshot per step\n205|├── steps/step_XXXX/        # Per-step artifacts (patches, evals)\n206|├── slow_update/epoch_XX/   # Slow update logs\n207|└── meta_skill/epoch_XX/    # Meta skill logs\n208|```\n209|\n210|Re-running the same command auto-resumes from the last completed step.\n211|\n212|---\n213|\n214|## Community-contributed configs\n215|\n216|These are **not** default SkillOpt settings — they are reference configs\n217|contributed by users for specific scenarios. The paper-reported numbers\n218|were obtained with the default settings, not these.\n219|\n220|- **`configs/examples/soft_gate.yaml`** *(PR #25, contributed by\n221|  [@lvbaocheng](https://github.com/lvbaocheng))* — switches the\n222|  validation gate from exact-match (`hard`) to soft / partial-credit\n223|  (`soft` or `mixed`). Useful when the held-out **selection split is\n224|  small** (e.g. ≤ ~10 items) and the **reward is continuous**, where the\n225|  discrete hard gate often rejects every candidate and training stalls.\n226|  See the comment at the top of the file for details and when not to use\n227|  it.\n228|\n229|---\n230|\n231|## WebUI\n232|\n233|Launch the monitoring dashboard (optional):\n234|\n235|```bash\n236|pip install -e \".[webui]\"\n237|python -m skillopt_webui.app\n238|```\n239|\n240|| Flag | Default | Description |\n241||---|---|---|\n242|| `--port` | 7860 | Server port |\n243|| `--host` | `0.0.0.0` | Bind address |\n244|| `--share` | off | Create a public Gradio share link |\n245|\n246|```bash\n247|# With public share link (useful for remote servers)\n248|python -m skillopt_webui.app --share\n249|```\n250|\n251|---\n252|\n253|## Citation\n254|\n255|```bibtex\n256|@misc{yang2026skilloptexecutivestrategyselfevolving,\n257|      title={SkillOpt: Executive Strategy for Self-Evolving Agent Skills}, \n258|      author={Yifan Yang and Ziyang Gong and Weiquan Huang and Qihao Yang and Ziwei Zhou and Zisu Huang and Yan Li and Xuemei Gao and Qi Dai and Bei Liu and Kai Qiu and Yuqing Yang and Dongdong Chen and Xue Yang and Chong Luo},\n259|      year={2026},\n260|      eprint={2605.23904},\n261|      archivePrefix={arXiv},\n262|      primaryClass={cs.AI},\n263|      url={https://arxiv.org/abs/2605.23904}\n264|}\n265|```\n266|", "total_lines": 265, "file_size": 8526, "truncated": false, "is_binary": false, "is_image": false} read_file 


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c0:
  {"content": "1|[build-system]\n2|requires = [\"setuptools>=68.0\", \"wheel\"]\n3|build-backend = \"setuptools.build_meta\"\n4|\n5|[project]\n6|name = \"skillopt\"\n7|version = \"0.1.0\"\n8|description = \"SkillOpt: Agentic Skill Optimization via Reflective Training Loops\"\n9|readme = \"README.md\"\n10|license = {text = \"MIT\"}\n11|requires-python = \">=3.10\"\n12|authors = [\n13|    {name = \"SkillOpt Team\"},\n14|]\n15|keywords = [\"agent\", \"prompt-optimization\", \"skill-learning\", \"LLM\", \"agentic\"]\n16|classifiers = [\n17|    \"Development Status :: 3 - Alpha\",\n18|    \"Intended Audience :: Science/Research\",\n19|    \"License :: OSI Approved :: MIT License\",\n20|    \"Programming Language :: Python :: 3\",\n21|    \"Programming Language :: Python :: 3.10\",\n22|    \"Programming Language :: Python :: 3.11\",\n23|    \"Programming Language :: Python :: 3.12\",\n24|    \"Topic :: Scientific/Engineering :: Artificial Intelligence\",\n25|]\n26|dependencies = [\n27|    \"openai>=1.30.0\",\n28|    \"pyyaml>=6.0\",\n29|    \"numpy>=1.24.0\",\n30|    \"openpyxl>=3.1.0\",\n31|    \"azure-identity>=1.15.0\",\n32|    \"azure-core>=1.30.0\",\n33|    \"httpx>=0.27.0\",\n34|]\n35|\n36|[project.optional-dependencies]\n37|# Benchmark-specific dependencies\n38|alfworld = [\"alfworld>=0.4.0\", \"gymnasium>=0.29.0\"]\n39|# Claude model backend\n40|claude = [\"claude-agent-sdk>=0.1.0\"]\n41|# Qwen local model backend (via vLLM)\n42|qwen = [\"vllm>=0.4.0\"]\n43|# Documentation site\n44|docs = [\"mkdocs-material>=9.5.0\", \"mkdocstrings[python]>=0.24.0\"]\n45|# WebUI dashboard\n46|webui = [\"gradio>=4.0.0\"]\n47|# Development tools\n48|dev = [\"ruff>=0.4.0\", \"pytest>=8.0.0\"]\n49|# All optional dependencies (except docs/dev/webui)\n50|all = [\n51|    \"alfworld>=0.4.0\",\n52|    \"gymnasium>=0.29.0\",\n53|    \"claude-agent-sdk>=0.1.0\",\n54|]\n55|\n56|[project.scripts]\n57|skillopt-train = \"scripts.train:main\"\n58|skillopt-eval = \"scripts.eval_only:main\"\n59|\n60|[project.urls]\n61|Homepage = \"https://github.com/microsoft/SkillOpt\"\n62|Documentation = \"https://microsoft.github.io/SkillOpt\"\n63|Repository = \"https://github.com/microsoft/SkillOpt\"\n64|Issues = \"https://github.com/microsoft/SkillOpt/issues\"\n65|\n66|[tool.setuptools.packages.find]\n67|include = [\"skillopt*\", \"scripts*\"]\n68|\n69|[tool.ruff]\n70|line-length = 120\n71|target-version = \"py310\"\n72|\n73|[tool.ruff.lint]\n74|select = [\"E\", \"F\", \"I\", \"W\"]\n75|ignore = [\"E501\"]\n76|", "total_lines": 75, "file_size": 2090, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  ## Análise do repositório `microsoft/SkillOpt`

  ### Procedimento
  1. Clonei o repositório para `/home/ice/SkillOpt`.
  2. Li `README.md` e `pyproject.toml` para identificar o problema, tema, hype e tecnologias.

  ### Problema
   Como otimizar habilidades reutilizáveis em linguagem natural (prompts/instruções, os "skills") para agentes LLM **congelados**, sem necessidade de ajustar pesos ou retreinar o modelo base.

  ### Tema principal
  **Otimização de skills/agentes em espaço textual**, enquadrada como uma “treino” em texto-space com epochs, batch size, taxa de aprendizado e validação, gerando artefatos deployáveis como `best_skill.md`.

  ### Razões do hype
  - **Framing inovador**: tratar o aprendizado de prompt como se fosse treinamento de rede neural, mas sem mexer nos pesos do LLM.
  - **Produto Microsoft** com paper no arXiv (2605.23904) e página oficial.
  - Suporte a **múltiplos benchmarks** (SearchQA, ALFWorld, DocVQA, LiveMathematicianBench, SpreadsheetBench, OfficeQA).
  - Compatível com **múltiplos backends** (Azure OpenAI, OpenAI-compatible, Anthropic Claude, Qwen/vLLM).
  - Artefatos **reutilizáveis e deployáveis** (`best_skill.md`).
  - **Validação-gated updates** (só atualiza se houver melhoria real).
  - **WebUI opcional** (Gradio) para monitoramento da evolução dos skills.
  - Alta popularidade: ~3.2k estrelas.

  ### Tecnologias
  - **Linguagem**: Python 3.10+
  - **Dependências core**: `openai >=1.30.0`, `pyyaml`, `numpy`, `openpyxl`, `azure-identity`, `azure-core`, `httpx`
  - **Opcionais**: `alfworld`/`gymnasium`, `claude-agent-sdk`, `vllm` (Qwen), `gradio` (WebUI), `mkdocs-material` (docs), `ruff` e `pytest` (dev)
  - **CLI**: `skillopt-train` e `skillopt-eval`
  - **Arquitetura**: configs YAML por benchmark, saída estruturada em `outputs/` (history, skills, formatos `.md`)
  - **Licença**: MIT

  Em resumo: é uma ferramenta de prompt optimization “treinável” para agentes LLM, com loop de validação e múltiplos suportes, criada pela Microsoft e alinhada à tendência de agentic AI / reusable skills.  


c0:
  Vou clonar e analisar o repositório FoundZiGu/GuJumpgate.  [{"id": "chatcmpl-tool-98121b727469178e", "call_id": "chatcmpl-tool-98121b727469178e", "response_item_id": "fc_chatcmpl-tool-98121b727469178e", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice && git clone https://github.com/FoundZiGu/GuJumpgate.git\", \"timeout\": 120}"}}]


c0:
    [{"id": "chatcmpl-tool-8ae12d6e9187cc9e", "call_id": "chatcmpl-tool-8ae12d6e9187cc9e", "response_item_id": "fc_chatcmpl-tool-8ae12d6e9187cc9e", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice/GuJumpgate && ls -la\"}"}}, {"id": "chatcmpl-tool-a83831d45774d5d0", "call_id": "chatcmpl-tool-a83831d45774d5d0", "response_item_id": "fc_chatcmpl-tool-a83831d45774d5d0", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\": \"/home/ice/GuJumpgate\", \"pattern\": \"*\", \"target\": \"files\"}"}}]


c0:
  {"output": "total 556\ndrwxrwxr-x 14 ice ice     48 May 30 22:53 .\ndrwx------ 21 ice ice     26 May 30 22:53 ..\ndrwxrwxr-x  8 ice ice     13 May 30 22:53 .git\n-rw-rw-r--  1 ice ice     59 May 30 22:53 .gitattributes\n-rw-rw-r--  1 ice ice    295 May 30 22:53 .gitignore\n-rw-rw-r--  1 ice ice    787 May 30 22:53 CONTRIBUTING.md\n-rw-rw-r--  1 ice ice   3370 May 30 22:53 GuJumpgate-mihomo-auto-routing-prompt.md\n-rw-rw-r--  1 ice ice   1113 May 30 22:53 LICENSE\n-rw-rw-r--  1 ice ice   6294 May 30 22:53 README.md\n-rw-rw-r--  1 ice ice   1895 May 30 22:53 RELEASING.md\n-rw-rw-r--  1 ice ice   4096 May 30 22:53 Release.md\n-rw-rw-r--  1 ice ice    567 May 30 22:53 SECURITY.md\n-rw-rw-r--  1 ice ice   1877 May 30 22:53 THIRD_PARTY_NOTICES.md\n-rw-rw-r--  1 ice ice   2729 May 30 22:53 ZeroOmegaOptions.bak\ndrwxrwxr-x  3 ice ice     30 May 30 22:53 background\n-rw-rw-r--  1 ice ice 672884 May 30 22:53 background.js\n-rw-rw-r--  1 ice ice  15122 May 30 22:53 cloudflare-temp-email-utils.js\n-rw-rw-r--  1 ice ice   6703 May 30 22:53 cloudmail-utils.js\ndrwxrwxr-x  2 ice ice     22 May 30 22:53 content\ndrwxrwxr-x  2 ice ice      5 May 30 22:53 data\ndrwxrwxr-x  5 ice ice     16 May 30 22:53 docs\ndrwxrwxr-x  3 ice ice      3 May 30 22:53 flows\n-rw-rw-r--  1 ice ice   5370 May 30 22:53 freemail-utils.js\n-rw-rw-r--  1 ice ice  15658 May 30 22:53 gopay-utils.js\n-rw-rw-r--  1 ice ice  21024 May 30 22:53 hotmail-utils.js\n-rw-rw-r--  1 ice ice   6701 May 30 22:53 icloud-utils.js\ndrwxrwxr-x  2 ice ice      5 May 30 22:53 icons\n-rw-rw-r--  1 ice ice  16408 May 30 22:53 luckmail-utils.js\n-rw-rw-r--  1 ice ice   6753 May 30 22:53 mail-provider-utils.js\n-rw-rw-r--  1 ice ice   5797 May 30 22:53 mail2925-utils.js\n-rw-rw-r--  1 ice ice   6309 May 30 22:53 managed-alias-utils.js\n-rw-rw-r--  1 ice ice   3754 May 30 22:53 manifest.json\n-rw-rw-r--  1 ice ice  18190 May 30 22:53 microsoft-email.js\n-rw-rw-r--  1 ice ice   9690 May 30 22:53 outlook-email-plus-utils.js\n-rw-rw-r--  1 ice ice     66 May 30 22:53 package.json\n-rw-rw-r--  1 ice ice   2000 May 30 22:53 paypal-utils.js\ndrwxrwxr-x  3 ice ice      3 May 30 22:53 phone-sms\n-rw-rw-r--  1 ice ice   1130 May 30 22:53 rules.json\ndrwxrwxr-x  2 ice ice      4 May 30 22:53 scripts\ndrwxrwxr-x  3 ice ice      3 May 30 22:53 services\ndrwxrwxr-x  2 ice ice      5 May 30 22:53 shared\ndrwxrwxr-x  2 ice ice     21 May 30 22:53 sidepanel\n-rw-rw-r--  1 ice ice   5311 May 30 22:53 start-hotmail-helper.bat\n-rw-rw-r--  1 ice ice    385 May 30 22:53 start-hotmail-helper.command\n-rw-rw-r--  1 ice ice  13897 May 30 22:53 开发者AI开发与PR提交流程.md\n-rw-rw-r--  1 ice ice  75441 May 30 22:53 项目完整链路说明.md\n-rw-rw-r--  1 ice ice  29590 May 30 22:53 项目开发规范（AI协作）.md\n-rw-rw-r--  1 ice ice  52243 May 30 22:53 项目文件结构说明.md", "exit_code": 0, "error": null} terminal 


c0:
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c0:
    [{"id": "chatcmpl-tool-b70c425e8d141cc5", "call_id": "chatcmpl-tool-b70c425e8d141cc5", "response_item_id": "fc_chatcmpl-tool-b70c425e8d141cc5", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/GuJumpgate/README.md\"}"}}, {"id": "chatcmpl-tool-bbae14d239820472", "call_id": "chatcmpl-tool-bbae14d239820472", "response_item_id": "fc_chatcmpl-tool-bbae14d239820472", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/GuJumpgate/manifest.json\"}"}}, {"id": "chatcmpl-tool-a7bfc734254d2a54", "call_id": "chatcmpl-tool-a7bfc734254d2a54", "response_item_id": "fc_chatcmpl-tool-a7bfc734254d2a54", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/home/ice/GuJumpgate/package.json\"}"}}]


c0:
  {"content": "1|# GuJumpgate\n2|\n3|一个也许能“真正解放双手”的全自动 GPT Plus 注册浏览器扩展。\n4|\n5|如果这个项目能帮上你，欢迎点个 Star⭐~\n6|\n7|> [!IMPORTANT]\n8|> 目前 PAYPAY 无卡 日区PP可完整跑通。\n9|> 美区已死，待复活 ;w;\n10|>\n11|\n12|## 已实现能力\n13|\n14|1. **自动注册 Free 账号**\n15|\n16|   借助 FlowPilot 项目实现 Free 账号的自动注册。\n17|\n18|2. **PayPal 激活 Plus 全流程**\n19|\n20|   - 自动跳转 Stripe 长链接\n21|   - 自动填写 Stripe 账单并跳转 PayPal\n22|   - 自动填写 PayPal 账单并完成流程\n23|\n24|3. **Hotmail / Outlook 自动别名功能**\n25|\n26|4. **PayPal 号码池管理**\n27|\n28|5. **自动 OAuth 回调到本地及各面板**\n29|\n30|   对 FlowPilot 原有回调流程做了调整和适配。\n31|\n32|6. **支持跳过 OAuth**\n33|\n34|   忽略 RT，生成只有 AT 的 JSON 文件到本地。\n35|\n36|## 前提要求\n37|\n38|1. 1 个带 API、且能连续正常接收 PayPal 验证码的 JP +81 接码手机号\n39|2. 1 个或 N 个支持 `IMAP` 和 `Graph` 的 Outlook 邮箱 或者 自建 Cloudflare Temp Email edu 前缀，如 @edu.openai.comedu.openai.com 这样才有试用资格\n40|   （推荐使用outlook，实测存活率高于自建域名邮箱，也可能是我杂米的原因）\n41|3. 1 个相对干净、支持 PayPal 注册的 JP 代理 (干净就不会跳 PAYPAL 的注册滑块，账单页面的 Captcha 扩展已经设置了自动屏蔽)\n42|4. 1 个充值好的SMS平台，开通 APIKEY 用于Oauth接码\n43|   （具体选择SMS平台的选择，你可以参考我的对比站：https://sms.fur.li/  ）\n44|\n45|> [!NOTE]\n46|> 自建 Cloudflare Temp Email / Cloud Mail 需要使用 `edu` 前缀，例如 `edu.openai.com`，才有试用资格。\n47|>\n48|> PayPal 注册代理越干净，越不容易触发 PayPal 注册滑块。账单页面的 Captcha 扩展已经实现了自动屏蔽。\n49|\n50|## 测试环境\n51|\n52|- 成功率：连续 10 次串行运行，注册并激活 Plus 100% 成功率\n53|- 浏览器：Chrome `148.0.7778.168`（64 位正式版），开启无痕模式\n54|- 网络环境： JP 自建代理\n55|\n56|过程中遇到任何卡死的问题，都可以先停止，然后点击流程的各个节点进行重试，也可以点击旁边选择跳过。\n57|\n58|## 安装与使用\n59|\n60|先到本仓库的 [Releases](https://github.com/FoundZiGu/GuJumpgate/releases) 页面下载扩展压缩包并解压。\n61|\n62|### 1. 打开扩展开发者模式\n63|\n64|打开 `chrome://extensions/`，开启开发者模式。\n65|\n66|![打开 Chrome 扩展开发者模式](docs/images/github-readme-1779190547983.png)\n67|\n68|### 2. 加载扩展目录\n69|\n70|选择“加载已解压的扩展程序”，然后选择刚才解压出的文件夹。\n71|\n72|![加载未打包的扩展程序](docs/images/readme-load-extension.png)\n73|\n74|### 3. 启用无痕权限\n75|\n76|在扩展详情页中勾选“在无痕模式下启用”。\n77|\n78|![启用扩展的无痕模式权限](docs/images/readme-incognito-permission.png)\n79|\n80|### 4. 配置代理\n81|\n82|现在推荐且能走的路径是 **全程JP代理**\n83|这条路径还是可以稳定出试用和正常激活PLUS的\n84|\n85|#### 方案一：直接使用 (关闭云端转换) -推荐\n86|\n87|直接开启代理工具的规则/全局 JP代理，即可开始使用\n88|\n89|#### 方案而：使用云端转换 (公益)\n90|\n91|直接开启代理工具的规则/全局 JP代理，选择云端转换，即可开始使用\n92|\n93|#### 方案三：本地配置代理\n94|\n95|配置本地用于支付转换的代理，出口必须是 JP 代理。\n96|\n97|![配置支付转换代理](docs/images/readme-payment-proxy.png)\n98|\n99|然后代理工具开启全局 JP，或者配置好相应规则分流至 JP。\n100|\n101|### 5. 启动 Hotmail Helper\n102|\n103|请注意：本地 JSON 生成导出依赖本地助手。无论你是否使用 Hotmail / Outlook 邮箱，都请启动。\n104|\n105|运行解压目录内的脚本：\n106|\n107|- Windows：`start-hotmail-helper.bat`\n108|- macOS：`start-hotmail-helper.command`\n109|\n110|![运行 start-hotmail-helper 脚本](docs/images/github-readme-1779193024860.png)\n111|\n112|### 6. 配置扩展参数\n113|\n114|在扩展中打开侧边栏，按你的环境配置参数。\n115|\n116|#### 选择最终 JSON 导出到的平台\n117|\n118|账号接入策略建议选择：`手机号注册/绑定 Oauth`。\n119|\n120|![选择最终 JSON 导出平台](docs/images/README-1780155605371.png)\n121|\n122|> [!WARNING]\n123|> OAuth 目前严重风控，要求绑定手机号，仅推荐使用 `手机号注册/绑定 Oauth`。\n124|\n125|#### 模式选择\n126|\n127|请保持默认或手动选择日区PP Plus Checkout\n128|\n129|![选择账号接入策略](docs/images/README-1780156040069.png)\n130|\n131|#### 验证码接口\n132|\n133|填写可直接 `GET` 请求的 `http` / `https` 地址。\n134|\n135|![填写验证码接口](docs/images/readme-verification-url.png)\n136|\n137|#### PAYPAL 接码电话\n138|\n139|填写 PayPal 接码电话，注意按扩展提示填写格式。\n140|\n141|![填写 PAYPAL 接码电话](docs/images/readme-paypal-phone.png)\n142|\n143|#### 邮箱渠道\n144|\n145|选择对应的邮箱渠道。自建邮箱需使用 `edu` 前缀获得试用资格。\n146|\n147|![选择邮箱渠道](docs/images/readme-mail-provider.png)\n148|\n149|然后填写或导入各自邮箱渠道所需的配置。\n150|\n151|#### 接码设置\n152|\n153|![导入邮箱渠道配置](docs/images/README-1780155776707.png)\n154|\n155|- 选择接码服务商\n156|- 配置服务商顺序\n157|- 配置国家优先级\n158|- 配置接码 API\n159|- 配置价格上限\n160|- 其他默认即可\n161|\n162|### 7. 开始运行\n163|\n164|保存配置后即可开始运行。\n165|\n166|![开始运行扩展流程](docs/images/github-readme-1779194981001.png)\n167|\n168|## 版权与来源说明\n169|\n170|本项目基于开源项目 [QLHazyCoder/FlowPilot](https://github.com/QLHazyCoder/FlowPilot) 进行修改、移植与二次开发，其部分早期代码与 [whwh1233/StepFlow-Duck](https://github.com/whwh1233/StepFlow-Duck) 具有共同历史。\n171|\n172|原项目及其相关开源部分采用 MIT License 发布。根据 MIT License，你可以在保留原版权声明和许可声明的前提下使用、修改、分发本项目的相关代码。\n173|\n174|为避免歧义，原项目作者、历史贡献者与当前二开版本之间不存在默认的认可、担保或背书关系。本项目中新增的适配、流程调整、脚本移植与文档整理内容，除另有说明外，均由当前维护者负责。\n175|\n176|如果你分发本项目或其修改版本，请一并保留仓库中的 `LICENSE` 及相关来源说明文件。\n177|\n178|## 使用提示\n179|\n180|- 使用者应自行遵守目标平台服务条款、适用法律及其所在地区的监管要求\n181|\n182|## 友情链接\n183|\n184|- [LINUX DO - 新的理想型社区](https://linux.do/)\n185|", "total_lines": 184, "file_size": 6294, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
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c0:
  {"content": "1|# GuJumpgate Mihomo 自动分流提示词\n2|\n3|将下面这段提示词复制给你电脑上的 AI，让它自动检测本机正在使用的 mihomo 配置，并添加 GuJumpgate 美国轮询分流。\n4|\n5|````text\n6|请你在我的电脑上自动检测正在使用的 mihomo / Clash Meta / Clash Verge / Clash Verge Rev / Clash Nyanpasu / Mihomo Party / FlClash 等 mihomo 内核客户端，找到当前实际加载的 YAML 配置文件，然后修改该配置。\n7|\n8|目标：\n9|\n10|新增一个美国节点轮询策略组，命名为：\n11|\n12|GuJumpgate自动轮询\n13|\n14|并让以下域名规则放在 rules: 最前面，优先且仅走 GuJumpgate自动轮询：\n15|\n16|- paypal.com\n17|- www.paypal.com\n18|- recaptcha.net\n19|- midtrans.com\n20|- checkout.stripe.com\n21|- pay.openai.com\n22|- chatgpt.com\n23|- openai.com\n24|- auth.openai.com\n25|\n26|实现要求：\n27|\n28|1. 先自动定位当前 mihomo 实际加载的配置文件，不要随便修改无关 YAML。\n29|2. 修改前必须备份原配置，例如生成 原文件名.bak_时间戳。\n30|3. 优先用 YAML 解析/结构化方式修改，避免用简单字符串拼接破坏格式。\n31|4. 在 proxy-groups: 最前面新增策略组：\n32|\n33|```yaml\n34|- name: \"GuJumpgate自动轮询\"\n35|  type: load-balance\n36|  strategy: round-robin\n37|  include-all: true\n38|  include-all-proxies: true\n39|  include-all-providers: true\n40|  filter: '(?i)(美国|美國|西美|united[ -]?states|\\bus\\b|\\busa\\b|america|🇺🇸)'\n41|  exclude-filter: '(?i)(剩余流量|距离下次重置剩余|下次重置剩余|重置剩余|套餐到期|到期时间|流量重置|traffic|expire|expiration|subscription|subscribe|reset|plan|建议)'\n42|  url: \"https://www.gstatic.com/generate_204\"\n43|  interval: 300\n44|  lazy: false\n45|  expected-status: 204\n46|````\n47|\n48|5. 如果当前 mihomo 版本或客户端不支持 include-all / include-all-proxies / include-all-providers，则改为扫描当前所有代理和 provider 节点名称，筛出美国节点，写成显式 proxies: 列表。\n49|6. 在 rules: 最前面插入以下规则，必须排在原有 openai、paypal、stripe、geolocation-!cn、MATCH 等规则之前：\n50|\n51|```yaml\n52|- DOMAIN,www.paypal.com,GuJumpgate自动轮询\n53|- DOMAIN-SUFFIX,paypal.com,GuJumpgate自动轮询\n54|- DOMAIN-SUFFIX,recaptcha.net,GuJumpgate自动轮询\n55|- DOMAIN-SUFFIX,midtrans.com,GuJumpgate自动轮询\n56|- DOMAIN,checkout.stripe.com,GuJumpgate自动轮询\n57|- DOMAIN,pay.openai.com,GuJumpgate自动轮询\n58|- DOMAIN,chatgpt.com,GuJumpgate自动轮询\n59|- DOMAIN,openai.com,GuJumpgate自动轮询\n60|- DOMAIN,auth.openai.com,GuJumpgate自动轮询\n61|```\n62|\n63|7. 避免重复添加：如果已经存在 GuJumpgate自动轮询 或上述规则，请更新/去重，而不是重复插入。\n64|8. 修改完成后验证 YAML 可以被正常解析，并检查：\n65|   - GuJumpgate自动轮询 存在于 proxy-groups\n66|   - 上述规则位于 rules 顶部\n67|   - 规则目标策略组名称完全一致\n68|   - 至少能匹配到一个美国节点；如果匹配不到，请提示我手动检查节点命名\n69|9. 不要改动与本需求无关的代理、订阅、端口、DNS、TUN、其他规则。\n70|10. 最后告诉我修改了哪个配置文件、备份文件在哪里、插入了哪些规则、是否验证通过。\n71|\n72|参考逻辑：\n73|\n74|实现方式类似在 mihomo 配置中新增一个美国 round-robin 轮询组，并把指定域名规则优先指向该轮询组。请适配我当前电脑上的实际 mihomo 配置，不要假设固定文件路径。\n75|```\n76|", "total_lines": 75, "file_size": 3370, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|# GuJumpgate v0.1.7\n2|\n3|发布日期：2026-05-29\n4|\n5|本次版本聚焦三件事：把 PayPal Plus Checkout 从单一美区链路扩展成美区 / 日区双模式；补齐 Hosted Checkout 在资料生成、验证码读取和 PayPal 客页填充上的稳定性；同时修正 SMSPool 补码、WhatsApp 识别和 Hotmail 账号管理里的几个高频误判与状态同步问题。\n6|\n7|## 本次更新\n8|\n9|- 版本号升级到 `0.1.7`，发布版本为 `GuJumpgate V0.1.7`。\n10|- Plus Checkout 新增双模式：\n11|  - `美区PP Plus Checkout`\n12|  - `日区PP Plus Checkout`\n13|- 侧边栏新增 Plus Checkout 模式切换入口；美区 / 日区各自维护独立的 Hosted Checkout 配置与短信池状态，避免切模式后把验证码接口、号码池和使用记录串用。\n14|- Hosted Checkout 日区链路补齐：\n15|  - 支持按日区模式请求日本地址资料。\n16|  - 自动规范日本都道府县、邮编、生日、信用卡有效期和注册密码格式。\n17|  - OpenAI Hosted Checkout 表单同步适配日区地址字段与州/都道府县映射。\n18|- Hosted Checkout 资料生成与验证码解析增强：\n19|  - Hosted Checkout 随机密码生成与外部返回密码会先做合法性归一，避免超长或不合规密码直接提交失败。\n20|- 手机验证码链路增强：\n21|  - `requestAdditionalSms` 支持返回新的 activation，并在后台立即刷新当前运行态，减少补码后仍沿用旧 activation 导致的错位。\n22|  - SMSPool 在请求补发短信前会先记录历史验证码，把旧码加入忽略列表，避免补码后重复消费历史短信。\n23|- WhatsApp 识别逻辑收敛：\n24|  - 添加手机号页和验证码页会区分“纯 WhatsApp 流程”和“短信 / WhatsApp 选择器文案”。\n25|  - 对 `Send code via WhatsApp SMS`、`通过以下方式发送验证码 短信 WhatsApp` 这类混合文案不再误判成必须重开 OAuth 的 WhatsApp 页面。\n26|- Hotmail 管理器增强：\n27|  - 新增从后台主动回读最新状态的同步逻辑。\n28|  - 导入、保存、切换、校验、测试、删除后会立即刷新侧边栏数据，减少前端列表和后台真实状态不一致的问题。\n29|\n30|## 修复内容\n31|\n32|- 修复 Plus Checkout 配置只能按单一模式保存的问题，自动运行恢复时现在会保留 `plusCheckoutMode` 和对应 profile。\n33|- 修复 Hosted Checkout 切换到日区后仍可能继续读取旧的美区验证码接口、手机号或短信池的问题。\n34|- 修复 Hosted Checkout 手动取码时把示例验证码 `012345`、说明文本或无效响应误识别为真实验证码的问题。\n35|- 修复 SMSPool 补码时历史短信未被忽略、重复拿到旧验证码的问题。\n36|- 修复手机号页正文只是在展示短信 / WhatsApp 选项时，就被误判为 WhatsApp 验证链路并触发重启的问题。\n37|- 修复 Hotmail 设置保存时，空 payload 可能覆盖已有账号列表的问题。\n38|- 修复 Hotmail 账号执行导入、验证、测试、切换、删除后，侧边栏展示与后台状态不同步的问题。\n39|\n40|## 测试覆盖\n41|\n42|- 新增 / 更新 Hosted Checkout 测试，覆盖：\n43|  - 示例验证码过滤\n44|  - `SmsCode` 字段提取\n45|  - Plus Checkout 双模式 profile 解析\n46|  - 日区地址拉取与资料归一\n47|- 新增 / 更新手机号验证测试，覆盖：\n48|  - 中文 / 英文短信 + WhatsApp 混合选择器文案识别\n49|  - WhatsApp 自动重启只在真正命中 WhatsApp 页面时触发\n50|- 新增 SMSPool 测试，覆盖：\n51|  - activation 归一化时保留 resend 元数据\n52|  - 补发短信前自动记录并忽略历史验证码\n53|\n54|## 升级注意\n55|\n56|- 如果你此前已经配置过 Hosted Checkout 验证码接口或号码池，升级后建议分别检查美区 / 日区模式下的配置是否符合预期。\n57|- 日区 PP Plus Checkout 会使用独立的地址资料与表单映射；如外部地址源返回字段异常，系统会自动回退到内置默认值，但仍建议优先检查你的资料接口。\n58|- 如果步骤 9 开启了 WhatsApp 页面自动重启，现在只有真正命中 WhatsApp 验证页才会触发；短信 / WhatsApp 选择器页会继续走正常短信链路。\n59|", "total_lines": 58, "file_size": 4096, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|[\n2|  {\n3|    \"id\": 1,\n4|    \"priority\": 1,\n5|    \"action\": {\n6|      \"type\": \"modifyHeaders\",\n7|      \"requestHeaders\": [\n8|        {\n9|          \"header\": \"Origin\",\n10|          \"operation\": \"set\",\n11|          \"value\": \"https://www.icloud.com\"\n12|        },\n13|        {\n14|          \"header\": \"Referer\",\n15|          \"operation\": \"set\",\n16|          \"value\": \"https://www.icloud.com/\"\n17|        }\n18|      ]\n19|    },\n20|    \"condition\": {\n21|      \"urlFilter\": \"|https://*.icloud.com/*\",\n22|      \"resourceTypes\": [\"xmlhttprequest\"],\n23|      \"excludedInitiatorDomains\": [\"apple.com\", \"icloud.com\"]\n24|    }\n25|  },\n26|  {\n27|    \"id\": 2,\n28|    \"priority\": 1,\n29|    \"action\": {\n30|      \"type\": \"modifyHeaders\",\n31|      \"requestHeaders\": [\n32|        {\n33|          \"header\": \"Origin\",\n34|          \"operation\": \"set\",\n35|          \"value\": \"https://www.icloud.com.cn\"\n36|        },\n37|        {\n38|          \"header\": \"Referer\",\n39|          \"operation\": \"set\",\n40|          \"value\": \"https://www.icloud.com.cn/\"\n41|        }\n42|      ]\n43|    },\n44|    \"condition\": {\n45|      \"urlFilter\": \"|https://*.icloud.com.cn/*\",\n46|      \"resourceTypes\": [\"xmlhttprequest\"],\n47|      \"excludedInitiatorDomains\": [\"apple.com.cn\", \"icloud.com.cn\"]\n48|    }\n49|  }\n50|]\n51|", "total_lines": 50, "file_size": 1130, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  ## Análise do repositório FoundZiGu/GuJumpgate

  **O que foi feito:**
  - Clonei o repositório em `/home/ice/GuJumpgate`
  - Li README, manifest.json, package.json, Release.md, rules.json e documentação em chinês

  ### O que encontrei

  **Problema:**
  Automação do registro e ativação do ChatGPT Plus / GPT-Plus com método “plus via PayPal” (Plus Checkout). A barreira é que, para obter Plus, é preciso fazer OAuth com uma conta GPT Free válida e depois passar pelo fluxo de pagamento (Stripe/PayPal), que exige dados de cartão/endereço, número de telefone para verificação e, além disso, o site tem bloqueios (captcha, slider do PayPal, verificação SMS). O projeto resolve isso automatizando passo a passo dentro do navegador, eliminando trabalho manual repetitivo.

  **Tema principal:**
  Extensão para navegador Chrome (MV3) de automação de cadastro e ativação de ChatGPT Plus. Destaque: fluxo automatizado de conferência de pagamento final usando PayPal (JP e US) e Stripe, sem depender de cartão de crédito na etapa do Plus (usa a verificação PayPal).

  **Razão do hype (3.115 estrelas):**
  - Funcionalidade altamente procurada: ChatGPT Plus “sem esforço”
  - Hype construído por relatos de alta confiabilidade em ambiente JP (10/10 sucesso relatado no README)
  - Combinação “automatizada” de contas (Hotmail/Outlook, iCloud, alias) + pagamentos + bypass de bloqueios (Slider/Captcha)
  - Baseado no projeto aberto FlowPilot/StepFlow, com PRs e comunidade ativa
  - Lançamento e ciclo rápido de versões (Release notes detalhadas)

  **Tecnologias usadas:**
  - JavaScript (Vanilla) — extensão Chrome MV3
  - Chrome Extension Manifest v3 (service worker, side panel, declarativeNetRequest)
  - Service worker: `background.js`
  - Content scripts injetados em domínios chave: OpenAI, Stripe, PayPal, iCloud, mail 163/qq, DuckDuckGo
  - Painel lateral (sidepanel): HTML + CSS + JS
  - Ferramentas auxiliares: Cloudflare Temp Email, Microsoft Graph/IMAP, iCloud Mail
  - Provedores de SMS (5-Sim, GrizzlySMS, Hero SMS, NexSMS, SMSPool, SMSBower, SMS Verification Number, ChatGPTSMS)
  - Manipulação de proxy (mihomo/clash recomendado)
  - Python para serviços auxiliares (ex.: `scripts/hotmail_helper.py`, `services/checkout-converter/app.py`)
  - Semanticamente, o projeto faz scraping/automação DOM e gerenciamento de sessão, OAuth e fluxos multi-steps

  ### Arquivos relevantes
  - `/home/ice/GuJumpgate/README.md`
  - `/home/ice/GuJumpgate/manifest.json`
  - `/home/ice/GuJumpgate/Release.md`
  - `/home/ice/GuJumpgate/rules.json`
  - `/home/ice/GuJumpgate/package.json`  


c0:
    [{"id": "chatcmpl-tool-ba6c571dac0de8a4", "call_id": "chatcmpl-tool-ba6c571dac0de8a4", "response_item_id": "fc_chatcmpl-tool-ba6c571dac0de8a4", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice && git clone https://github.com/simplifaisoul/osiris.git 2>&1 | tail -5\", \"timeout\": 120}"}}]


c0:
    [{"id": "chatcmpl-tool-a4aaaadb77a81c66", "call_id": "chatcmpl-tool-a4aaaadb77a81c66", "response_item_id": "fc_chatcmpl-tool-a4aaaadb77a81c66", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"cd /home/ice/osiris && find . -maxdepth 2 -type f | sed 's|^\\\\./||' | sort | head -80\", \"timeout\": 20}"}}, {"id": "chatcmpl-tool-82ccfc33bd4613f6", "call_id": "chatcmpl-tool-82ccfc33bd4613f6", "response_item_id": "fc_chatcmpl-tool-82ccfc33bd4613f6", "type": "function", "function": {"name": "read_file", "arguments": "{\"limit\": 200, \"offset\": 1, \"path\": \"/home/ice/osiris/README.md\"}"}}]


c0:
  {"output": ".dockerignore\n.env.example\n.env.template\n.git/HEAD\n.git/config\n.git/description\n.git/index\n.git/packed-refs\n.gitignore\nAGENTS.md\nCLAUDE.md\nDOCKER.md\nDO_NOT_PUSH.md\nDockerfile\nLICENSE\nREADME.md\nSECURITY.md\nannounce_upgrade.js\ndocker-compose.yml\neslint.config.mjs\nfork.diff\ngenerate_countries.js\ngenerate_region_map.js\nito69_fork.diff\nnext.config.ts\npackage-lock.json\npackage.json\npostcss.config.mjs\npublic/android-chrome-192x192.png\npublic/android-chrome-512x512.png\npublic/apple-touch-icon.png\npublic/casaos-icon.png\npublic/favicon-16x16.png\npublic/favicon-32.svg\npublic/favicon-32x32.png\npublic/favicon.ico\npublic/favicon.svg\npublic/favicon_io.zip\npublic/file.svg\npublic/globe.svg\npublic/icon-192.png\npublic/manifest.json\npublic/next.svg\npublic/og-image.png\npublic/osiris-icon.png\npublic/robots.txt\npublic/site.webmanifest\npublic/sitemap.xml\npublic/vercel.svg\npublic/window.svg\nsrc/middleware.ts\ntsconfig.json\nvercel.json", "exit_code": 0, "error": null}

  [Subdirectory context discovered: osiris/AGENTS.md]
  <!-- BEGIN:nextjs-agent-rules -->
  # This is NOT the Next.js you know

  This version has breaking changes — APIs, conventions, and file structure may all differ from your training data. Read the relevant guide in `node_modules/next/dist/docs/` before writing any code. Heed deprecation notices.
  <!-- END:nextjs-agent-rules --> terminal 


c0:
  {"content": "1|<div align=\"center\">\n2|\n3|# ⬡ OSIRIS\n4|\n5|### Open Source Intelligence & Reconnaissance Integrated System\n6|\n7|[![Live Demo](https://img.shields.io/badge/osirisai.live-00E5FF?style=for-the-badge&logo=vercel&logoColor=white)](https://osirislive.app)\n8|[![Support OSIRIS](https://img.shields.io/badge/Support_Project-Patreon-FF424D?style=for-the-badge&logo=patreon&logoColor=white)](https://www.patreon.com/posts/159077425)\n9|[![Next.js](https://img.shields.io/badge/Next.js-16-black?style=for-the-badge&logo=next.js)](https://nextjs.org)\n10|[![TypeScript](https://img.shields.io/badge/TypeScript-5-3178C6?style=for-the-badge&logo=typescript&logoColor=white)](https://typescriptlang.org)\n11|[![MapLibre](https://img.shields.io/badge/MapLibre_GL-GPU_Rendered-396CB2?style=for-the-badge)](https://maplibre.org)\n12|[![License](https://img.shields.io/badge/License-MIT-D4AF37?style=for-the-badge)](LICENSE)\n13|\n14|**A real-time global intelligence dashboard that aggregates live flight tracking, CCTV networks, earthquake monitoring, conflict zone mapping, and 24/7 news feeds into a single GPU-accelerated interface.**\n15|\n16|[Live Demo](https://osirisai.live) · [Report Bug](https://github.com/simplifaisoul/osiris/issues) · [Request Feature](https://github.com/simplifaisoul/osiris/issues) · [Join Discord](https://discord.gg/umBykEpb98)\n17|\n18|</div>\n19|\n20|---\n21|\n22|## Overview\n23|\n24|Osiris is a production-grade OSINT platform that provides situational awareness across multiple intelligence domains. Built with Next.js 16 and MapLibre GL, every data point is rendered via WebGL for 60fps performance even with thousands of concurrent entities on-screen.\n25|\n26|### Key Capabilities\n27|\n28|| Domain | Data Points | Sources |\n29||--------|------------|---------|\n30|| **Aviation** | Commercial, Private, Military, Jets | OpenSky Network |\n31|| **Maritime** | 39 Global Ports, 10 Chokepoints | Static Naval Intel |\n32|| **CCTV** | 2,000+ Cameras | TfL, WSDOT, Caltrans, NYC DOT, VicRoads + more |\n33|| **Seismic** | Real-time M2.5+ | USGS Earthquake API |\n34|| **Fires** | Active Hotspots | NASA FIRMS |\n35|| **News** | 24/7 Live Streams | 25+ Global Broadcasters |\n36|| **Weather** | Severe Events | NASA EONET |\n37|| **Space** | Solar Weather, Satellites | NOAA SWPC, N2YO |\n38|| **Cyber** | CVE Threats, Vulnerability Scanning | NVD, Custom Scanner |\n39|| **Conflict** | 13 Active Zones | Static OSINT Intel |\n40|| **Crypto** | BTC + ETH Wallet Tracing, OFAC SDN Match | blockstream.info, Blockscout, OpenSanctions |\n41|| **Sanctions** | Person / Org / Vessel SDN Search | OpenSanctions (US OFAC SDN mirror) |\n42|| **Telegram OSINT** | Geoparsed Posts from Public Channels | `t.me/s/<channel>` web preview |\n43|\n44|---\n45|\n46|## Architecture\n47|\n48|```\n49|┌─────────────────────────────────────────────────┐\n50|│                  OSIRIS CLIENT                   │\n51|│  ┌──────────┐  ┌──────────┐  ┌───────────────┐ │\n52|│  │ MapLibre  │  │  HUD     │  │  RECON Toolkit│ │\n53|│  │  GL (GPU) │  │ Panels   │  │  Port Scan    │ │\n54|│  │  WebGL    │  │ Layers   │  │  DNS / WHOIS  │ │\n55|│  │  Render   │  │ Controls │  │  Vuln Scanner │ │\n56|│  └──────────┘  └──────────┘  └───────────────┘ │\n57|├─────────────────────────────────────────────────┤\n58|│               NEXT.JS API ROUTES                 │\n59|│  /api/flights         /api/earthquakes          │\n60|│  /api/cctv            /api/news                 │\n61|│  /api/fires           /api/maritime             │\n62|│  /api/gdelt           /api/satellites           │\n63|│  /api/weather         /api/scanner              │\n64|│  /api/sentinel        /api/telegram-feed        │\n65|│  /api/osint/*  (whois, dns, ip, cve, sanctions, │\n66|│                 crypto, sweep, threats, …)      │\n67|├─────────────────────────────────────────────────┤\n68|│              EXTERNAL DATA SOURCES               │\n69|│  OpenSky · USGS · NASA · NOAA · TfL · NVD      │\n70|│  GDACS · EONET · FIRMS · N2YO · RSS Feeds      │\n71|│  blockstream.info · Blockscout · OpenSanctions  │\n72|│  t.me public previews                            │\n73|└─────────────────────────────────────────────────┘\n74|```\n75|\n76|---\n77|\n78|## Features\n79|\n80|### Intelligence Layers\n81|- **16 toggleable data layers** with real-time entity counts\n82|- **GPU-accelerated rendering** — all map data rendered via WebGL, not DOM\n83|- **Progressive loading** — data fetched on-demand when layers are activated\n84|- **Viewport-aware** — only loads relevant data for the visible region\n85|\n86|### RECON Toolkit\n87|- **Port Scanner** — TCP connect scan with service fingerprinting\n88|- **DNS Lookup** — Full record resolution (A, AAAA, MX, NS, TXT, CNAME)\n89|- **WHOIS** — Domain/IP registration data (auto-cross-checked against OFAC SDN)\n90|- **SSL/TLS Inspector** — Certificate chain analysis\n91|- **IP Intelligence** — Geolocation, ASN, threat reputation (auto-cross-checked against OFAC SDN)\n92|- **Vulnerability Scanner** — CVE lookup against NVD database\n93|- **Crypto Wallet Trace** — BTC + ETH lookup (balance, tx history, OFAC SDN sanctions flag)\n94|- **OFAC Sanctions Search** — query persons, organizations, vessels and aircraft against the US OFAC SDN list\n95|\n96|### Live Broadcast Network\n97|- **25+ live 24/7 news streams** from global broadcasters\n98|- Click any news dot on the map to open the live stream\n99|- Feeds from NBC, CBS, ABC, Sky News, Al Jazeera, France 24, NHK, WION, and more\n100|\n101|### Telegram OSINT Layer\n102|- **Public-channel feed** scraped from the unauthenticated `t.me/s/<channel>` web preview — no Bot API token, no MTProto\n103|- Default curated set of 5 channels (EN + RU/UA war reporting), overridable via `OSIRIS_TELEGRAM_CHANNELS`\n104|- Posts are geoparsed against a multilingual place dictionary (EN + Cyrillic + Arabic) and plotted on the map\n105|- Click any cyan dot to read the post and jump to the original on Telegram\n106|\n107|### Crypto Wallet Intelligence\n108|- **BTC** lookups via [blockstream.info](https://blockstream.info) (Esplora API, keyless)\n109|- **ETH** lookups via [Blockscout](https://github.com/blockscout/blockscout)'s public ETH instance (`eth.blockscout.com`, keyless)\n110|- Every lookup is cross-checked against the OFAC SDN sanctioned-address list (mirrored from [`0xB10C/ofac-sanctioned-digital-currency-addresses`](https://github.com/0xB10C/ofac-sanctioned-digital-currency-addresses))\n111|- Sanctioned wallets surface a red **SANCTIONED — OFAC SDN** badge in the RECON panel\n112|\n113|### OFAC SDN Cross-Check\n114|- Standalone `SANCTIONS` tab in the RECON toolkit — full-text search across persons, organisations, vessels and aircraft\n115|- WHOIS and IP-intel routes auto-cross-check registrant / ASN-owner names against the SDN list and surface an inline alert\n116|- Data sourced from [OpenSanctions](https://www.opensanctions.org) (CC-BY 4.0) — keyless, ~7 MB cached in-memory for 24h\n117|\n118|### Conflict Zone Monitoring\n119|- **13 active conflict/tension zones** with severity-coded warning markers\n120|- Active Wars: Ukraine, Gaza, Sudan, Myanmar, DRC, Yemen\n121|- High Tension: Syria, Lebanon, Sahel, Somalia, Red Sea\n122|- Elevated: Taiwan Strait, Korean DMZ\n123|\n124|### Performance Optimized\n125|- **75% reduction in edge requests** vs initial release\n126|- Aggressive polling relaxation (15-30 min intervals for stable data)\n127|- Static data served from memory (zero external API calls for news feeds)\n128|- `layerFetchedRef` prevents duplicate API requests\n129|\n130|---\n131|\n132|## Quick Start\n133|\n134|```bash\n135|git clone https://github.com/simplifaisoul/osiris.git\n136|cd osiris\n137|npm install\n138|npm run dev\n139|```\n140|\n141|Open [http://localhost:3000](http://localhost:3000)\n142|\n143|### Docker / Self-Hosting\n144|\n145|```bash\n146|git clone https://github.com/simplifaisoul/osiris.git\n147|cd osiris\n148|cp .env.template .env     # optional — configure keys / port\n149|docker compose up -d\n150|```\n151|\n152|Open [http://localhost:3000](http://localhost:3000). The image is a multi-stage\n153|`node:22-alpine` standalone build (~220 MB, non-root). The compose file also\n154|carries CasaOS app metadata (`x-casaos:`) for one-click install on\n155|[CasaOS](https://casaos.io). See **[DOCKER.md](DOCKER.md)** for the full Docker,\n156|CasaOS and API-key guide.\n157|\n158|**Prebuilt image (GHCR)** — skip the build and pull it directly:\n159|\n160|```bash\n161|docker pull ghcr.io/aiacos/osiris:latest\n162|docker run -d -p 3000:3000 --env-file .env ghcr.io/aiacos/osiris:latest\n163|```\n164|\n165|**Custom port** — the container always listens on `3000`; set `OSIRIS_PORT` in\n166|`.env` to change the published host port (e.g. `OSIRIS_PORT=3005`) without\n167|editing the compose file.\n168|\n169|### Environment Variables\n170|\n171|OSIRIS works **partially without any API keys** — all core feeds use public,\n172|keyless sources. Copy [`.env.template`](.env.template) to `.env` and set only\n173|what you need:\n174|\n175|```env\n176|# Published host port (container always listens on 3000). Default: 3000\n177|OSIRIS_PORT=3000\n178|\n179|# RECON scanner backend (the only vars the current code reads).\n180|# SCANNER_KEY must match the backend's OSIRIS_KEY — generate with: openssl rand -hex 32\n181|SCANNER_URL=\n182|SCANNER_KEY=\n183|\n184|# Optional, for higher rate limits / future sources (see DOCKER.md for signup links)\n185|FIRMS_API_KEY=                # NASA FIRMS  — firms.modaps.eosdis.nasa.gov/api/map_key/\n186|OPENSKY_CLIENT_ID=            # OpenSky OAuth2 (since Mar 2025) — opensky-network.org\n187|OPENSKY_CLIENT_SECRET=\n188|N2YO_API_KEY=                 # N2YO satellites — n2yo.com (Profile → API key)\n189|AIS_API_KEY=                 # aisstream.io maritime\n190|```\n191|\n192|> Without `SCANNER_URL`/`SCANNER_KEY` the RECON toolkit returns `503`; every\n193|> other layer works out of the box. `.env` is gitignored — only the template is committed.\n194|\n195|---\n196|\n197|## Tech Stack\n198|\n199|| Layer | Technology |\n200||-------|-----------|\n201|", "total_lines": 245, "file_size": 11265, "truncated": true, "hint": "Use offset=201 to continue reading (showing 1-200 of 245 lines)", "is_binary": false, "is_image": false} read_file 


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c0:
  {"content": "201|| Framework | Next.js 16 (App Router, Turbopack) |\n202|| Language | TypeScript 5 |\n203|| Map Engine | MapLibre GL JS (WebGL) |\n204|| Animations | Framer Motion |\n205|| Icons | Lucide React |\n206|| Styling | Custom CSS Design System |\n207|| Deployment | Vercel Edge Network |\n208|\n209|---\n210|\n211|## Keyboard Shortcuts\n212|\n213|| Key | Action |\n214||-----|--------|\n215|| `F` | Toggle flight layers |\n216|| `E` | Toggle earthquakes |\n217|| `S` | Toggle satellites |\n218|| `D` | Toggle day/night cycle |\n219|| `Escape` | Close panels |\n220|\n221|---\n222|\n223|## License\n224|\n225|MIT — see [LICENSE](LICENSE) for details.\n226|\n227|---\n228|\n229|<div align=\"center\">\n230|\n231|**🛠️ SUPPORT THE OSIRIS PROJECT**\n232|The OSIRIS Global Intelligence Grid is entirely open-source, but running the backend scanners and data firehoses isn't cheap.\n233|\n234|If you want to help keep the servers alive, and support us to get access to better tools  unlock the **Special OSIRIS Console**, Currently Just a Cool UI. a you can officially support the project here : \n235|\n236|🔗 [Support OSIRIS on Patreon](https://www.patreon.com/posts/159077425)\n237|\n238|*Supporters receive the `🔴 RedTeam Console` role and access to encrypted developer comms.*\n239|\n240|\n241|**Built by [simplifaisoul](https://github.com/simplifaisoul)**\n242|\n243|[Join our Discord to be a part of this movement!](https://discord.gg/umBykEpb98)\n244|\n245|</div>\n246|", "total_lines": 245, "file_size": 11265, "truncated": false, "is_binary": false, "is_image": false} read_file 


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c0:
  {"content": "1|'use client';\r\n2|\r\n3|import { useEffect, useRef, useState, useCallback, memo } from 'react';\r\n4|import maplibregl from 'maplibre-gl';\r\n5|import 'maplibre-gl/dist/maplibre-gl.css';\r\n6|\r\n7|interface OsirisMapProps {\r\n8|  data: any;\r\n9|  activeLayers: Record<string, boolean>;\r\n10|  onEntityClick?: (entity: any) => void;\r\n11|  onMouseCoords?: (coords: { lat: number; lng: number }) => void;\r\n12|  onRightClick?: (coords: { lat: number; lng: number }) => void;\r\n13|  onViewStateChange?: (vs: { zoom: number; latitude: number }) => void;\r\n14|  flyToLocation?: { lat: number; lng: number; ts: number } | null;\r\n15|  projection?: 'mercator' | 'globe';\r\n16|  mapStyle?: string;\r\n17|  sweepData?: any;\r\n18|  scanTargets?: any[];\r\n19|}\r\n20|\r\n21|function computeSolarTerminator(): [number, number][] {\r\n22|  const now = new Date();\r\n23|  const dayOfYear = Math.floor((now.getTime() - new Date(now.getFullYear(), 0, 0).getTime()) / 86400000);\r\n24|  const declination = -23.44 * Math.cos((2 * Math.PI / 365) * (dayOfYear + 10));\r\n25|  const decRad = declination * Math.PI / 180;\r\n26|  const utcHours = now.getUTCHours() + now.getUTCMinutes() / 60;\r\n27|  const subsolarLng = (12 - utcHours) * 15;\r\n28|  const points: [number, number][] = [];\r\n29|  for (let lng = -180; lng <= 180; lng += 2) {\r\n30|    const lngRad = (lng - subsolarLng) * Math.PI / 180;\r\n31|    const lat = Math.atan(-Math.cos(lngRad) / Math.tan(decRad)) * 180 / Math.PI;\r\n32|    points.push([lng, lat]);\r\n33|  }\r\n34|  const darkSide = declination >= 0 ? -90 : 90;\r\n35|  points.push([180, darkSide]);\r\n36|  points.push([-180, darkSide]);\r\n37|  points.push(points[0]);\r\n38|  return points;\r\n39|}\r\n40|\r\n41|const EMPTY_FC = { type: 'FeatureCollection' as const, features: [] };\r\n42|\r\n43|function OsirisMap({ data, activeLayers, onEntityClick, onMouseCoords, onRightClick, onViewStateChange, flyToLocation, projection = 'globe', mapStyle = 'dark', sweepData, scanTargets = [] }: OsirisMapProps) {\r\n44|  const containerRef = useRef<HTMLDivElement>(null);\r\n45|  const mapRef = useRef<maplibregl.Map | null>(null);\r\n46|  const popupRef = useRef<maplibregl.Popup | null>(null);\r\n47|  const [mapReady, setMapReady] = useState(false);\r\n48|  const prevStyleRef = useRef(mapStyle);\r\n49|\r\n50|  // Create aircraft icon on canvas (for WebGL symbol layer)\r\n51|  const createIcon = useCallback((map: maplibregl.Map, id: string, color: string, size: number) => {\r\n52|    if (map.hasImage(id)) return;\r\n53|    const canvas = document.createElement('canvas');\r\n54|    canvas.width = size; canvas.height = size;\r\n55|    const ctx = canvas.getContext('2d')!;\r\n56|    const cx = size / 2, cy = size / 2;\r\n57|    ctx.fillStyle = color;\r\n58|    ctx.beginPath();\r\n59|    ctx.moveTo(cx, cy - size * 0.4);\r\n60|    ctx.lineTo(cx - size * 0.12, cy + size * 0.1);\r\n61|    ctx.lineTo(cx - size * 0.4, cy + size * 0.2);\r\n62|    ctx.lineTo(cx - size * 0.4, cy + size * 0.3);\r\n63|    ctx.lineTo(cx - size * 0.12, cy + size * 0.15);\r\n64|    ctx.lineTo(cx, cy + size * 0.35);\r\n65|    ctx.lineTo(cx + size * 0.12, cy + size * 0.15);\r\n66|    ctx.lineTo(cx + size * 0.4, cy + size * 0.3);\r\n67|    ctx.lineTo(cx + size * 0.4, cy + size * 0.2);\r\n68|    ctx.lineTo(cx + size * 0.12, cy + size * 0.1);\r\n69|    ctx.closePath();\r\n70|    ctx.fill();\r\n71|    map.addImage(id, { width: size, height: size, data: new Uint8Array(ctx.getImageData(0, 0, size, size).data) });\r\n72|  }, []);\r\n73|\r\n74|  const createDot = useCallback((map: maplibregl.Map, id: string, color: string, size: number) => {\r\n75|    if (map.hasImage(id)) return;\r\n76|    const canvas = document.createElement('canvas');\r\n77|    canvas.width = size; canvas.height = size;\r\n78|    const ctx = canvas.getContext('2d')!;\r\n79|    ctx.fillStyle = color;\r\n80|    ctx.beginPath();\r\n81|    ctx.arc(size/2, size/2, size/2 - 1, 0, Math.PI * 2);\r\n82|    ctx.fill();\r\n83|    map.addImage(id, { width: size, height: size, data: new Uint8Array(ctx.getImageData(0, 0, size, size).data) });\r\n84|  }, []);\r\n85|\r\n86|  useEffect(() => {\r\n87|    if (!containerRef.current || mapRef.current) return;\r\n88|    const map = new maplibregl.Map({\r\n89|      container: containerRef.current,\r\n90|      style: 'https://basemaps.cartocdn.com/gl/dark-matter-gl-style/style.json',\r\n91|      center: [25.48, 42.70], zoom: 6.5, minZoom: 1.5, maxZoom: 18,\r\n92|      attributionControl: false,\r\n93|      maxPitch: 85,\r\n94|    });\r\n95|\r\n96|    map.on('load', () => {\r\n97|      mapRef.current = map;\r\n98|      // Create icons\r\n99|      createIcon(map, 'plane-cyan', '#00E5FF', 24);\r\n100|      createIcon(map, 'plane-green', '#00E676', 24);\r\n101|      createIcon(map, 'plane-pink', '#FF69B4', 24);\r\n102|      createIcon(map, 'plane-red', '#FF3D3D', 24);\r\n103|      createIcon(map, 'plane-grey', '#555555', 24);\r\n104|      createDot(map, 'dot-gold', '#D4AF37', 8);\r\n105|      createDot(map, 'dot-red', '#FF3D3D', 10);\r\n106|      createDot(map, 'dot-orange', '#FF9500', 10);\r\n107|      createDot(map, 'dot-green', '#00E676', 10);\r\n108|      createDot(map, 'dot-fire', '#FF6B00', 10);\r\n109|      createDot(map, 'dot-cctv', '#39FF14', 10);\r\n110|\r\n111|      // Sources\r\n112|      const sources = ['flights','military','jets','private-fl','satellites','earthquakes','gdelt','gps-jamming','day-night','cctv','fires','weather','infrastructure','maritime','maritime-choke','maritime-ships','live-news','sigint-news','conflict-zones', 'war-alerts-targets', 'war-alerts-lines', 'balloons', 'radiation', 'ip-sweep-devices', 'ip-sweep-pulse', 'ip-sweep-connections', 'scan-targets', 'sdk-entities', 'sdk-links'];\r\n113|      sources.forEach(s => map.addSource(s, { type: 'geojson', data: EMPTY_FC }));\r\n114|\r\n115|      // Warning icon generator (parameterized — eliminates 3x copy-paste)\r\n116|      const createWarningIcon = (id: string, color: string) => {\r\n117|        const s = 20;\r\n118|        const c = document.createElement('canvas');\r\n119|        c.width = s; c.height = s;\r\n120|        const ctx = c.getContext('2d')!;\r\n121|", "total_lines": 1452, "file_size": 89155, "truncated": true, "hint": "Use offset=121 to continue reading (showing 1-120 of 1452 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  Resultado da análise do repositório `simplifaisoul/osiris` — documento já fechado, sem arquivos gerados ou alterados.

  - Problema: Falta uma plataforma unificada e em tempo real para correlacionar inteligência de domínios dispersos (aviação, marítimo, terremotos, câmeras, notícias, cyber, cripto e sanções) sem precisar de múltiplas ferramentas separadas.

  - Tema principal: Dashboard OSINT global em tempo real — “Inteligência Global & Reconhecimento Integrado”, focado em consciência situacional via um único mapa interativo.

  - Razão do hype: Junta feeds ao vivo de várias fontes em uma interface acelerada por WebGL (MapLibre GL) com 60fps, inclui até um módulo “RECON” com scanner, DNS, WHOIS, SSL e busca em sanções OFAC, transmitindo a ideia de uma ferramenta estilo “Palantir” para o público geral.

  - Tecnologias envolvidas:
    - TypeScript 5
    - Next.js 16 (App Router, Turbopack)
    - React 19
    - MapLibre GL JS / WebGL
    - Framer Motion, lucide-react
    - hls.js, satellite.js, rss-parser
    - Tailwind CSS 4
    - Vercel Edge
    - Node 22 para imagem Docker  


c0:
    [{"id": "chatcmpl-tool-be2ad6a2c1b22413", "call_id": "chatcmpl-tool-be2ad6a2c1b22413", "response_item_id": "fc_chatcmpl-tool-be2ad6a2c1b22413", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"git clone --depth 1 https://github.com/strukto-ai/mirage.git /tmp/mirage\"}"}}]


c0:
    [{"id": "chatcmpl-tool-8ff0866a1f7a5db8", "call_id": "chatcmpl-tool-8ff0866a1f7a5db8", "response_item_id": "fc_chatcmpl-tool-8ff0866a1f7a5db8", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"ls -la /tmp/mirage && echo '---' && ls -la /tmp/mirage packages 2>/dev/null || true\"}"}}]


c0:
  {"output": "total 64\ndrwxrwxr-x 14 ice  ice    500 May 30 22:54 .\ndrwxrwxrwt 11 root root   420 May 30 22:54 ..\n-rw-rw-r--  1 ice  ice    588 May 30 22:54 .env.example\ndrwxrwxr-x  8 ice  ice    280 May 30 22:54 .git\ndrwxrwxr-x  4 ice  ice    120 May 30 22:54 .github\n-rw-rw-r--  1 ice  ice   5051 May 30 22:54 .gitignore\n-rw-rw-r--  1 ice  ice     44 May 30 22:54 .isort.cfg\n-rw-rw-r--  1 ice  ice   3614 May 30 22:54 .pre-commit-config.yaml\nlrwxrwxrwx  1 ice  ice      9 May 30 22:54 AGENTS.md -> CLAUDE.md\n-rw-rw-r--  1 ice  ice    274 May 30 22:54 CITATION.cff\n-rw-rw-r--  1 ice  ice   5393 May 30 22:54 CLAUDE.md\n-rw-rw-r--  1 ice  ice   3361 May 30 22:54 CONTRIBUTING.md\n-rw-rw-r--  1 ice  ice  11342 May 30 22:54 LICENSE\n-rw-rw-r--  1 ice  ice  10752 May 30 22:54 README.md\n-rw-rw-r--  1 ice  ice   1114 May 30 22:54 SECURITY.md\ndrwxrwxr-x  2 ice  ice    160 May 30 22:54 assets\ndrwxrwxr-x  2 ice  ice    260 May 30 22:54 data\ndrwxrwxr-x  8 ice  ice    220 May 30 22:54 docs\ndrwxrwxr-x  4 ice  ice     80 May 30 22:54 examples\ndrwxrwxr-x  3 ice  ice    300 May 30 22:54 integ\ndrwxrwxr-x  2 ice  ice    100 May 30 22:54 licenses\ndrwxrwxr-x  4 ice  ice    180 May 30 22:54 python\ndrwxrwxr-x  2 ice  ice    120 May 30 22:54 scripts\ndrwxrwxr-x  4 ice  ice    100 May 30 22:54 spec\ndrwxrwxr-x  5 ice  ice    300 May 30 22:54 typescript\n---\n/tmp/mirage:\ntotal 64\ndrwxrwxr-x 14 ice  ice    500 May 30 22:54 .\ndrwxrwxrwt 11 root root   420 May 30 22:54 ..\n-rw-rw-r--  1 ice  ice    588 May 30 22:54 .env.example\ndrwxrwxr-x  8 ice  ice    280 May 30 22:54 .git\ndrwxrwxr-x  4 ice  ice    120 May 30 22:54 .github\n-rw-rw-r--  1 ice  ice   5051 May 30 22:54 .gitignore\n-rw-rw-r--  1 ice  ice     44 May 30 22:54 .isort.cfg\n-rw-rw-r--  1 ice  ice   3614 May 30 22:54 .pre-commit-config.yaml\nlrwxrwxrwx  1 ice  ice      9 May 30 22:54 AGENTS.md -> CLAUDE.md\n-rw-rw-r--  1 ice  ice    274 May 30 22:54 CITATION.cff\n-rw-rw-r--  1 ice  ice   5393 May 30 22:54 CLAUDE.md\n-rw-rw-r--  1 ice  ice   3361 May 30 22:54 CONTRIBUTING.md\n-rw-rw-r--  1 ice  ice  11342 May 30 22:54 LICENSE\n-rw-rw-r--  1 ice  ice  10752 May 30 22:54 README.md\n-rw-rw-r--  1 ice  ice   1114 May 30 22:54 SECURITY.md\ndrwxrwxr-x  2 ice  ice    160 May 30 22:54 assets\ndrwxrwxr-x  2 ice  ice    260 May 30 22:54 data\ndrwxrwxr-x  8 ice  ice    220 May 30 22:54 docs\ndrwxrwxr-x  4 ice  ice     80 May 30 22:54 examples\ndrwxrwxr-x  3 ice  ice    300 May 30 22:54 integ\ndrwxrwxr-x  2 ice  ice    100 May 30 22:54 licenses\ndrwxrwxr-x  4 ice  ice    180 May 30 22:54 python\ndrwxrwxr-x  2 ice  ice    120 May 30 22:54 scripts\ndrwxrwxr-x  4 ice  ice    100 May 30 22:54 spec\ndrwxrwxr-x  5 ice  ice    300 May 30 22:54 typescript", "exit_code": 0, "error": null} terminal 


c0:
    [{"id": "chatcmpl-tool-881e374e432650b0", "call_id": "chatcmpl-tool-881e374e432650b0", "response_item_id": "fc_chatcmpl-tool-881e374e432650b0", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/tmp/mirage/README.md\"}"}}, {"id": "chatcmpl-tool-ab663d237cf73b93", "call_id": "chatcmpl-tool-ab663d237cf73b93", "response_item_id": "fc_chatcmpl-tool-ab663d237cf73b93", "type": "function", "function": {"name": "read_file", "arguments": "{\"path\": \"/tmp/mirage/CLAUDE.md\"}"}}, {"id": "chatcmpl-tool-9ec3a6b8a6ab7a70", "call_id": "chatcmpl-tool-9ec3a6b8a6ab7a70", "response_item_id": "fc_chatcmpl-tool-9ec3a6b8a6ab7a70", "type": "function", "function": {"name": "search_files", "arguments": "{\"path\": \"/tmp/mirage\", \"pattern\": \"package.json\", \"target\": \"files\"}"}}]


c0:
  {"content": "1|<p align=\"center\">\n2|  <img src=\"assets/mirage-og-light@2x.png\" alt=\"Mirage: A Unified Virtual File System for AI Agents\" width=\"900\">\n3|</p>\n4|\n5|<p align=\"center\">\n6|    <a href=\"https://docs.mirage.strukto.ai\" alt=\"Documentation\">\n7|        <img src=\"https://img.shields.io/badge/mirage-docs-0C0C0C?labelColor=FAFAFA\" /></a>\n8|    <a href=\"https://www.strukto.ai\" alt=\"Website\">\n9|        <img src=\"https://img.shields.io/badge/made by-strukto.ai-0C0C0C?labelColor=FAFAFA\" /></a>\n10|    <a href=\"https://github.com/strukto-ai/mirage/blob/main/LICENSE\" alt=\"License\">\n11|        <img src=\"https://img.shields.io/github/license/strukto-ai/mirage?color=0C0C0C&labelColor=FAFAFA\" /></a>\n12|    <a href=\"https://discord.gg/u8BPQ65KsS\" alt=\"Discord\">\n13|        <img src=\"https://img.shields.io/badge/discord-join-0C0C0C?labelColor=FAFAFA&logo=discord&logoColor=0C0C0C\" /></a>\n14|    <br/>\n15|    <a href=\"https://docs.mirage.strukto.ai/python/quickstart\" alt=\"Python docs\">\n16|        <img src=\"https://img.shields.io/badge/python-docs-0C0C0C?labelColor=FAFAFA&logo=python&logoColor=0C0C0C\" alt=\"Python docs\"></a>\n17|    <a href=\"https://pypi.org/project/mirage-ai/\" alt=\"PyPI Version\">\n18|        <img src=\"https://img.shields.io/pypi/v/mirage-ai.svg?color=0C0C0C&labelColor=FAFAFA\"/></a>\n19|    <br/>\n20|    <a href=\"https://docs.mirage.strukto.ai/typescript/quickstart\" alt=\"TypeScript docs\">\n21|        <img src=\"https://img.shields.io/badge/typescript-docs-0C0C0C?labelColor=FAFAFA&logo=typescript&logoColor=0C0C0C\" alt=\"TypeScript docs\"></a>\n22|    <a href=\"https://www.npmjs.com/package/@struktoai/mirage-node\" alt=\"NPM Version\">\n23|        <img src=\"https://img.shields.io/npm/v/@struktoai/mirage-node.svg?color=0C0C0C&labelColor=FAFAFA\"/></a>\n24|</p>\n25|\n26|Mirage is **a Unified Virtual File System for AI Agents**: a single tree that mounts services and data sources like S3, Google Drive, Slack, Gmail, and Redis side-by-side as one filesystem.\n27|\n28|AI agents reach every backend with the same handful of Unix-like tools, and pipelines compose across services as naturally as on a local disk. It's a simulated environment, agents see one filesystem underneath. Any LLM that already knows bash can use Mirage out of the box, with zero new vocabulary.\n29|\n30|```ts\n31|const ws = new Workspace({\n32|  '/data':   new RAMResource(),\n33|  '/s3':     new S3Resource({ bucket: 'logs' }),\n34|  '/slack':  new SlackResource({}),\n35|  '/github': new GitHubResource({}),\n36|})\n37|\n38|await ws.execute('grep alert /slack/general/*.json | wc -l')\n39|await ws.execute('cat /github/mirage/README.md')\n40|await ws.execute('cp /s3/report.csv /data/local.csv')\n41|\n42|// Register a new command, available across every mount.\n43|ws.command('summarize', ...)\n44|\n45|// Override a command for a specific resource + filetype —\n46|// `cat` on a Parquet file in /s3 renders rows as JSON instead of raw bytes.\n47|ws.command('cat', { resource: 's3', filetype: 'parquet' }, ...)\n48|\n49|await ws.execute('summarize /github/mirage/README.md')\n50|await ws.execute('cat /s3/events/2026-05-06.parquet | jq .user')\n51|```\n52|\n53|## About\n54|\n55|- **One filesystem, every backend.** Every service speaks the same filesystem semantics, so agents reason about one abstraction instead of N SDKs and M MCPs, leaning on the filesystem and bash vocabulary LLMs are most fluent in.\n56|- **Multiple resources, one filesystem:** RAM, Disk, Redis, S3 / R2 / OCI / Supabase / GCS, Gmail / GDrive / GDocs / GSheets / GSlides, GitHub / Linear / Notion / Trello, Slack / Discord / Telegram / Email, MongoDB, SSH, and more, mounted side-by-side under a single root.\n57|- **Familiar bash tools across every mount.** Agents reuse the same handful of Unix-like tools instead of learning a new API per service, and pipelines compose across services as naturally as on a local disk, the exact corpus modern LLMs are most heavily trained on.\n58|- **Portable workspaces:** clone, snapshot, and version your environment. Move agent runs between machines without restarting or reconfiguring the system.\n59|- **Embed in your apps and services:** Python and TypeScript SDKs let you give your AI agents a virtual filesystem directly inside FastAPI, Express, browser apps, or any async runtime, no separate process required. Clone, snapshot, and version the workspace from inside your code.\n60|- **Works with major agent application frameworks:** OpenAI Agents SDK, Vercel AI SDK (TypeScript), LangChain, Pydantic AI, CAMEL, and OpenHands.\n61|- **Lightweight CLI + daemon:** plugs into coding agents like Claude Code and Codex so they reach every mounted resource through familiar bash, getting more useful work done per turn.\n62|\n63|## Architecture\n64|\n65|<p align=\"center\">\n66|  <picture>\n67|    <source media=\"(prefers-color-scheme: dark)\" srcset=\"assets/mirage-arch-dark.svg\">\n68|    <img src=\"assets/mirage-arch-light.svg\" alt=\"Mirage architecture: AI Agent and Application → Mirage Bash and VFS → Dispatcher &amp; Cache → Infrastructure and Remote\" width=\"900\">\n69|  </picture>\n70|</p>\n71|\n72|## Installation\n73|\n74|### Prerequisites\n75|\n76|- **Python** ≥ 3.12 for the `mirage-ai` package and the `mirage` CLI\n77|- **Node.js** ≥ 20 for the TypeScript SDK\n78|- **macOS** or **Linux** (FUSE-based mounts require platform support)\n79|\n80|### Python\n81|\n82|```bash\n83|uv add mirage-ai\n84|```\n85|\n86|This installs both the `mirage` library and the `mirage` CLI binary.\n87|\n88|### TypeScript\n89|\n90|Pick the package that matches your runtime:\n91|\n92|```bash\n93|npm install @struktoai/mirage-node      # Node.js servers and CLIs\n94|npm install @struktoai/mirage-browser   # browser / edge runtimes\n95|npm install @struktoai/mirage-core      # runtime-agnostic primitives\n96|```\n97|\n98|`@struktoai/mirage-node` and `@struktoai/mirage-browser` both pull in `@struktoai/mirage-core` automatically.\n99|\n100|### CLI\n101|\n102|```bash\n103|curl -fsSL https://strukto.ai/mirage/install.sh | sh\n104|```\n105|\n106|Or via your package manager of choice:\n107|\n108|```bash\n109|npm install -g @struktoai/mirage-cli\n110|```\n111|\n112|```bash\n113|uvx mirage-ai\n114|```\n115|\n116|```bash\n117|npx @struktoai/mirage-cli\n118|```\n119|\n120|## Quickstart (Python)\n121|\n122|```python\n123|from mirage import Workspace\n124|from mirage.resource.gdocs import GDocsConfig, GDocsResource\n125|from mirage.resource.ram import RAMResource\n126|from mirage.resource.s3 import S3Config, S3Resource\n127|from mirage.resource.slack import SlackConfig, SlackResource\n128|\n129|ws = Workspace({\n130|    \"/data\":  RAMResource(),\n131|    \"/s3\":    S3Resource(S3Config(bucket=\"my-bucket\")),\n132|    \"/slack\": SlackResource(SlackConfig()),\n133|    \"/docs\":  GDocsResource(GDocsConfig()),\n134|})\n135|\n136|await ws.execute(\"cp /s3/report.csv /data/report.csv\")\n137|await ws.execute(\"grep alert /s3/data/log.jsonl | wc -l\")\n138|\n139|ws.snapshot(\"demo.tar\")\n140|```\n141|\n142|## Quickstart (TypeScript)\n143|\n144|```ts\n145|import {\n146|  Workspace,\n147|  RAMResource,\n148|  S3Resource,\n149|  SlackResource,\n150|  GDocsResource,\n151|} from '@struktoai/mirage-browser'\n152|\n153|const ws = new Workspace({\n154|  '/data':  new RAMResource(),\n155|  '/s3':    new S3Resource({ bucket: 'my-bucket' }),\n156|  '/slack': new SlackResource({}),\n157|  '/docs':  new GDocsResource({}),\n158|})\n159|\n160|await ws.execute('cp /s3/report.csv /data/report.csv')\n161|await ws.execute('grep alert /s3/data/log.jsonl | wc -l')\n162|```\n163|\n164|## Quickstart (CLI)\n165|\n166|```bash\n167|mirage workspace create ws.yaml --id demo\n168|mirage execute   --workspace_id demo --command \"cp /s3/report.csv /data/report.csv\"\n169|mirage provision --workspace_id demo --command \"cat /s3/data/large.jsonl\"\n170|mirage workspace snapshot demo demo.tar\n171|mirage workspace load demo.tar --id demo-restored\n172|```\n173|\n174|## Agent Frameworks\n175|\n176|Mirage drops into the major agent application frameworks as a sandbox or tool layer. Your agent runs against the same mount tree it would in bash, so swapping the model or runtime never changes the surface.\n177|\n178|### OpenAI Agents SDK (Python)\n179|\n180|The `MirageSandboxClient` plugs a `Workspace` into the OpenAI Agents SDK as a sandbox: bash commands the agent runs execute against your mounts.\n181|\n182|```python\n183|from agents import Runner\n184|from agents.run import RunConfig\n185|from agents.sandbox import SandboxAgent, SandboxRunConfig\n186|\n187|from mirage.agents.openai_agents import MirageSandboxClient\n188|\n189|client = MirageSandboxClient(ws)\n190|agent = SandboxAgent(\n191|    name=\"Mirage Sandbox Agent\",\n192|    model=\"gpt-5.4-nano\",\n193|    instructions=ws.file_prompt,\n194|)\n195|\n196|result = await Runner.run(\n197|    agent,\n198|    \"Summarize /s3/data/report.parquet into /report.txt.\",\n199|    run_config=RunConfig(sandbox=SandboxRunConfig(client=client)),\n200|)\n201|```\n202|\n203|### Vercel AI SDK (TypeScript)\n204|\n205|`mirageTools(ws)` exposes the workspace as a typed AI SDK tool set, so any model wired into the AI SDK can read and write across mounts, in Node or the browser.\n206|\n207|```ts\n208|import { generateText } from 'ai'\n209|import { openai } from '@ai-sdk/openai'\n210|import { mirageTools } from '@struktoai/mirage-agents/vercel'\n211|import { buildSystemPrompt } from '@struktoai/mirage-agents/openai'\n212|\n213|const { text } = await generateText({\n214|  model: openai('gpt-5.4-nano'),\n215|  system: buildSystemPrompt({ mountInfo: { '/': 'In-memory filesystem' } }),\n216|  prompt: \"Use readFile to read /docs/paper.pdf, then describe what's in it.\",\n217|  tools: mirageTools(ws),\n218|})\n219|```\n220|\n221|LangChain, Pydantic AI, CAMEL, OpenHands, and Mastra adapters live alongside these.\n222|\n223|## Cache\n224|\n225|Every `Workspace` ships with a **two-layer cache** so repeated work against remote backends (S3, GDrive, Slack, …) hits local state instead of the network:\n226|\n227|- **Index cache.** Listings and metadata. The first directory walk hits the API; subsequent ones serve from the index until TTL expires.\n228|- **File cache.** Object bytes. The first read streams from origin; later pipelines read from cache.\n229|- **Pluggable backends.** Each layer is a store with two built-ins:\n230|  - **RAM** (default): in-process, zero setup, 512 MB file cache and 10-minute index TTL. Best for single-process apps and notebooks.\n231|  - **Redis**: shared across workers, processes, and machines. Best for serverless, multi-replica services, or when you want cache state to survive restarts.\n232|\n233|```ts\n234|import { RedisFileCacheStore, RedisIndexCacheStore, Workspace } from 'mirage/node'\n235|\n236|const ws = new Workspace(\n237|  { '/s3': new S3Resource({ bucket: 'my-bucket' }) },\n238|  {\n239|    cache: new RedisFileCacheStore({ url: 'redis://localhost:6379/0', limit: '8GB' }),\n240|    index: new RedisIndexCacheStore({ url: 'redis://localhost:6379/0', ttl: 600 }),\n241|  },\n242|)\n243|```\n244|\n245|```ts\n246|import { S3Resource, Workspace } from 'mirage/node'\n247|\n248|const ws = new Workspace({ '/s3': new S3Resource({ bucket: 'my-bucket' }) })\n249|\n250|// 1. Index miss → S3 LIST. Listing stored in index cache.\n251|await ws.execute('ls /s3/data/')\n252|\n253|// 2. Index hit → 0 network calls.\n254|await ws.execute('find /s3/data/ -name \"*.jsonl\"')\n255|\n256|// 3. File miss → S3 GET. Bytes stored in file cache.\n257|await ws.execute('cat /s3/data/log.jsonl | wc -l')\n258|\n259|// 4. File hit → 0 network calls.\n260|await ws.execute('grep alert /s3/data/log.jsonl')\n261|```\n262|\n263|## Contributors\n264|\n265|Thanks to everyone who has contributed to Mirage.\n266|\n267|<a href=\"https://github.com/strukto-ai/mirage/graphs/contributors\">\n268|  <img src=\"https://contrib.rocks/image?repo=strukto-ai/mirage\" alt=\"Mirage contributors\" />\n269|</a>\n270|", "total_lines": 269, "file_size": 10752, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|# CLAUDE.md\n2|\n3|MIRAGE is a package that allows you to mount anything as a filesystem and make it usable by AI Agents.\n4|\n5|## Repo Layout\n6|\n7|This monorepo hosts two sibling implementations:\n8|\n9|- `python/` — the Python package (`mirage/`, `tests/`, `pyproject.toml`, `uv.lock`).\n10|- `typescript/` — the TypeScript monorepo (`packages/core`, `packages/node`, etc.).\n11|- `docs/`, `examples/`, `.github/` — shared across both.\n12|\n13|Run Python commands from `python/`, TypeScript commands from `typescript/`.\n14|\n15|## Development Setup\n16|\n17|This project uses `uv` for Python dependency management. Install dependencies with:\n18|\n19|```bash\n20|cd python && uv sync --all-extras --no-extra camel\n21|```\n22|\n23|`camel` is declared as conflicting with `openai` (and other extras) in `pyproject.toml`, so `uv sync --all-extras` fails. Exclude `camel` to keep the `openai` stack.\n24|\n25|### Running examples\n26|\n27|Examples under `examples/python/` load `.env.development` from the repo root (cwd-relative). To keep cwd at the root while using the `python/` venv, invoke the venv interpreter directly:\n28|\n29|```bash\n30|./python/.venv/bin/python examples/python/s3/s3.py\n31|```\n32|\n33|Avoid `uv --directory python run ...` for examples — it changes cwd to `python/` and breaks `load_dotenv(\".env.development\")`.\n34|\n35|## Backward Compatibility\n36|\n37|- No need to consider backward compatibility for the code.\n38|\n39|## Create a PR\n40|\n41|When asked to create a PR, please follow the following steps:\n42|\n43|1. Run `pre-commit run --all-files` from the repo root to lint and format the code.\n44|1. Run `cd python && uv run pytest` to run the Python tests.\n45|1. Run `git add -A` to add all changes.\n46|1. Run `git checkout -b <branch-name>` to create a new branch.\n47|1. Run `git commit -m \"<commit-message>\"` to commit the changes.\n48|1. Run `git push origin <branch-name>` to push the changes to the remote repository.\n49|1. Run `gh pr create --title \"<pr-title>\" --body \"<pr-body>\"` to create a PR.\n50|\n51|## Commands\n52|\n53|### Linting and Formatting\n54|\n55|After making major changes, run pre-commit from the repo root to ensure code quality:\n56|\n57|```bash\n58|./python/.venv/bin/pre-commit run --all-files\n59|```\n60|\n61|Invoke the venv's `pre-commit` binary directly (not via `uv --directory python run`) so cwd stays at the repo root — otherwise `git ls-files` only lists files under `python/` and `examples/` gets silently skipped.\n62|\n63|## Type Conventions\n64|\n65|- Paths must always be represented as `PathSpec`, never raw strings. All functions that accept or return paths use `list[str | PathSpec]` where `str` is for text arguments and `PathSpec` is for paths. Never pass a path as a plain `str` — wrap it in `PathSpec`.\n66|\n67|## Rules\n68|\n69|- **Shell-style commands** (cat, grep, du, find, head, tail, wc, ls, etc.) follow POSIX / Unix coreutils semantics as much as possible; match BSD/GNU behavior and document any deliberate divergence.\n70|- **Async-native by default.** I/O uses `aiofiles` / `redis.asyncio` / `aioboto3`, and command pipelines are async generators.\n71|- **Tests mirror src 1:1 where reasonable.** Try to have a matching `tests/<path>/test_a.py` for each source file `mirage/<path>/a.py`. `__init__.py`, pure type-stub modules, and trivial re-exports are fine to skip; modules with real logic should have one.\n72|- **Do not add `__init__.py` files under `tests/`.** Tests are namespace packages and pytest discovers them without `__init__.py`. Don't create one when adding a new test directory.\n73|- Avoid add any comments or docstrings on the top of the file.\n74|- Do not create nested functions.\n75|- Add type to Args for docstring.\n76|- Do not add comment after each line of code in the format of \"# 10MB - trigger segmentation for files larger than this\". The most you can add is \"# 10MB\".\n77|- For all imports you need to put to the top of the file. Don't have imports within each function.\n78|- **No circular imports.** If putting an import at the top would cause a cycle, that's a sign the dependency direction is wrong — fix the design (dependency injection, splitting modules, moving the shared piece to a leaf), don't paper over it with function-local lazy imports. Verify by checking that running `cd python && uv run python -c \"import <every changed module>\"` succeeds without ImportError.\n79|- **Never silently swallow exceptions.** `try: ... except: pass` (or `except SomeError: pass`) hides real bugs. If you genuinely need to ignore an error, log it (`logger.debug(...)`) or document loudly why it's safe. Default behavior should be: let the exception propagate. Especially never silently swallow `RuntimeError` — it usually signals something deeper (event loop in wrong state, recursion limit, etc.) that you need to actually fix.\n80|- **Never call `asyncio.run()` inside a sync function that might be invoked under an outer event loop.** It will raise `RuntimeError: asyncio.run() cannot be called from a running event loop`. If you need async behavior from a sync API, either: (a) make the calling function `async`, (b) operate on the underlying sync state directly (e.g. write to a dict instead of calling an async setter), or (c) use a sync alternative of the same library (e.g. `redis.Redis` instead of `redis.asyncio.Redis`). Do NOT wrap with `try/except RuntimeError: pass` — that masks the bug AND leaks the unawaited coroutine.\n81|- Please don't change any file name unless I ask you to do so.\n82|- Don't add too many printings or comments in the code.\n83|- Don't add README.md unless I ask you to do so.\n84|- Use uv add to install new dependencies.\n85|", "total_lines": 84, "file_size": 5393, "truncated": false, "is_binary": false, "is_image": false} read_file 


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c0:
    [{"id": "chatcmpl-tool-90e19fde9dd6c808", "call_id": "chatcmpl-tool-90e19fde9dd6c808", "response_item_id": "fc_chatcmpl-tool-90e19fde9dd6c808", "type": "function", "function": {"name": "write_file", "arguments": "{\"path\": \"/home/ice/analise_repositorio.md\", \"content\": \"# An\u00e1lise do Reposit\u00f3rio: strukto-ai/mirage\\n\\n## Problema\\nO reposit\u00f3rio **Mirage** resolve o problema de fragmenta\u00e7\u00e3o de acesso a dados e servi\u00e7os por parte de agentes de IA. Antes, cada servi\u00e7o (S3, Google Drive, Slack, Gmail, Redis, GitHub, etc.) exigia um SDK e um modelo de dados pr\u00f3prios, dificultando a composi\u00e7\u00e3o de fluxos de trabalho e aumentando a curva de aprendizado para modelos de linguagem. Mirage unifica todos esses servi\u00e7os em uma \u00e1rvore de diret\u00f3rio \u00fanica, permitindo que agentes usem as mesmas ferramentas bash/Unix para interagir com qualquer backend.\\n\\n## Tema Principal\\n**A Unified Virtual Filesystem For AI Agents**: um sistema de arquivos virtual que monta servi\u00e7os e fontes de dados variadas (S3, Google Drive, Slack, Gmail, Redis, GitHub, MongoDB etc.) lado a lado sob uma \u00fanica raiz, acess\u00edvel por comandos shell familiares (cat, grep, ls, cp, find, wc).\\n\\n## Raz\u00e3o do Hype\\n- **Abstra\u00e7\u00e3o simples e universal:** age como tradutor universal entre LLMs e servi\u00e7os externos via sem\u00e2ntica de sistema de arquivos.\\n- **Multi-framework:** compat\u00edvel com OpenAI Agents SDK, Vercel AI SDK, LangChain, Pydantic AI, CAMEL, OpenHands, Mastra.\\n- **Multi-linguagem:** SDKs em Python e TypeScript, mais CLI global.\\n- **Cache plug\u00e1vel:** duas camadas de cache (\u00edndice e arquivo) com backends em RAM ou Redis para reduzir chamadas de rede.\\n- **Portabilidade:** snapshots e clones do workspace permitem reproduzir/mover ambientes entre m\u00e1quinas sem reconfigura\u00e7\u00e3o.\\n- **Habilidade para rodar em m\u00faltiplos runtimes:** Node.js, browser, Python/async, FastAPI, Express.\\n\\n## Tecnologias\\n- **Linguagens:** TypeScript (node, browser, core, agents, cli, server) e Python (mirage-ai)\\n- **Runtime:** Node.js \u2265 20, Python \u2265 3.12\\n- **Camada de integra\u00e7\u00e3o/bindings:** aiofiles, redis.asyncio / aioboto3, aiosqlite, PyArrow, DuckDB, httpx/HTTP, fsspec-like\\n- **Armazenamento alternativa local:** RAM, Disk, Redis, SQLite, DuckDB\\n- **Servi\u00e7os suportados:** S3/R2/OCI/Supabase/GCS, Gmail/GDrive/GDocs/GSheets/GSlides, GitHub/Linear/Notion/Trello, Slack/Discord/Telegram/Email, MongoDB, SSH, entre outros\\n- **CLI + Daemon:** permite uso direto por coding agents como Claude Code e Codex\\n- **Gerenciamento de depend\u00eancias Python:** uv\\n\\n---\\n*An\u00e1lise feita em 30/05/2026, a partir do clone do reposit\u00f3rio https://github.com/strukto-ai/mirage.*\\n\"}"}}]


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c0:
  - **O que foi feito**
    - Clonei o repositório `strukto-ai/mirage` para `/tmp/mirage`.
    - Li `README.md`, `CLAUDE.md` e listei os `package.json` do módulo TypeScript.
    - Gerei um arquivo de análise estruturado em `/home/ice/analise_repositorio.md`.

  - **Resultado**
    - **Problema:** Fragilidade na integração de agentes de IA com múltiplos serviços externos, exigindo SDKs e interfaces diferentes por backend.
    - **Tema principal:** Sistema de arquivos virtual unificado para agentes de IA, montando vários serviços como diretórios de um único filesystem.
    - **Razão do hype:** Abstração simples (shell/semântica de filesystem), stack multi-linguagem e multi-framework, cache plugável e possibilidade de snapshot/clone do ambiente.
    - **Tecnologias:** TypeScript + Python, Node ≥ 20, Python ≥ 3.12, `uv` (Python), Redis, aio stack, RAM/Disk/SQLite/DuckDB como backends locais.

  - **Arquivo gerado:** `/home/ice/analise_repositorio.md`  


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  {"content": "1|# a-stock-data\n2|\n3|A 股全栈数据工具包 — 7 层架构 · 27 个端点 · 13 个数据源 · 零第三方数据封装依赖\n4|\n5|一个自包含的 Skill 文件，把分散在 13 个数据源里的 A 股原始数据整合成 AI 编程助手直接能用的工具集。你不用再背 mootdx 的 K 线参数、东财的 PDF Referer 头、iwencai 的 X-Claw 鉴权——全部封装好了。\n6|\n7|> **V3.2（2026-05-30）：** ① **数据源优先级 + 东财防封**——优先用通达信(mootdx)/腾讯（不封 IP），东财仅用于其独有数据，并新增统一节流入口 `em_get()`，所有东财接口内置串行限流（间隔≥1s+随机抖动）+ 会话复用，AI 抄代码即自带防封；② **财联社快讯下线（#14）**——`cls.cn` 旧 API 全面 404，改用东财全球资讯。\n8|>\n9|> **V3.1 修复（2026-05-19）：** 替换 4 个失效接口（百度 PAE 资金流→东财 push2、大宗交易/机构席位报表名更新）+ 修复东财全球资讯和巨潮公告参数变更。\n10|>\n11|> **V3.0 Breaking Change：** 彻底移除 akshare 依赖，所有数据源改为直连 HTTP API。新增资金面/筹码层。\n12|\n13|> 兼容 [Claude Code](https://github.com/anthropics/claude-code) · [Codex](https://github.com/openai/codex) · [OpenClaw](https://github.com/anthropics/openclaw)\n14|>\n15|> Skill 文件本质是结构化 Markdown + 内嵌 Python，任何支持上下文注入的 AI 编程助手都能用。\n16|\n17|---\n18|\n19|## 架构\n20|\n21|```\n22|A 股全栈数据 · 七层架构 · V3.2\n23|│  （优先级：mootdx/腾讯 不封IP 优先用；东财仅用于独有数据，已内置限流防封）\n24|├── 行情层    mootdx + 腾讯财经 + 百度K线   K线(带MA5/10/20) + 五档盘口 + PE/PB/市值 + 指数/ETF\n25|├── 研报层    东财 reportapi + 同花顺 + iwencai  研报列表 / PDF下载 / 一致预期 / NL搜索\n26|├── 信号层    同花顺 + 百度股市通 + 东财     强势股 + 题材归因 + 北向资金 + 概念板块\n27|│                                           + 资金流向(push2) + 龙虎榜 + 全市场龙虎榜 + 解禁 + 行业对比\n28|├── 资金面    东财 datacenter + push2        融资融券 + 大宗交易 + 股东户数 + 分红送转 + 资金流(分钟+120日)\n29|├── 新闻层    东财（直连HTTP）              个股新闻 / 全球资讯（财联社快讯已下线）\n30|├── 基础数据  mootdx + 东财 + 新浪           季报37字段 / F10九大类 / 财报三表\n31|└── 公告层    巨潮 cninfo + mootdx           沪深北全量公告\n32|```\n33|\n34|---\n35|\n36|## 快速开始\n37|\n38|**3 步，2 分钟。**\n39|\n40|```bash\n41|# 1. 创建 skill 目录\n42|mkdir -p ~/.claude/skills/a-stock-data\n43|\n44|# 2. 把 SKILL.md 放进去\n45|curl -o ~/.claude/skills/a-stock-data/SKILL.md \\\n46|  https://raw.githubusercontent.com/simonlin1212/a-stock-data/main/SKILL.md\n47|\n48|# 3. 安装依赖（V3.0 不再需要 akshare）\n49|pip install mootdx requests pandas stockstats\n50|```\n51|\n52|启动 Claude Code，说一句「帮我看看 688017 的估值」，自动激活。\n53|\n54|> **Codex / OpenClaw 用户：** 把 SKILL.md 的内容贴入你的系统 prompt 或项目上下文文件即可，内嵌的 Python 代码可直接执行。\n55|\n56|---\n57|\n58|## 27 个端点能力清单\n59|\n60|### 行情层（实时，不封 IP）\n61|\n62|| 端点 | 数据 |\n63||------|------|\n64|| mootdx 行情 | K线(多周期) + 五档盘口 + 逐笔成交 + 实时报价 46 字段 |\n65|| 腾讯财经 | PE(TTM) / PB / 总市值 / 流通市值 / 换手率 / 涨跌停价 / 指数 / ETF |\n66|| **百度K线** | 日K线 + MA5/MA10/MA20 均价直接返回（V3.0 新增） |\n67|\n68|### 研报层\n69|\n70|| 端点 | 数据 |\n71||------|------|\n72|| 东财 reportapi | 研报列表 + 评级 + 三年 EPS 预测 |\n73|| 东财 PDF 下载 | 完整研报 PDF（已处理 Referer 鉴权） |\n74|| 同花顺一致预期 | 机构一致预期 EPS（直连 basic.10jqka.com.cn） |\n75|| iwencai NL 搜索 | 自然语言跨主题研报检索 |\n76|\n77|### 信号层\n78|\n79|| 端点 | 数据 |\n80||------|------|\n81|| 同花顺热点 | 当日强势股 + 题材归因 reason tags（编辑部人工标注） |\n82|| 同花顺北向（实时） | 沪股通 / 深股通分钟级流向（262 个时间点） |\n83|| 同花顺北向（历史） | 本地自缓存日级历史 |\n84|| 百度概念板块 | 行业 / 概念 / 地域三维归属 + 当日涨跌幅 |\n85|| **东财资金流向** | 主力 / 大单 / 中单 / 小单 / 超大单分钟级净流入（V3.1 替换百度 PAE） |\n86|| 龙虎榜席位 | 上榜记录 + 买卖席位 TOP5 + 机构动向 |\n87|| 全市场龙虎榜 | 每日全市场上榜股票 + 净买额排名 + 上榜原因 |\n88|| 限售解禁日历 | 历史解禁 + 未来 90 天待解禁预警 |\n89|| **行业板块排名** | 东财行业涨跌/上涨下跌家数（V3.0 替换同花顺，零鉴权） |\n90|\n91|### 资金面 / 筹码层（V3.0 新增）\n92|\n93|| 端点 | 数据 |\n94||------|------|\n95|| **融资融券明细** | 日级融资余额/买入/偿还 + 融券余额/卖出/偿还 |\n96|| **大宗交易** | 成交价/量 + 买卖方营业部 + 溢价率 |\n97|| **股东户数变化** | 季度股东数 + 环比变化 + 户均持股（筹码集中度） |\n98|| **分红送转历史** | 每股派息/送股/转增 + 进度状态 |\n99|| **个股资金流120日** | 主力/大单/中单/小单日级净流入 |\n100|\n101|### 新闻层\n102|\n103|| 端点 | 数据 |\n104||------|------|\n105|| 个股新闻 | 东财个股新闻流（直连 search-api-web） |\n106|| ~~财联社快讯~~ | ⚠️ 已下线（cls.cn 迁 Next.js，旧 API 404，#14）→ 用全球资讯替代 |\n107|| 全球资讯 | 东财全球财经资讯（直连 np-weblist，7×24） |\n108|\n109|### 基础数据 + 公告\n110|\n111|| 端点 | 数据 |\n112||------|------|\n113|| 季报快照 | 37 字段（EPS / ROE / 净利润 / 主营收入...） |\n114|| F10 公司资料 | 9 大类文本（截断优化，-70% token） |\n115|| 东财个股信息 | 行业/总股本/流通股/市值/上市日期（直连 push2） |\n116|| 新浪财报三表 | 资产负债表/利润表/现金流量表（直连 quotes.sina.cn） |\n117|| 巨潮公告 | 沪深北交所全量公告 |\n118|\n119|### 鉴权要求\n120|\n121|除 iwencai 外，其余所有数据源**完全免费无 Key**。仅 iwencai 语义搜索需要 API Key（[申请地址](https://www.iwencai.com/skillhub)）。\n122|\n123|---\n124|\n125|## 使用示例\n126|\n127|跟你的 AI 助手说这些话就能激活：\n128|\n129|| 场景 | 说什么 |\n130||------|--------|\n131|| 个股估值 | 「帮我估一下 688017，给我 PE / PEG / 消化时间」 |\n132|| 题材归因 | 「今天哪些股票走强，主要是什么题材」 |\n133|| 研报检索 | 「人形机器人产业链最近的研报，特别是丝杠和减速器」 |\n134|| 北向资金 | 「今天北向资金流入流出怎么样」 |\n135|| 概念板块 | 「688017 属于哪些概念板块」 |\n136|| 资金流向 | 「000858 今天主力资金流入还是流出」 |\n137|| 龙虎榜 | 「002475 最近上过龙虎榜吗，哪些营业部在买」 |\n138|| 全市场龙虎榜 | 「今天龙虎榜哪些票净买入最多」 |\n139|| 解禁预警 | 「这只股票未来 3 个月有没有限售解禁」 |\n140|| 行业轮动 | 「今天哪些行业涨幅最大，资金在流入哪些板块」 |\n141|| 融资融券 | 「600519 最近的融资余额变化趋势」 |\n142|| 大宗交易 | 「这只票最近有没有大宗交易，溢价还是折价」 |\n143|| 股东户数 | 「000858 股东户数在增加还是减少，筹码集中吗」 |\n144|| 分红送转 | 「茅台历年分红派息多少」 |\n145|| 新闻公告 | 「拉一下 300476 最近的新闻和公告」 |\n146|| 批量对比 | 「帮我对比这 5 只半导体股的估值」 |\n147|\n148|### 内置 4 套调研流程\n149|\n150|| 流程 | 做什么 | 耗时 |\n151||------|--------|------|\n152|| 单票估值 | 实时价 → 一致预期 EPS → 前向 PE / PEG / PE 消化年数 | 30 秒 |\n153|| 批量对比 | 多只股票横向估值排列 | 1 分钟 |\n154|| 主题研报 | iwencai 多关键词 NL 搜索 + 东财 PDF 交叉补充 | 2 分钟 |\n155|| 新标的调研 | 机构覆盖 → 估值 → 概念板块 → 资金流向 → 龙虎榜 → 解禁 → 两融 | 1 分钟 |\n156|\n157|---\n158|\n159|## V3.2 亮点\n160|\n161|| 变化 | 说明 |\n162||------|------|\n163|| **数据源优先级原则** | 明确「能用通达信(mootdx)/腾讯就别用东财」——前两者 TCP/HTTP 实测不封 IP，可放心高频；东财仅用于其独有数据 |\n164|| **东财统一限流防封** | 新增节流入口 `em_get()`，所有东财端点（datacenter/push2/reportapi/search/np-weblist）改用它，内置串行限流（间隔≥1s+随机抖动）+ 会话复用，批量抄代码即自带防封 |\n165|| **东财风控阈值文档化** | SKILL 新增「数据源优先级 & 东财防封」章节，列出触发封禁的阈值（每秒>5/并发≥10/1分≥200/5分≥300）与防封铁律 |\n166|| **财联社快讯下线（#14）** | `cls.cn` 旧 API 全面 404，标注弃用，改用东财全球资讯 |\n167|\n168|---\n169|\n170|## 数据源优先级（V3.2 重排，按封 IP 风险）\n171|\n172|> **原则：行情/K线/实时价/市值/财务能从 mootdx 或腾讯拿到的，一律优先用它们（不封 IP）。东财只用于它独有、别处拿不到的数据，且全部走 `em_get()` 内置限流。**\n173|\n174|| 优先级 | 数据源 | 协议 | 封 IP 风险 | 用途 |\n175||--------|--------|------|-----------|------|\n176|| **1（首选）** | mootdx（通达信） | TCP 7709 | **不封 IP** | K线/五档/逐笔/财务快照/F10 |\n177|| **2（首选）** | 腾讯财经 | HTTP | **不封 IP** | 实时价/PE/PB/市值/换手率/涨跌停/指数/ETF |\n178|| 3 | 同花顺热点/北向 | HTTP | 极低（零鉴权） | 强势股/题材归因/北向资金 |\n179|| 4 | 百度股市通 | HTTP | 极低 | 概念板块/K线 |\n180|| 5 | 新浪财经 | HTTP | 低 | 财报三表 |\n181|| 6 | 巨潮 cninfo | HTTP | 低 | 公告全文 |\n182|| 7 | 同花顺一致预期 | HTTP | 低（需 UA） | EPS 一致预期 |\n183|| 8 | iwencai | OpenAPI | 低（需 Key） | NL 语义搜索 |\n184|| **末位（仅独有数据）** | **东财** datacenter/push2/reportapi/search/np-weblist | HTTP | **中 — 有风控会封 IP** | 龙虎榜/解禁/两融/大宗/股东户数/分红/资金流/研报/个股新闻/全球资讯（已统一走 `em_get()` 限流） |\n185|\n186|> **架构原则：** 除 mootdx（TCP 二进制协议）外，全部直连 HTTP API，零第三方数据封装依赖。**东财系接口有访问频率风控，所有调用统一经 `em_get()` 串行限流防封；批量任务请调大 `EM_MIN_INTERVAL`。**\n187|\n188|---\n189|\n190|## FAQ\n191|\n192|**Q: mootdx 和腾讯有什么区别？**\n193|互补。mootdx = 交易层（价格 + 盘口 + K 线），腾讯 = 估值层（PE / PB / 市值 / 换手率 / 涨跌停价）。两者都不封 IP。\n194|\n195|**Q: 在海外服务器跑，mootdx 超时？**\n196|mootdx 走 TCP 直连通达信行情服务器，需国内 IP 才稳定。海外环境建议走代理或切换到 yfinance。\n197|\n198|**Q: 腾讯 API 字段 43 是 PB 吗？**\n199|不是。43 = 振幅%，46 = PB。网上大量教程写错了，这里是实测校准结果。\n200|\n201|", "total_lines": 465, "file_size": 23991, "truncated": true, "hint": "Use offset=201 to continue reading (showing 1-200 of 465 lines)", 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c0:
  {"content": "1|# Changelog\n2|\n3|## v3.2.1 — 2026-05-30\n4|\n5|### 修复（预先存在的解析 Bug，非 v3.2 引入）\n6|- **§5.1 东财个股新闻 `eastmoney_stock_news`**：东财实际返回里 `result.cmsArticleWebOld` **直接就是文章列表**（非 `{list:[...]}` 嵌套），旧写法 `.get(\"cmsArticleWebOld\", {}).get(\"list\", [])` 对 list 调用 `.get` 触发 `AttributeError` / 返回空 → 改为遍历 `d.get(\"result\", {}).get(\"cmsArticleWebOld\", []) or []`。\n7|- **§6.4 新浪财报三表 `sina_financial_report`**：新浪实际结构是 `result.data.report_list`（按报告期如 `'20260331'` 为键的 dict，每期对象的 `data` 字段才是行项列表 `[{item_title, item_value, item_tongbi}]`），旧写法取 `result.data.{report_type}` **永久返回空** → 改为遍历 `report_list` 期次（倒序），每期从 `data` 按 `item_title` 提取，返回「按报告期记录列表」（`{\"报告期\": ..., \"<科目>\": <值>, \"<科目>_同比\": <同比>}`）。新增 `num` 参数（默认 8 期）。\n8|\n9|### 测试\n10|- 两函数用真实公开 API（茅台 600519，零 key）实测：个股新闻返回 20 条、字段（date/title/content/mediaName/url）齐全；财报三表 lrb/fzb/llb 各返回 8 期、净利润+同比可取。\n11|- 验证方式：exec SKILL.md 代码块本身（含 `em_get` 助手）直连真实 API 断言非空。\n12|\n13|### 说明\n14|- 端点数（27）、数据源数不变；修复来自姊妹项目 astock-peg 移植时实测发现并验证的正确修法。\n15|\n16|## v3.2 — 2026-05-30\n17|\n18|### 新增（数据源优先级 + 东财防封）\n19|- **数据源优先级原则**：新增「数据源优先级 & 东财防封」章节，明确「能用通达信(mootdx)/腾讯（不封 IP）就别用东财，东财仅用于其独有数据」\n20|- **统一节流入口 `em_get()`**：所有东财端点（datacenter / push2 / push2his / reportapi / search-api / np-weblist 共 9 处调用）改用 `em_get()`，内置：\n21|  - 串行限流（`EM_MIN_INTERVAL=1.0s` 最小间隔 + 0.1~0.5s 随机抖动）\n22|  - 复用 `EM_SESSION`（Keep-Alive）+ 默认 UA\n23|  - 批量任务调大 `EM_MIN_INTERVAL` 即进一步降速\n24|- **东财风控阈值文档化**：列出触发封禁的实测阈值（每秒>5 / 并发≥10 / 1分≥200 / 5分≥300）与 5 条防封铁律\n25|\n26|### 修复（失效接口）\n27|- **财联社快讯下线（#14）**：`cls.cn/nodeapi/telegraphList` 等旧接口全面 404（网站迁 Next.js + 新 API 需签名）→ §5.2 标注弃用，全市场快讯改用 §5.3 东财全球资讯（np-weblist）\n28|\n29|### 变更\n30|- 端点数 28 → 27（财联社快讯下线）\n31|- README 数据源优先级表重排：mootdx/腾讯置顶（标注「不封 IP」），东财降至末位（标注「中—有风控会封 IP」）\n32|- 用真实东财 API（datacenter 股东户数 + np-weblist 全球资讯）实测 `em_get` 功能与限流间隔（间隔 ≥1s 通过）\n33|\n34|## v3.1 — 2026-05-19\n35|\n36|### 修复（失效接口替换）\n37|- **百度 PAE 资金流** `fundflow` + `fundsortlist` 已下线（返回 null）→ 替换为东财 push2 分钟级资金流 `eastmoney_fund_flow_minute()`\n38|- **大宗交易** `RPT_DATA_OCCURTRADE` 报表配置已下线 → 替换为 `RPT_DATA_BLOCKTRADE`（字段兼容）\n39|- **龙虎榜机构买卖** `RPT_ORGANIZATION_BUSSINESS` 报表配置已下线 → 改用 BUY/SELL 席位明细筛选 `OPERATEDEPT_CODE=\"0\"`\n40|- **东财全球资讯** 新增必填参数 `req_trace`（UUID），否则返回 403\n41|- **巨潮公告** `stock` 参数格式变更：旧 `\"{code},{plate}\"` → 新 `\"{code},{orgId}\"`（如 `600519,gssh0600519`），`column` 改为空字符串\n42|\n43|### 优化\n44|- 信号层资金流数据源从百度切换到东财 push2，与 Layer 4 资金面统一为东财体系\n45|- 数据源优先级表更新：百度股市通降级为概念板块+K线，资金流功能归入东财 push2\n46|\n47|### 测试\n48|- 28 端点全量实测（2026-05-19），所有端点均通过贵州茅台 600519 验证\n49|- push2 系列 5 个端点在阿里云服务器直连验证通过（本地 Clash 代理可能干扰）\n50|\n51|---\n52|\n53|## v3.0 — 2026-05-17\n54|\n55|### Breaking Changes\n56|- **彻底移除 akshare 依赖**：所有 13 个 akshare 调用替换为直连 HTTP API（东财/新浪/同花顺/财联社源头接口）\n57|- `pip install` 不再需要 `akshare`，依赖缩减为 `mootdx requests pandas stockstats`\n58|- 行业板块数据源从同花顺（401 反爬）切换至东财 push2（`m:90+t:2`，零鉴权）\n59|\n60|### 新增（资金面/筹码层 — Layer 4）\n61|- **融资融券明细** `margin_trading()` — 日级融资余额/买入/偿还 + 融券余额/卖出/偿还\n62|- **大宗交易** `block_trade()` — 成交价/量 + 买卖方营业部 + 溢价率\n63|- **股东户数变化** `holder_num_change()` — 季度股东数 + 环比变化 + 户均持股\n64|- **分红送转历史** `dividend_history()` — 每股派息/送股/转增 + 进度状态\n65|- **个股资金流120日** `stock_fund_flow_120d()` — 主力/大单/中单/小单日级净流入\n66|\n67|### 新增（行情层）\n68|- **百度K线（带MA5/10/20）** `baidu_kline()` — 返回时直接含均价，无需自行计算\n69|- **指数/ETF 实时行情** — 腾讯 API 扩展支持指数代码和 ETF 代码\n70|\n71|### 优化\n72|- 架构从六层升级为**七层**，端点从 20 个增至 **28 个**\n73|- 数据源从 8 个增至 **13 个**（东财 datacenter/push2his/search-api/np-weblist + 新浪 + 财联社 独立计数）\n74|- 新增 `eastmoney_datacenter()` 统一 helper — 龙虎榜/解禁/融资融券/大宗/股东/分红共用\n75|- FAQ 新增 5 条常见问题（akshare 移除原因、行业板块切换、海外部署等）\n76|\n77|### 测试\n78|- 28 端点全量实测（2026-05-17），覆盖主板/中小板/科创板/ST\n79|- 所有新增 Tier 1 端点均通过贵州茅台 600519 验证\n80|\n81|---\n82|\n83|## v2.1 — 2026-05-12\n84|\n85|### 新增\n86|- **龙虎榜席位**：`get_dragon_tiger_board` — 上榜记录 + 买卖席位 TOP5 + 机构动向（akshare 三函数聚合）\n87|- **限售解禁日历**：`get_lockup_expiry` — 历史解禁记录 + 未来 90 天待解禁事件\n88|- **行业横向对比**：`get_industry_comparison` — 同花顺 90 行业涨跌幅排名 + 成交额 + 净流入 + 领涨股\n89|- **百度股市通概念板块**：`get_concept_blocks` — 行业/概念/地域三维板块归属 + 当日涨跌幅\n90|- **百度股市通资金流向**：`get_fund_flow` — 主力/散户/超大单/大单分钟级流向 + 20 日历史\n91|- 架构端点从 15 个增至 **20 个**，数据源从 7 个增至 **8 个**\n92|\n93|### 优化\n94|- **北向资金自缓存**：eastmoney 全系北向数据 2024-08 起断供，改为本地 CSV 自缓存模式（每次调用自动积累）\n95|- **F10 股东研究截断**：【4.股东变化】只保留最新一期，19969→5906 chars（**-70% token 消耗**）\n96|- **百度 PAE ResultCode 修复**：返回类型 int/string 不稳定，统一 `str()` 比较\n97|\n98|### 测试\n99|- 17 接口全量实测，覆盖主板/中小板/科创板/ST 四类股票\n100|- 50 OK / 1 预期 WARN（ST 无机构覆盖）/ 0 FAIL\n101|\n102|---\n103|\n104|## v2.0 — 2026-05-11\n105|\n106|首次开源发布。\n107|\n108|### 新增\n109|- **信号层**：同花顺热点（当日强势股 + 题材归因 reason tags）\n110|- **信号层**：同花顺北向资金（hsgtApi 实时分钟 + 历史日级）\n111|- 架构从五层升级为**六层**，端点从 13 个增至 **15 个**\n112|\n113|### 包含\n114|- 行情层：mootdx K 线 + 盘口 + 逐笔 / 腾讯财经 PE·PB·市值\n115|- 研报层：东财 reportapi + PDF / akshare 一致预期 / iwencai NL 搜索\n116|- 新闻层：个股新闻 / 财联社快讯 / 全球资讯\n117|- 基础数据：季报 37 字段 / F10 九大类 / 个股基本面\n118|- 公告层：巨潮全量公告 / F10 最新提示\n119|- 4 套调研流程：单票估值 / 批量对比 / 主题研报 / 新标的调研\n120|- 估值框架：前向 PE / PE 消化 / PEG / 30x 锚点\n121|\n122|---\n123|\n124|## v1.0 — 2026-04\n125|\n126|内部版本（未开源）。\n127|\n128|- 五层架构 · 13 端点\n129|- 行情 / 研报 / 新闻 / 基础数据 / 公告\n130|", "total_lines": 129, "file_size": 7876, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|---\n2|name: a-stock-data\n3|description: A股全栈数据工具包 — 覆盖行情(mootdx+腾讯+百度K线)、研报(东财+同花顺+iwencai)、信号(同花顺热点+北向+龙虎榜+解禁+行业)、资金面(融资融券+大宗交易+股东户数+分红+资金流分钟级+资金流120日)、新闻(东财个股+全球资讯)、基础数据(mootdx财务/F10+东财+新浪三表)、公告(巨潮)七层数据源，内嵌全部调用代码，自包含零依赖外部文件。优先用通达信(mootdx)/腾讯(不封IP)，东财接口已内置限流防封。适用于个股估值、研报检索、题材归因、龙虎榜跟踪、解禁预警、行业轮动、融资融券跟踪、筹码分析、产业链调研、批量筛选等场景。\n4|origin: custom\n5|version: 3.2.1\n6|---\n7|\n8|> 📦 项目主页：https://github.com/simonlin1212/a-stock-data — 更新、反馈、支持作者\n9|> \n10|> 作者：Simon 林 · 抖音「Simon林」· 公众号「硅基世纪」\n11|\n12|# A股全栈数据工具包 V3.2.1\n13|\n14|七层数据架构，27 个端点实测可用（2026-05 验证；财联社快讯已下线，详见 §5.2），覆盖主板/中小板/科创板/ST。\n15|\n16|> **V3.2.1（Bug 修复）：** 修复两个内嵌函数的解析逻辑（预先存在，非 V3.2 引入）——\n17|> - **§5.1 东财个股新闻**：东财实际返回里 `result.cmsArticleWebOld` 直接就是文章列表，旧写法对 list 调 `.get(\"list\")` 触发 AttributeError / 返回空 → 改为遍历 `cmsArticleWebOld` 列表本身。\n18|> - **§6.4 新浪财报三表**：新浪实际结构是 `result.data.report_list`（按报告期为键的 dict，每期 `data` 才是行项列表），旧写法取 `result.data.{lrb}` 永久返回空 → 改为遍历 `report_list` 期次、从每期 `data` 按 `item_title` 提取。\n19|> - 两函数均用茅台 600519 公开 API（零 key）实测返回非空、字段正确。\n20|>\n21|> **V3.2（防封 + 失效修复）：**\n22|> - **数据源优先级 + 东财防封**：明确「通达信(mootdx)/腾讯不封IP 优先用，东财仅用于其独有数据」原则；新增统一节流入口 `em_get()`，所有东财接口内置串行限流（间隔≥1s+随机抖动）+ 会话复用，AI 抄代码即自带防封。详见「数据源优先级 & 东财防封」章节。\n23|> - **财联社快讯下线（#14）**：`cls.cn` 旧 API 全面 404，标注弃用并改用东财全球资讯。\n24|>\n25|> **V3.1 修复：** 替换 4 个失效接口（百度 PAE 资金流→东财 push2、大宗交易 RPT 报表名更新、机构席位改用 BUY/SELL 明细筛选）+ 修复东财全球资讯 req_trace 参数 + 修复巨潮公告 orgId 格式。\n26|>\n27|> **V3.0 Breaking Change**：彻底移除 akshare 依赖，所有数据源改为直连 HTTP API（零第三方数据依赖，仅 mootdx 保留 TCP）。\n28|\n29|**使用方式：** 将本文件放入 `~/.claude/skills/a-stock-data/SKILL.md`，Claude Code 会自动识别并在 A 股相关对话中激活。\n30|\n31|```\n32|行情层（实时，不封IP）\n33|├── mootdx        → K线 + 五档盘口 + 逐笔成交 (TCP 7709)\n34|├── 腾讯财经 API   → PE/PB/市值/换手率/涨跌停/指数/ETF (HTTP)\n35|└── 百度股市通     → K线带MA5/10/20 (V3.0 新增，HTTP)\n36|\n37|研报层\n38|├── 东财 reportapi → 研报列表 + PDF下载 + 评级 + 三年EPS\n39|├── 同花顺 THS     → 一致预期EPS (直连 basic.10jqka.com.cn)\n40|└── iwencai        → NL语义搜索研报 (唯一能力，需X-Claw)\n41|\n42|信号层\n43|├── 同花顺热点     → 当日强势股 + 题材归因 reason tags (零鉴权 73ms)\n44|├── 同花顺北向     → hgt/sgt 分钟资金流向 + 本地自缓存历史\n45|├── 百度股市通     → 概念板块归属 (HTTP)\n46|├── 东财 push2     → 个股资金流向 分钟级 (V3.1 替换百度PAE)\n47|├── 龙虎榜席位     → 上榜记录 + 买卖席位 TOP5 + 机构动向 (datacenter-web)\n48|├── 全市场龙虎榜   → 每日全市场上榜股票 + 净买额排名 (datacenter-web)\n49|├── 限售解禁日历   → 历史解禁 + 未来90天待解禁 (datacenter-web)\n50|└── 行业板块排名   → 东财行业涨跌/上涨下跌家数 (V3.0 替换同花顺)\n51|\n52|资金面 / 筹码层\n53|├── 融资融券明细   → 日级融资余额/买入/偿还 + 融券 (datacenter-web)\n54|├── 大宗交易       → 成交价/量 + 买卖方营业部 (datacenter-web)\n55|├── 股东户数变化   → 季度股东户数 + 环比变化 (datacenter-web)\n56|├── 分红送转       → 历史每股派息/送股/转增 (datacenter-web)\n57|└── 个股资金流120日 → 主力/大单/中单/小单 日级净流入 (push2his)\n58|\n59|新闻层\n60|├── 东财个股新闻   → 个股相关新闻 (search-api-web JSONP)\n61|├── 财联社快讯     → ⚠️ 已下线 (cls.cn 迁 Next.js，旧API 404)\n62|└── 东财全球资讯   → 7×24 财经快讯 (np-weblist，财联社替代)\n63|\n64|基础数据层\n65|├── mootdx finance → 季报快照 (37字段, EPS/ROE/净利)\n66|├── mootdx F10     → 公司资料 (9大类文本)\n67|├── 东财个股信息   → 行业/总股本/流通股/市值/上市日期 (push2)\n68|└── 新浪财报三表   → 资产负债表/利润表/现金流量表 (quotes.sina.cn)\n69|\n70|公告层\n71|├── 巨潮 cninfo    → 公告全文检索+下载 (cninfo.com.cn)\n72|└── mootdx F10     → 最新公告摘要\n73|```\n74|\n75|## 数据源优先级 & 东财防封（重要，先读）\n76|\n77|### 优先级原则：能用通达信/腾讯，就别用东财\n78|\n79|| 优先级 | 数据源 | 协议 | 封 IP 风险 | 覆盖 |\n80||--------|--------|------|-----------|------|\n81|| **1（首选）** | **mootdx（通达信）** | TCP 7709 二进制 | **不封 IP** | K线、五档盘口、逐笔成交、财务快照、F10 |\n82|| **2** | **腾讯财经** | HTTP GBK | **不封 IP** | 实时价、PE/PB/市值/换手率/涨跌停、指数、ETF |\n83|| **3** | 新浪 / 巨潮 / 同花顺 | HTTP | 低 | 财报三表、公告、一致预期/热点 |\n84|| **4（仅独有数据才用）** | **东财 eastmoney** | HTTP | **有风控，会封 IP** | 见下 |\n85|\n86|**凡是行情 / K线 / 实时价 / 市值 / 财务三表能从 mootdx 或腾讯拿到的，一律走它们**——TCP 协议和腾讯接口实测不封 IP，可放心高频调用。\n87|\n88|### 东财只用于它「独有、别处拿不到」的数据\n89|\n90|下列数据**只有东财有**，通达信/腾讯/新浪都没有，必须用东财（但要限流）：\n91|\n92|> 龙虎榜席位 · 全市场龙虎榜 · 限售解禁日历 · 融资融券 · 大宗交易 · 股东户数 · 分红送转 · 个股资金流向（分钟/日级）· 行业板块排名 · 研报列表/PDF · 个股新闻 · 全球资讯\n93|\n94|### 东财风控阈值（社区实测，2026-05）\n95|\n96|| 行为 | 触发封禁的阈值 | 风险 |\n97||------|---------------|------|\n98|| 每秒请求数 | > 5 次/秒 | 高 |\n99|| 单 IP 并发连接 | ≥ 10 | 高 |\n100|| 1 分钟请求总数 | ≥ 200 次 | 中高 |\n101|| 5 分钟请求总数 | ≥ 300 次 | 触发封禁 |\n102|| User-Agent | 空 UA / 无浏览器特征 | 中 |\n103|\n104|被封表现：连续请求后 `403` / `429` / 连接超时 / 返回空数据。临时封禁通常几分钟到几小时。\n105|\n106|### 防封铁律（调用东财时必须遵守）\n107|\n108|1. **串行，不并发**——绝不对东财开多线程/协程并发请求\n109|2. **每次间隔 ≥ 1 秒 + 随机抖动**（QPS ≤ 2），批量筛选时调大到 1.5~2 秒\n110|3. **复用 HTTP 会话**（Keep-Alive），不要每次新建连接\n111|4. **带正常 UA + Referer**（本 SKILL 各端点已配好）\n112|5. **批量场景每只股票之间 sleep**——AI 跑批量循环（如筛选 100 只股逐个拉龙虎榜/资金流）是被封的头号元凶\n113|\n114|### 已内置限流：所有东财请求走 `em_get()`\n115|\n116|本 SKILL 提供统一的节流入口 `em_get()`（定义见下方「东财数据中心统一查询（共用 helper）」），它自动做到：串行限流（最小间隔 `EM_MIN_INTERVAL=1.0s` + 随机抖动）+ 复用 `EM_SESSION`（Keep-Alive）+ 默认 UA。**所有 `eastmoney.com` 端点的代码块都已改用 `em_get` 而非裸 `requests.get`**，AI 直接抄代码即自带防封。批量任务把 `EM_MIN_INTERVAL` 调大即可进一步降速。\n117|\n118|> 注：`em_get` / `EM_SESSION` / `EM_MIN_INTERVAL` 是所有东财代码块共用的前置定义，使用任一东财端点前需先执行「共用 helper」代码块。\n119|\n120|---\n121|\n122|## When to Activate\n123|\n124|- 用户要查 A 股个股估值（一致预期 / PE / PEG / PE消化）\n125|- 用户要拉实时行情（价格 / 五档盘口 / K线 / 涨跌停价）\n126|- 用户要搜研报（按主题 / 按标的 / 按行业 / 下载PDF）\n127|- 用户要看**当日强势股 / 题材归因 / 概念热点**\n128|- 用户要看**北向资金动向**（沪股通/深股通分钟流向）\n129|- 用户要看**概念板块归属**（行业/概念/地域）\n130|- 用户要看**个股资金流向**（主力/散户/超大单/大单分钟级）\n131|- 用户要看**龙虎榜席位**（营业部 + 机构买卖）\n132|- 用户要看**全市场龙虎榜**（当日所有上榜股票 + 净买额排名）\n133|- 用户要看**限售解禁日历**（历史解禁 + 未来待解禁）\n134|- 用户要做**行业横向对比**（涨跌排名 / 资金流入 / 领涨股）\n135|- 用户要看**融资融券 / 两融数据**（融资余额 + 融券余额）\n136|- 用户要看**大宗交易**（成交价/量 + 买卖方营业部）\n137|- 用户要看**股东户数变化**（筹码集中度）\n138|- 用户要看**分红送转历史**（每股派息 + 送股 + 转增）\n139|- 用户要看**指数/ETF行情**（上证指数 / 沪深300 / 创业板指 / ETF）\n140|- 用户要看新闻资讯（个股新闻 / 财联社快讯 / 全球资讯）\n141|- 用户要查公告（巨潮公告全文）\n142|- 用户要做产业链调研 / 批量横向对比\n143|- 关键词：估值、一致预期、机构预测、市盈率、PEG、市值、研报、产业链、行业研究、K线、盘口、公告、新闻、**强势股、题材、热点、概念归因、北向资金、沪股通、深股通、概念板块、资金流向、主力、龙虎榜、席位、营业部、全市场龙虎榜、净买入、解禁、限售、行业对比、行业轮动、融资融券、两融、大宗交易、股东户数、筹码集中、分红、派息、送股、指数、ETF**\n144|\n145|---\n146|\n147|## Prerequisites\n148|\n149|```bash\n150|pip install mootdx requests pandas stockstats\n151|```\n152|\n153|| 依赖 | 版本要求 | 用途 |\n154||------|---------|------|\n155|| mootdx | >= 0.10 | TCP行情+财务+F10（唯一非HTTP依赖） |\n156|| requests | any | 所有HTTP API直连 |\n157|| pandas | any | 数据处理+HTML表格解析 |\n158|| stockstats | any | 技术指标计算（RSI/MACD/BOLL等） |\n159|\n160|> **V3.0 架构：** 除 mootdx（TCP 二进制协议）外，所有数据源均为直连 HTTP API，零第三方数据封装依赖。每个端点的底层 URL/参数完全暴露，方便调试和定制。\n161|\n162|### iwencai API Key（仅语义搜索需要）\n163|\n164|```bash\n165|# 环境变量方式\n166|export IWENCAI_API_KEY=\"your_key_here\"\n167|export IWENCAI_BASE_URL=\"https://openapi.iwencai.com\"\n168|\n169|# 申请地址: https://www.iwencai.com/skillhub\n170|# 注册后安装 SkillHub CLI，再安装 report-search 技能即可获得 Key\n171|```\n172|\n173|其他数据源（mootdx / 腾讯 / 东财 / 同花顺 / 百度股市通 / 新浪 / 巨潮）全部免费，无需 key。\n174|\n175|### 市场前缀规则（全局通用）\n176|\n177|```python\n178|def get_prefix(code: str) -> str:\n179|    \"\"\"6位代码 → 市场前缀\"\"\"\n180|    if code.startswith((\"6\", \"9\")):\n181|        return \"sh\"\n182|    elif code.startswith(\"8\"):\n183|        return \"bj\"\n184|    else:\n185|        return \"sz\"\n186|```\n187|\n188|### Ticker 格式归一化\n189|\n190|所有接口统一支持多种输入格式，内部归一化为纯 6 位数字：\n191|\n192|| 输入 | 归一化结果 |\n193||------|-----------|\n194|| `688017` | `688017` |\n195|| `SH688017` / `sh688017` | `688017` |\n196|| `688017.SH` / `688017.sh` | `688017` |\n197|| `SZ000001` | `000001` |\n198|| `BJ832000` | `832000` |\n199|\n200|### 东财数据中心统一查询（共用 helper）\n201|", "total_lines": 2090, "file_size": 78160, "truncated": true, "hint": "Use offset=201 to continue reading (showing 1-200 of 2090 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  Resumo da análise do repositório **simonlin1212/a-stock-data**:

  - Tema principal: toolkit “full-stack” de dados para ações chinesas A-share, empacotado principalmente como um arquivo **SKILL.md** auto-contido para ser injetado em assistentes de código/IA (Claude Code, Codex, OpenClaw).
  - Problema resolvido: acessar dados de 13 fontes chinesas exigia wrappers complexos e frágeis (akshare, mootdx, iwencai com headers específicos, mudanças de API etc.). O projeto elimina dependências de pacotes terceiros de dados, conectando diretamente às APIs HTTP/TCP originais e já lida com prevenção de bloqueio de IP pelos endpoints de leste-europeu.
  - Razão do hype: 
    - **Zero terceirização de dados**: remove akshare, com dependências mínimas (mootdx, requests, pandas, stockstats).
    - **Pronto para agentes de IA**: SKILL.md com código Python inline, ativação contextual, exemplos e fluxos prontos.
    - **Mitigação de bloqueio**: prioriza fontes estáveis+mootdx/腾讯 e introduz limitador série comum `em_get()` para dados de leste-europeu.
  - Tecnologias:
    - **mootdx** (TCP 7709 direto, só aqui para preservar protocolo)
    - **HTTP direto** (腾讯, 同花顺, 东财, 百度股市通, 新浪, 巨潮)
    - **Python** (requests, pandas, stockstats)
    - Somente um endpoint opcional pago/key: **iwencai** (OpenAPI)

  Sem prosseguir para leitura adicional do código, porque o repositório é basicamente uma documentação estruturada em markdown com blocos de código; os achados acima já respondem aos 4 elementos pedidos.  


c0:
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c0:
  {"output": "total 185\ndrwx------ 21 ice  ice    26 May 30 22:53 .\ndrwxr-xr-x  3 root root    3 May 30 20:40 ..\n-rw-------  1 ice  ice    86 May 30 20:40 .bash_history\n-rw-r--r--  1 ice  ice   220 May 30 20:40 .bash_logout\n-rw-r--r--  1 ice  ice  3640 May 30 20:41 .bashrc\ndrwxrwxr-x  5 ice  ice     5 May 30 20:51 .cache\ndrwxrwxr-x  3 ice  ice     5 May 30 21:45 .cloakbrowser\ndrwxrwxr-x  5 ice  ice     5 May 30 20:44 .config\ndrwx------ 17 ice  ice    36 May 30 22:54 .hermes\ndrwxrwxr-x  5 ice  ice     5 May 30 20:45 .local\ndrwxrwxr-x  5 ice  ice     6 May 30 20:41 .npm\n-rw-r--r--  1 ice  ice   833 May 30 20:41 .profile\n-rw-rw-r--  1 ice  ice    26 May 30 20:41 .zshrc\ndrwxrwxr-x 10 ice  ice    20 May 30 22:51 CodexPlusPlus\ndrwxrwxr-x 14 ice  ice    48 May 30 22:53 GuJumpgate\ndrwxrwxr-x 18 ice  ice    40 May 30 22:51 OrcaSlicer-bambulab\ndrwxrwxr-x 11 ice  ice    22 May 30 22:53 SkillOpt\ndrwxrwxr-x  8 ice  ice    16 May 30 22:52 bumblebee\ndrwxrwxr-x  4 ice  ice     6 May 30 22:52 dirtyfrag", "exit_code": 0, "error": null} terminal 


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c0:
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c0:
  {"output": "total 130\ndrwxrwxr-x 13 ice ice    20 May 30 22:54 .\ndrwx------ 23 ice ice    28 May 30 22:54 ..\ndrwxrwxr-x  8 ice ice    13 May 30 22:54 .git\n-rw-rw-r--  1 ice ice   927 May 30 22:54 .gitignore\n-rw-rw-r--  1 ice ice 26191 May 30 22:54 CHANGELOG.md\n-rw-rw-r--  1 ice ice  1086 May 30 22:54 LICENSE\n-rw-rw-r--  1 ice ice  6945 May 30 22:54 README.md\n-rw-rw-r--  1 ice ice 16252 May 30 22:54 SKILL.md\ndrwxrwxr-x  5 ice ice     5 May 30 22:54 adapters\ndrwxrwxr-x  2 ice ice     4 May 30 22:54 docs\ndrwxrwxr-x  2 ice ice     3 May 30 22:54 examples\ndrwxrwxr-x  2 ice ice     8 May 30 22:54 hooks\n-rwxrwxr-x  1 ice ice  7810 May 30 22:54 install.sh\ndrwxrwxr-x  2 ice ice     7 May 30 22:54 migrations\ndrwxrwxr-x  2 ice ice    11 May 30 22:54 shared-references\ndrwxrwxr-x 17 ice ice    17 May 30 22:54 skills\ndrwxrwxr-x  2 ice ice     4 May 30 22:54 starter-rubrics\ndrwxrwxr-x  2 ice ice    14 May 30 22:54 templates\ndrwxrwxr-x  2 ice ice     5 May 30 22:54 tools\n-rwxrwxr-x  1 ice ice  2725 May 30 22:54 uninstall.sh", "exit_code": 0, "error": null} terminal 


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c0:
  {"content": "1|<h1 align=\"center\">\n2|  <img src=\"docs/logo.svg\" alt=\"Cheat on Content\" width=\"720\">\n3|</h1>\n4|\n5|<h2 align=\"center\">Cheat on Content</h2>\n6|\n7|<p align=\"center\">\n8|For content creators — a skill that turns every post into a calibrated experiment.\n9|</p>\n10|\n11|<p align=\"center\">\n12|You're reading this. The skill predicted it.<br>\n13|It turns every \"I feel this will go viral\" into a calibrated experiment.<br>\n14|It took me from zero to 1M followers in a month. It said I'd write this. I did.<br>\n15|Your doubt — predicted too.\n16|</p>\n17|\n18|<p align=\"center\">\n19|  <strong>English</strong>\n20|  &nbsp;·&nbsp;\n21|  <a href=\"docs/README_CN.md\"><strong>简体中文</strong></a>\n22|</p>\n23|\n24|<p align=\"center\">\n25|<a href=\"CHANGELOG.md\"><img src=\"https://img.shields.io/badge/version-v0.1.0-orange\" alt=\"Version\"></a>\n26|&nbsp;\n27|<a href=\"LICENSE\"><img src=\"https://img.shields.io/badge/license-MIT-blue.svg\" alt=\"License\"></a>\n28|</p>\n29|\n30|---\n31|\n32|## 🎬 What it actually does\n33|\n34|Most creators live in the same gambling loop:\n35|\n36|> Publish → Numbers come in → Learn nothing → Roll the dice again\n37|\n38|A creator who's shipped 200 pieces is barely 10% sharper than someone who's shipped 1 — because they never **kept books** after each round.\n39|\n40|**Cheat on Content** makes every judgment get logged, retrospected, absorbed into the next:\n41|\n42|📊 Score → 🎯 Blind-predict → 🚀 Publish → 📈 T+3d retro → 🧬 Evolve your rubric\n43|\n44|This isn't motivation. It's **compounding** — every piece you don't retro is silently eroding your ability to see yourself.\n45|\n46|One month in = you have a hit-formula that's **only yours**.\n47|Three months in = you're 10× sharper than your first-day self.\n48|\n49|---\n50|\n51|## 🌀 Origin\n52|\n53|> I never believed in fate. Until this skill made me film a video — and predicted exactly how much traffic that video would pull.\n54|>\n55|> I tried to break it. I told my audience. I hoped collective observation would collapse the wave function and shift the trajectory.\n56|>\n57|> The data was accurate.\n58|>\n59|> I didn't escape fate. I just moved from first-order to second-order.\n60|>\n61|> If even my awakening — even my audience's observation — was already in its prediction, then right now, reading this:\n62|> are you here out of curiosity, or just closing the algorithm's last move?\n63|>\n64|> — *the creator*\n65|\n66|---\n67|\n68|## ⚖️ How it differs from other \"creator tools\"\n69|\n70|| Others | This |\n71||---|---|\n72|| Give you \"inspiration\" | Make **your own intuition** measurable |\n73|| AI writes for you | AI **judges** for you — the script stays yours |\n74|| Ship 10 versions, A/B test | Ship one — **bet** in writing, settle the books with data |\n75|| Static dashboard | An **evolving rubric** — your formula 3 months from now isn't the starting one |\n76|\n77|In a sentence: other tools help you \"ship more.\" This helps you \"judge sharper.\"\n78|\n79|---\n80|\n81|## 🤔 Can't I just use ChatGPT / DeepSeek / Doubao?\n82|\n83|Those are **general assistants** — they tell everyone the same thing. You ask \"will this go viral?\" and the answer is fitted to global average opinion, not your channel. Ask again tomorrow — same answer. **It doesn't remember you. It doesn't change because of you.**\n84|\n85|This is **your own ops expert** — serving only your one channel:\n86|\n87|- The scoring formula is reverse-engineered from **your** history, not the global training distribution\n88|- Every piece you ship updates its understanding — by month three, judgment accuracy is 10× sharper than day one (**auto-evolving**)\n89|- It knows your benchmark account, your cadence, the last three reasons you flopped — things ChatGPT forgets after the first reply\n90|\n91|General LLMs help everyone. This helps **your** account.\n92|\n93|---\n94|\n95|## 🛡️ Why the loop actually evolves\n96|\n97|📝 **Every piece is logged**: Score and prediction get written before publish, archived end-to-end. Three days later you settle accounts — you see exactly where you were sharp, where you were off. No more vague \"I feel this one didn't land.\"\n98|\n99|🔁 **It gets sharper**: Three same-direction misses in a row, the tool actively prompts you to upgrade your scoring formula. **You don't have to remember — it remembers for you.**\n100|\n101|🛡️ **Upgrades have a brake**: Switching the formula requires re-scoring all historical samples — only released if it ranks more accurately than the old. Plus a cross-model independent audit — **so you can't fool yourself.**\n102|\n103|🪒 **The rubric is a workbench, not a museum**: Observations refuted by data get deleted; observations absorbed into formal dimensions also get deleted. It only holds what's most useful right now.\n104|\n105|---\n106|\n107|## 📦 Install\n108|\n109|```bash\n110|git clone https://github.com/XBuilderLAB/cheat-on-content.git\n111|cd cheat-on-content\n112|bash install.sh\n113|```\n114|\n115|> ⚠️ **Upgrading from v0.x?** Run `/cheat-migrate` in your content project after `git pull`. The 1.3 → 1.4 migration is **BREAKING for blind-channel integrity** — it splits `rubric_notes.md` so the blind sub-agent can't leak actuals. Without migrate, blind scoring will keep flagging `non_blind_warning`. See [CHANGELOG](CHANGELOG.md) and [migrations/1.3-to-1.4.md](migrations/1.3-to-1.4.md).\n116|\n117|14 sub-skills are symlinked into your agent's skill directory. One install, every content project gets it.\n118|\n119|**Supported agents**: Claude Code (default) · Codex (`bash install.sh --codex`) · Both (`bash install.sh --all`)\n120|\n121|> Frozen version: `bash install.sh --copy` / `bash install.sh --codex --copy`\n122|>\n123|> Uninstall: `bash uninstall.sh` / `bash uninstall.sh --codex` (your content data is not touched)\n124|\n125|---\n126|\n127|## 🚀 First run\n128|\n129|In your content project directory, open a skill-compatible agent and say:\n130|\n131|```\n132|初始化 cheat-on-content\n133|```\n134|\n135|(or `init cheat-on-content`)\n136|\n137|Five yes/no questions complete onboarding. **Strongly recommend importing a benchmark account** — 5–10 samples and the tool gets an anchor immediately. Without one, your first 5 predictions land at ±50% precision.\n138|\n139|---\n140|\n141|## ⚡ Daily use\n142|\n143|```\n144|score this scripts/<...>.md       → grade only\n145|start prediction scripts/<...>.md → blind prediction + decision log\n146|shot scripts/<...>.md             → create video folder + buffer +1\n147|shipped https://...                → buffer -1\n148|retro videos/<...>/                → T+3d data + retrospective\n149|status / fetch trends / find topic / bump rubric / find benchmark\n150|```\n151|\n152|Hook-aware agents auto-report buffer + pending retros + top candidates at every session start — no need to ask. Other agents: just say `status`.\n153|\n154|Full workflow + sub-skill details: see [SKILL.md](SKILL.md).\n155|\n156|---\n157|\n158|## 📈 Star History\n159|\n160|<a href=\"https://star-history.com/#XBuilderLAB/cheat-on-content&Date\">\n161|  <img src=\"https://api.star-history.com/svg?repos=XBuilderLAB/cheat-on-content&type=Date\" alt=\"Star History Chart\" width=\"720\">\n162|</a>\n163|\n164|---\n165|\n166|## 📜 License\n167|\n168|MIT. Commercial use, modification, closed-source integration — all fine.\n169|\n170|---\n171|\n172|*Is this cheating? So was the calculator. So was Google.*\n173|*The future doesn't reward effort — it rewards those who see the pattern first.*\n174|\n175|*You reading this line — that's predicted too.*\n176|", "total_lines": 175, "file_size": 6945, "truncated": false, "is_binary": false, "is_image": false} read_file 


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  {"content": "1|---\n2|name: cheat-on-content\n3|description: 给所有想把\"感觉\"变成可校准预测的内容创作者。**方法论通用**——打分 → 盲预测 → T+3d 复盘 → 进化 rubric 的循环适用任何能被量化（播放 / 阅读 / 收听 / 点击）的内容。**rubric 是循环的内容，不是循环本身**——当前内置一份观点视频 rubric（参考博主 25+ 视频拟合），其他形态可借这套起步并 bump 调权重。**强烈建议导入对标账号**作为初始信号源（/cheat-learn-from）。触发词：\"初始化\"/\"打分这篇\"/\"启动预测\"/\"已发布\"/\"复盘\"/\"升级 rubric\"/\"推荐选题\"/\"抓热点\"/\"状态\"/\"找对标\"/\"learn from\"。**首次使用必须先跑 /cheat-init。**\n4|argument-hint: [draft-path] [— mode: cold-start|calibration]\n5|allowed-tools: Bash(*), Read, Write, Edit, Grep, Glob, Skill, mcp__llm-chat__chat\n6|---\n7|\n8|# 网红作弊器 / Cheat on Content\n9|\n10|> 🎯 **方法论通用，rubric 当前内置观点视频版**\n11|>\n12|> **方法论**（5 阶段闭环）：任何能被量化的内容形态都适用——视频 / 文章 / 播客 / Newsletter / 短文 thread。\n13|>\n14|> **当前内置 rubric**：观点类视频（评论 / 时评 / 论说 / 议题讨论 / 个人观点表达），7 个维度由参考博主 25+ 已发样本拟合而来。如果你做其他形态，需要：\n15|> - 自己写一份 rubric（参照 [starter-rubrics/opinion-video-zero.md](starter-rubrics/opinion-video-zero.md) 的格式）\n16|> - 或等内置扩展（长文 / 短文 / 播客 starter 在批次 3 路线图）\n17|>\n18|> 默认假设：**用户是从零开始的新人**（一条视频都没发过）——cold-start 期的预测会**简化**，只要 7 维打分 + 一句话 bet，不强求 bucket 数字（避免 false precision）。已有 5+ 篇数据的老手走 calibration 模式解锁完整 7 组件预测。\n19|\n20|把内容创作变成可校准预测循环：**打分 → 预测 → 发布 → 复盘 → 进化 rubric**。\n21|\n22|本文件是**总协议 + 路由器**。具体每个阶段的工作流在 `skills/cheat-*/SKILL.md` 各子 skill 里。\n23|\n24|## Codex compatibility\n25|\n26|Codex 没有 Claude Code 的 slash-command harness。安装到 Codex 后，按自然语言触发同一套路由即可：\n27|\n28|- `初始化 cheat-on-content` → 读取并执行 `skills/cheat-init/SKILL.md`\n29|- `打分这篇 scripts/foo.md` → 读取并执行 `skills/cheat-score/SKILL.md`\n30|- `启动预测 scripts/foo.md` → 读取并执行 `skills/cheat-predict/SKILL.md`\n31|- `拍了 ...` / `已发布 ...` / `复盘 ...` / `升级 rubric` / `状态` → 分别读取对应 `skills/cheat-*/SKILL.md`\n32|\n33|执行时遵循本文件的三条原则和路由表；不要依赖 `/cheat-*` 命令是否存在。Claude Code 专用 hook（`.claude/settings.json`）仍只在 Claude Code 里自动触发；Codex 中需要用户主动说 `状态` 查看 buffer、待复盘和候选池。\n34|\n35|---\n36|\n37|## 三条不可妥协原则\n38|\n39|任何一条被违反，整个校准循环退化为\"凭直觉的自我安慰\"。如果用户要求打破其中任何一条，**拒绝执行并说明原因**。\n40|\n41|1. **盲预测（Blind prediction）**：预测必须在看到任何实际数据**之前**写完。一旦写完，`## 预测` 段是 immutable——只能往 `## 复盘` 段追加。完整规范：[shared-references/blind-prediction-protocol.md](shared-references/blind-prediction-protocol.md)。**hooks/prediction-immutability.sh 在 harness 层强制执行**。\n42|\n43|2. **升级 = 全量重打（Bump = full re-score）**：rubric 升级时，校准池所有有实绩数据的样本必须用新公式重打分；新排序与实际表现排序若在 ≥4/5 样本上不一致，升级被拒；升级必须经跨模型独立审核。完整规范：[shared-references/bump-validation-protocol.md](shared-references/bump-validation-protocol.md)。\n44|\n45|3. **rubric 是工作台，不是博物馆**：被新数据推翻或被吸收为正式维度的观察，**删掉**。绝不留\"我曾经以为 X，但其实...\"的考古层。git history 才是档案。完整规范：[shared-references/observation-lifecycle.md](shared-references/observation-lifecycle.md)。\n46|\n47|---\n48|\n49|## 路由表（触发词 → 子 skill）\n50|\n51|| 用户说 | 调用 | 前置条件 |\n52||---|---|---|\n53|| \"初始化\" / \"init\" / \"首次使用\" | `/cheat-init` | 无（这是入口） |\n54|| \"找对标\" / \"学这个账号\" / \"拆这几个对标视频\" / \"learn from\" / \"导入对标账号\" | `/cheat-learn-from` | 已 init；cold-start 强烈建议；后续可随时 --append / --replace |\n55|| \"找选题\" / \"我不知道拍什么\" / \"seed\" / \"找前 5 个选题\" | `/cheat-seed` | 已 init（cold-start 用户专用一次性种子动作） |\n56|| \"打分这篇 [path]\" / \"score this [path]\" | `/cheat-score` | rubric_notes.md 存在 |\n57|| \"启动预测\" / \"start prediction\" / \"给这稿子打分并预测\" | `/cheat-predict` | 已 init + 有最终稿 |\n58|| \"拍了 X\" / \"shot it\" / \"录完了\" | `/cheat-shoot` | 对应预测已写（buffer +1） |\n59|| \"已发布\" / \"I shipped it\" / \"发布链接是 X\" | `/cheat-publish` | 对应预测文件存在（buffer -1） |\n60|| \"复盘\" / \"retro this\" / \"T+3d 数据来了\" | `/cheat-retro` | 对应预测文件存在 + 已过 RETRO_WINDOW_DAYS |\n61|| \"构造受众画像\" / \"更新 persona\" / \"我的观众是谁\" / \"build persona\" | `/cheat-persona` | 已 init；有复盘评论数据（或 benchmark seed） |\n62|| \"升级 rubric\" / \"bump rubric\" / \"更新公式\" | `/cheat-bump` | 校准池 ≥ MIN_SAMPLES_FOR_BUMP |\n63|| \"推荐选题\" / \"next topic\" | `/cheat-recommend` | candidates.md 存在且非空 |\n64|| \"抓热点\" / \"fetch trends\" / \"今天有什么可做的\" | `/cheat-trends` | trend-sources adapter 已配置（日常补充候选池） |\n65|| \"状态\" / \"status\" / \"看板\" | `/cheat-status` | 任意时刻可调 |\n66|| \"迁移\" / \"升级 state\" / \"schema 版本不对\" / \"migrate\" | `/cheat-migrate` | 已 init；用户 git pull 拉了新版后；SessionStart hook 提示 schema mismatch 后 |\n67|\n68|> 拍 vs 发分两个动作：buffer 警戒系统需要明确知道\"拍了但没发\"vs\"已发\"两种状态。详见 [shared-references/cadence-protocol.md](shared-references/cadence-protocol.md)。\n69|\n70|**Mode detection**（首次接到非 init 触发词时执行）：\n71|1. 检查用户当前目录是否有 `.cheat-state.json` → 没有 → 强制路由到 `/cheat-init`\n72|2. 检查 `predictions/` 下有几个文件含完整 `## 复盘` 段填了真实数据 → 决定 `mode: cold-start | calibration`\n73|3. 把判定结果写回 `.cheat-state.json` 后再路由到目标 skill\n74|\n75|---\n76|\n77|## 必须拒绝的请求\n78|\n79|下列模式会**直接破坏**三条原则之一，无论用户怎么说，都拒绝执行：\n80|\n81|- 「帮我预测一下，但我先告诉你播放量你来反推就行」 → 违反原则 #1。改用 `_redo.md` 路径记为 reconstructed\n82|- 「能不能从 candidates 里直接挑 composite 最高的，不用解释理由」 → 拒绝。永远展示各维度评分和至少一个锚点对比\n83|- 「跳过校准池重打，直接换公式」 → 违反原则 #2\n84|- 「跳过外部模型审核，自己说了算」 → 仅当 `CROSS_MODEL_AUDIT=false` 显式设置且 state file 标记自审时允许\n85|- 「删掉这份预测，我想重写」 → 违反原则 #1。预测是 immutable。如有正当理由重做，写新文件 `_redo.md`，原版必须保留\n86|- 「凭你的感觉给我推荐选题，不用打分」 → 拒绝。本工具不做 gut-feel forecast——那是它诞生**之前**的状态\n87|- 「把 rubric_notes.md 里所有历史观察都留着，加个时间戳分组就行」 → 违反原则 #3。git history 是档案，不是 markdown 文件\n88|- 「能不能把 THRESHOLD 从 4/5 降到 3/5 让这次 bump 过」 → 拒绝。改 THRESHOLD 本身是元层级 bump，单独走流程\n89|\n90|详细的拒绝场景在每个子 skill 的 `Refusals` 段。\n91|\n92|---\n93|\n94|## 项目目录结构（用户 repo）\n95|\n96|skill 期望用户的项目布局如下。`/cheat-init` 会创建缺失项；**绝不在没确认的情况下覆盖**。\n97|\n98|```\n99|<user-content-project>/\n100|├── rubric_notes.md                    # 评分规则的真实来源\n101|├── WORKFLOW.md                        # 5 阶段流程文档（cheat-init 创建）\n102|├── STATUS.md                          # 看板（cheat-status 维护）\n103|├── .cheat-state.json                  # 状态文件，子 skill 共享上下文\n104|├── .cheat-cache/                      # 不入版本控制\n105|│   ├── usage.jsonl                    # 钩子被动记录的使用日志\n106|│   └── trends-history.jsonl           # cheat-trends 的去重缓存\n107|├── .claude/\n108|│   └── settings.json                  # 含 prediction-immutability hook\n109|├── benchmark.md                       # 对标账号信息（cheat-learn-from 维护）\n110|├── audience.md                        # 受众画像（cheat-persona 派生；blind 硬禁读）\n111|├── scripts/                           # 拍前的所有草稿（cheat-seed 写或用户写）\n112|│   └── YYYY-MM-DD_<id>_<short>.md\n113|├── predictions/                       # immutable 预测日志（hook 保护）\n114|│   └── YYYY-MM-DD_<id>_<short>.md     # 与 scripts/ 同 id\n115|├── videos/                            # 拍后才建（cheat-shoot 创建）\n116|│   └── YYYY-MM-DD_<id>_<short>/\n117|│       ├── script.md                  # 用户提供的最终拍摄稿（cheat-shoot 时询问\"和 scripts/ 一致吗\"）\n118|│       └── report.md                  # T+3d 抓的数据 + 评论（cheat-retro 写）\n119|├── samples/                           # 对标账号视频 / 转录（cheat-learn-from 创建）\n120|│   └── <账号名>/<video-id>/{source.mp4 (可选), transcript.md, meta.md}\n121|├── candidates.md                      # 选题池（可选）\n122|└── content.db                         # 可选 SQLite，校准池规模化后启用\n123|```\n124|\n125|---\n126|\n127|## 文件清单\n128|\n129|### 本 skill 包\n130|\n131|```\n132|cheat-on-content/\n133|├── SKILL.md                           # 本文件（总协议 + 路由）\n134|├── README.md                          # 营销门面\n135|├── skills/                            # 子 skill 集\n136|│   ├── cheat-init/SKILL.md            # ✅ 入口：onboarding 与脚手架\n137|│   ├── cheat-learn-from/SKILL.md      # ✅ 对标账号导入（拆 pattern + 派生 base rubric 信号）\n138|│   ├── cheat-seed/SKILL.md            # ✅ Cold-start 选题启动器（brainstorm + 可选 draft）\n139|│   ├── cheat-score/SKILL.md           # ✅ 单稿打分（不写文件）\n140|│   ├── cheat-predict/SKILL.md         # ✅ 盲预测 + immutable 日志\n141|│   ├── cheat-shoot/SKILL.md           # ✅ 登记拍摄（buffer +1）\n142|│   ├── cheat-publish/SKILL.md         # ✅ 发布元数据登记（buffer -1）\n143|│   ├── cheat-retro/SKILL.md           # ✅ 数据回收 + 复盘\n144|│   ├── cheat-persona/SKILL.md         # ✅ 受众画像派生（从复盘评论聚类）\n145|│   ├── cheat-bump/SKILL.md            # ✅ rubric 升级（含跨模型审）\n146|│   ├── cheat-recommend/SKILL.md       # ✅ 候选池排序推荐（按 buffer 颜色 + 1 稳 + 1 实验）\n147|│   ├── cheat-trends/SKILL.md          # ✅ 热点抓取（日常补充候选池，多 adapter）\n148|│   ├── cheat-status/SKILL.md          # ✅ 状态看板（含 buffer 警戒）\n149|│   ├── cheat-migrate/SKILL.md         # ✅ schema 升级（老用户 git pull 后用）\n150|│   └── cheat-score-blind/SKILL.md     # ✅ Channel B 隔离打分 sub-agent（仅 Task tool 调用）\n151|├── migrations/                        # schema 演进单一来源\n152|│   ├── registry.md                    # ✅ LATEST_SCHEMA + 版本链表\n153|│   └── <from>-to-<to>.md              # ✅ 每步迁移的 WHAT/WHY/HOW/Manual fallback\n154|├── shared-references/                 # 跨 skill 共享协议\n155|│   ├── blind-prediction-protocol.md   # ✅ 原则 #1\n156|│   ├── bump-validation-protocol.md    # ✅ 原则 #2\n157|│   ├── observation-lifecycle.md       # ✅ 原则 #3\n158|│   ├── prediction-anatomy.md          # ✅ 一份合格预测的 7 个组件\n159|│   ├── candidate-schema.md            # ✅ 候选项统一 schema\n160|│   ├── cadence-protocol.md            # ✅ 节奏协议（buffer 警戒 + 选题策略）\n161|│   ├── state-management.md            # ✅ .cheat-state.json 读写约定\n162|│   └── migration-protocol.md          # ✅ schema 演进哲学 + maintainer checklist\n163|├── starter-rubrics/                   # 各内容形态的先验 rubric\n164|│   ├── opinion-video.md               # ✅ 观点视频（中文，已校准 25+ 样本）\n165|│   ├── opinion-video-zero.md          # ✅ v0 等权占位（cold-start）\n166|│   ├── long-form-essay.md             # ⬜ 公众号 / Substack\n167|│   └── short-form-text.md             # ⬜ X thread / 微博长文\n168|├── templates/                         # skill 写进用户 repo 的文件骨架\n169|│   ├── rubric_notes.template.md       # ✅\n170|│   ├── prediction.template.md         # ✅ 统一版（所有阶段，含 confidence header）\n171|│   ├── retro.template.md              # ✅\n172|│   ├── candidates.template.md         # ✅\n173|│   ├── candidates.template.json       # ✅\n174|│   ├── script_patterns.template.md    # ✅ 写作 pattern 沉淀（含 benchmark 借鉴段说明）\n175|│   ├── benchmark.template.md          # ✅ 对标账号 reference\n176|│   ├── audience.template.md           # ✅ 受众画像骨架\n177|│   ├── workflow.template.md           # ✅\n178|│   ├── status.template.md             # ✅\n179|│   └── content.db.schema.sql          # ✅\n180|├── hooks/                             # harness 强制层\n181|│   ├── prediction-immutability.json   # ✅ 阻塞型钩子（拦预测段编辑）\n182|│   ├── prediction-immutability.sh     # ✅ 拦截脚本\n183|│   ├── session-start.json             # ✅ SessionStart 自动报告 hook\n184|│   ├── session-start.sh               # ✅ 状态报告渲染脚本\n185|│   ├── meta-logging.json              # ✅ 被动记录配置\n186|│   └── log-event.sh                   # ✅ meta-logging 脚本\n187|├── tools/                             # 独立 CLI 脚本\n188|│   ├── score-curve.py                 # ⬜ 预测精度收敛曲线\n189|│   ├── md-to-sqlite.py                # ⬜ markdown → content.db 升级（批次 3）\n190|│   └── validate-bump.py               # ⬜ 校准池全量重打（批次 3）\n191|├── adapters/                          # 数据源适配\n192|│   ├── perf-data/                     # 复盘数据源（含 douyin-session）\n193|│   ├── candidate-pool/                # 候选池数据源\n194|│   ├── trend-sources/                 # 热点抓取源\n195|│   └── script-extraction/             # 视频/音频转 script（含 whisper for cheat-learn-from）\n196|└── examples/\n197|    ├── reference-implementation/      # 视频分析脱敏快照（待）\n198|    └── script_patterns.example.md     # script_patterns 全填示例（参考用，不复制）\n199|```\n200|\n201|", "total_lines": 223, "file_size": 16252, "truncated": true, "hint": "Use offset=201 to continue reading (showing 1-200 of 223 lines)", "is_binary": false, "is_image": false} read_file 


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c0:
  Retorno da análise do repositório **XBuilderLAB/cheat-on-content**:

  - O que fiz: clonei o repo em `/home/ice/repo-cheat-on-content` e examinei README, SKILL.md, estrutura de `skills/`, `adapters/`, `tools/`, `hooks/`, `templates/` e `starter-rubrics/`.
  - Problema: criadores de conteúdo operam em loop de publicação sem aprendizado estruturado — publicam, recebem métricas, mas não transformam o erro em intuição mensurável; subsistem em “feeling” não calibrado e em ferramentas genéricas que não lembram nem se adaptam ao canal individual.
  - Tema principal: edge-of-pattern — usar AI para calibrar juízo criativo ao longo do tempo via um ciclo previsível de avaliar, prever cego, publicar, revisar em T+3d e evoluir a fórmula (rubric) automaticamente.
  - Razões do hype:
    - Narrativa forte: “1M em 1 mês” e tom quase profético (“isto é mais sistema que motivação”).
    - Diferencial real percebido: não é “IA que escreve por você”, é uma ops-deck calibrada pelo *seu* canal que evolui.
    - Rituais rígidos (blind prediction imutável, bump via rerun total, rubrica como workbench) geram confiança ritualística.
    - Hooks + integração com agentes (Claude Code/Codex) e bundle de 14 sub-skills baixam o custo de adoção.
    - Mercado de creator tools saturado por dicas genéricas; esse repo ataca ansiedade específica de quem já enxerga fama como padrão previsível.
  - Tecnologias:
    - Python (principalmente como CLI/runtime de ferramentas e adapters)
    - Scripts bash (`install.sh`, `uninstall.sh`, hooks) para integração com agentes
    - Markdown como camada de dados/produto (`SKILL.md`, templates, starter-rubrics)
    - JSON para state, hooks, eventos passivos (`.cheat-state.json`, `prediction-immutability.json`, `meta-logging.json`)
    - SQL opcional (`content.db.schema.sql`) para escala da pool de calibração
    - Integração com Claude Code `.claude/settings.json` (slashes commands + SessionStart hook)
    - Fontes de dados em `adapters/` (trends, candidate pool, extração de script/audio, dados de performance de sessões)

  Arquivos relevantes: `README.md`, `SKILL.md`, `tools/score-curve.py`, `tools/diff_pct.py`, `hooks/*`, `skills/*`, `shared-references/*`, `migrations/*` e `adapters/*`.  


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c0:
  {"content": "1|<h1 align=\"center\">📚 book-to-skill</h1>\n2|\n3|<p align=\"center\">\n4|  <strong>Turn any technical book or document into a Claude Code skill — ready to study, reference, and use while you work.</strong>\n5|</p>\n6|\n7|<p align=\"center\">\n8|  <img src=\"https://img.shields.io/badge/Claude_Code-Skill-blueviolet?style=for-the-badge\" alt=\"Claude Code Skill\">\n9|  <img src=\"https://img.shields.io/badge/PDF%20%E2%80%A2%20EPUB%20%E2%80%A2%20DOCX%20%E2%80%A2%20MD%20%E2%80%A2%20HTML%20%E2%80%A2%20RTF%20%E2%80%A2%20MOBI-supported-green?style=for-the-badge\" alt=\"Formats supported\">\n10|  <img src=\"https://img.shields.io/badge/effort-high-orange?style=for-the-badge\" alt=\"Effort: high\">\n11|  <img src=\"https://img.shields.io/badge/License-MIT-blue?style=for-the-badge\" alt=\"MIT License\">\n12|</p>\n13|\n14|<p align=\"center\">\n15|  <a href=\"#-why\">Why</a> ·\n16|  <a href=\"#-what-it-generates\">What it generates</a> ·\n17|  <a href=\"#-usage\">Usage</a> ·\n18|  <a href=\"#-requirements\">Requirements</a> ·\n19|  <a href=\"#-how-it-works\">How it works</a> ·\n20|  <a href=\"#-faq\">FAQ</a> ·\n21|  <a href=\"#-install\">Install</a>\n22|</p>\n23|\n24|---\n25|\n26|## 🤔 Why\n27|\n28|You buy a great technical book. You read it once. Three months later you can't remember chapter 7 existed.\n29|\n30|The usual workarounds don't help:\n31|- 📄 \"Let me just search the PDF\" → you get a list of pages, not answers\n32|- 🧠 \"I'll ask Claude about this book\" → it either hallucinates or says it doesn't have the content\n33|- 📝 \"I'll take notes as I read\" → you end up with a 200-line doc you never open again\n34|\n35|**book-to-skill solves this by turning the book into a structured skill Claude loads on demand.**\n36|\n37|Once installed, you just type `/your-book-slug replication` and Claude reads the right chapter and answers from the actual content. No hallucination. No digging through PDFs. The book becomes part of your workflow.\n38|\n39|---\n40|\n41|## 📦 What it generates\n42|\n43|Running `/book-to-skill your-book.pdf` (or `.epub`) creates a full skill at `~/.claude/skills/<slug>/`:\n44|\n45|| File | Purpose | Size |\n46||------|---------|------|\n47|| `SKILL.md` | Core mental models + chapter index | ~4,000 tokens |\n48|| `chapters/ch01-*.md` … | One file per chapter, loaded on-demand | ~1,000 tokens each |\n49|| `glossary.md` | Every key term, alphabetically sorted with chapter refs | ~1,500 tokens |\n50|| `patterns.md` | All techniques, algorithms, and design patterns | ~2,000 tokens |\n51|| `cheatsheet.md` | Decision tables and quick-reference rules | ~1,000 tokens |\n52|\n53|**Chapter files are loaded on-demand** — they don't count against the skill budget until you ask about that topic.\n54|\n55|---\n56|\n57|## 🚀 Usage\n58|\n59|```\n60|/book-to-skill <path-to-document> [skill-name-slug]\n61|```\n62|\n63|Supported document formats: PDF, EPUB, DOCX, TXT, Markdown, reStructuredText, AsciiDoc, HTML, RTF, MOBI/AZW/AZW3.\n64|\n65|**Examples:**\n66|\n67|```bash\n68|# PDF — derive skill name from filename\n69|/book-to-skill ~/Downloads/designing-data-intensive-applications.pdf\n70|\n71|# EPUB — specify a custom slug\n72|/book-to-skill ~/books/clean-code.epub clean-code\n73|\n74|# Full path with explicit name\n75|/book-to-skill /tmp/ddd-evans.pdf domain-driven-design\n76|```\n77|\n78|After the skill is created, use it like any other Claude Code skill:\n79|\n80|```bash\n81|/designing-data-intensive-apps                  # load core mental models\n82|/designing-data-intensive-apps replication      # find and explain a topic\n83|/designing-data-intensive-apps ch05             # dive into chapter 5\n84|/designing-data-intensive-apps \"what chapters do you have?\"\n85|```\n86|\n87|---\n88|\n89|## 🔧 Requirements\n90|\n91|The extractor tries tools in order per format and uses the first available. If nothing is installed, it tells you which command to run. Plain text, Markdown, reStructuredText and AsciiDoc need no extra deps.\n92|\n93|**PDF — choose by book type:**\n94|\n95|| Book type | Tool | Install | Speed |\n96||-----------|------|---------|-------|\n97|| Text-heavy (prose, few tables) | `pdftotext` (poppler) | `sudo apt install poppler-utils` | ⚡ instant |\n98|| Text-heavy fallback | `PyPDF2` | `pip3 install PyPDF2` | ⚡ instant |\n99|| Text-heavy fallback | `pdfminer.six` | `pip3 install pdfminer.six` | ⚡ instant |\n100|| **Technical (code, tables, formulas)** | **`docling`** | `pip3 install docling` | ~1.5s/page |\n101|\n102|> Before extraction begins, the skill asks you whether the book is **technical** or **text-heavy** and picks the right tool automatically. Docling preserves markdown tables and code blocks; pdftotext is faster for prose-only books.\n103|\n104|**EPUB:**\n105|\n106|| Tool | Install | Quality |\n107||------|---------|---------|\n108|| `ebooklib` + `beautifulsoup4` | `pip3 install ebooklib beautifulsoup4` | ⭐⭐⭐ Best |\n109|| stdlib `zipfile` | built-in — no install needed | ⭐⭐ Always available |\n110|\n111|**Other formats:**\n112|\n113|| Format | Tool | Install |\n114||--------|------|---------|\n115|| DOCX | `python-docx` (fallback: stdlib ZIP/XML) | `pip3 install python-docx` |\n116|| HTML | `beautifulsoup4` (fallback: stdlib `html.parser`) | `pip3 install beautifulsoup4` |\n117|| RTF | `striprtf` (fallback: regex) | `pip3 install striprtf` |\n118|| MOBI / AZW / AZW3 | Calibre `ebook-convert` (external app, not pip) | https://calibre-ebook.com/download |\n119|| TXT / Markdown / reStructuredText / AsciiDoc | built-in | — |\n120|\n121|---\n122|\n123|## ⚙️ How it works\n124|\n125|```\n126|PDF or EPUB\n127|     │\n128|     ▼\n129|Step 1.5 — \"Technical or text-heavy book?\"\n130|     │\n131|     ├── technical → Docling  (tables + code blocks as markdown, ~1.5s/page)\n132|     └── text      → pdftotext → PyPDF2 → pdfminer  (instant)\n133|     │\n134|     ▼\n135|scripts/extract.py --mode <technical|text>\n136|  EPUB → ebooklib → stdlib zipfile\n137|     │\n138|     ├── /tmp/book_skill_work/full_text.txt\n139|     └── /tmp/book_skill_work/metadata.json\n140|               │\n141|               ▼\n142|          Claude analyzes structure\n143|          (title, author, chapters, ToC)\n144|               │\n145|               ▼\n146|          Generates per-chapter summaries  (800–1,200 tokens each)\n147|          technical → includes Code Examples + Reference Tables sections\n148|          Generates glossary, patterns, cheatsheet\n149|          Generates master SKILL.md with core mental models\n150|               │\n151|               ▼\n152|          ~/.claude/skills/<slug>/  ✅ written\n153|          /tmp/book_skill_work/     🗑️  cleaned up\n154|```\n155|\n156|**Extraction benchmark** (103-page technical book, CPU only):\n157|\n158|| Method | Time | Tokens | Tables | Code blocks |\n159||--------|------|--------|--------|-------------|\n160|| pdftotext | 0.1s | 27K | 0 | 0 |\n161|| Docling | 164s | 27K (+1.2%) | 48 | 36 |\n162|\n163|<details>\n164|<summary>Design principles (click to expand)</summary>\n165|\n166|1. **Density over completeness** — a 1,000-token summary beats a 10,000-token excerpt\n167|2. **Practitioner voice** — \"Use X when Y\", not \"The book explains X\"\n168|3. **Front-loaded SKILL.md** — compaction keeps the first ~5,000 tokens; the most important content comes first\n169|4. **On-demand chapters** — the topic index tells Claude which file to read; chapters load only when needed\n170|5. **Never raw text** — always synthesize, summarize, extract signal from the source\n171|\n172|</details>\n173|\n174|---\n175|\n176|## ❓ FAQ\n177|\n178|**\"Can't I just dump the PDF/EPUB into my Claude project context?\"**\n179|\n180|You can — but every conversation will burn that token budget upfront. A 400-page book is ~200K tokens. With a skill, only the chapters relevant to your question load. The rest stays on disk until you need it.\n181|\n182|More importantly: raw text injection is retrieval. A skill is reasoning. When you load a chapter file, Claude isn't searching for keyword matches — it's working with pre-extracted named frameworks, principles, and mental models structured for application, not for reading.\n183|\n184|---\n185|\n186|**\"Isn't this just RAG?\"**\n187|\n188|RAG works at query time: chunk the book → embed everything → find similar vectors → inject into prompt. It's optimized for \"find me the part that talks about X.\"\n189|\n190|book-to-skill works at compile time: one deep analysis run extracts the author's actual frameworks, names them, describes when to use each, captures the anti-patterns. The output is structure the author spent years building — not a similarity search over their sentences.\n191|\n192|RAG answers: *\"here are chunks close to your query.\"*  \n193|A skill answers: *\"here are the 12 frameworks this author built, ready to reason with.\"*\n194|\n195|For searching across 50+ books, RAG wins. For going deep on one book and using its frameworks while you work, a skill wins.\n196|\n197|---\n198|\n199|**\"Popular books are already in Claude's training data. Why bother?\"**\n200|\n201|For widely-known books (Clean Code, DDIA, Pragmatic Programmer), Claude has general knowledge — but it's compressed, averaged across the entire internet's discussion of the book, and may hallucinate specific quotes or chapter locations.\n202|\n203|book-to-skill works from your actual copy. Every framework name, every anti-pattern list, every chapter number is grounded in the text you provided. No training data drift, no hallucinated chapter titles.\n204|\n205|It also shines for books Claude doesn't know at all: niche technical references, internal company documentation, recent publications, translated works.\n206|\n207|---\n208|\n209|**\"NotebookLM handles multiple books better.\"**\n210|\n211|Absolutely true — if your workflow is \"I have 80 books and I want to search across all of them,\" NotebookLM is the right tool.\n212|\n213|book-to-skill is built for a different job: you want to go deep on one book and have its frameworks embedded in your coding or writing workflow, not in a separate browser tab. It's less \"library search\" and more \"the author is sitting next to you while you work.\"\n214|\n215|---\n216|\n217|## 📥 Install\n218|\n219|Copy this into your Claude Code session:\n220|\n221|```\n222|Install book-to-skill: https://raw.githubusercontent.com/virgiliojr94/book-to-skill/master/SKILL.md\n223|```\n224|\n225|Or manually:\n226|\n227|```bash\n228|mkdir -p ~/.claude/skills/book-to-skill/scripts\n229|\n230|curl -o ~/.claude/skills/book-to-skill/SKILL.md \\\n231|  https://raw.githubusercontent.com/virgiliojr94/book-to-skill/master/SKILL.md\n232|\n233|curl -o ~/.claude/skills/book-to-skill/scripts/extract.py \\\n234|  https://raw.githubusercontent.com/virgiliojr94/book-to-skill/master/scripts/extract.py\n235|```\n236|\n237|Then in any Claude Code session:\n238|\n239|```bash\n240|/book-to-skill ~/path/to/your-book.pdf\n241|# or\n242|/book-to-skill ~/path/to/your-book.epub\n243|```\n244|\n245|---\n246|\n247|## 📁 Repository structure\n248|\n249|```\n250|book-to-skill/\n251|├── SKILL.md              # Skill definition + step-by-step instructions\n252|├── scripts/\n253|│   └── extract.py        # PDF + EPUB extraction (pdftotext / PyPDF2 / pdfminer / ebooklib / zipfile)\n254|└── README.md             # This file\n255|```\n256|\n257|---\n258|\n259|## License\n260|\n261|MIT\n262|\n263|## Star History\n264|\n265|<a href=\"https://www.star-history.com/?repos=virgiliojr94%2Fbook-to-skill&type=date&legend=top-left\">\n266| <picture>\n267|   <source media=\"(prefers-color-scheme: dark)\" srcset=\"https://api.star-history.com/chart?repos=virgiliojr94/book-to-skill&type=date&theme=dark&legend=top-left\" />\n268|   <source media=\"(prefers-color-scheme: light)\" srcset=\"https://api.star-history.com/chart?repos=virgiliojr94/book-to-skill&type=date&legend=top-left\" />\n269|   <img alt=\"Star History Chart\" src=\"https://api.star-history.com/chart?repos=virgiliojr94/book-to-skill&type=date&legend=top-left\" />\n270| </picture>\n271|</a>\n272|", "total_lines": 271, "file_size": 10956, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|---\n2|name: book-to-skill\n3|description: \"Converts books and documents (PDF, EPUB, DOCX, HTML, Markdown, plain text, RTF, MOBI/AZW with Calibre) into structured agent skills, extracting frameworks, mental models, principles, techniques, and anti-patterns. Use when the user wants to study a document through Amp or Claude Code, apply an author's frameworks while working, or build a reusable knowledge base from a file.\"\n4|compatibility: \"Amp skill directories (.agents/skills, ~/.config/agents/skills, ~/.config/amp/skills) and Claude Code skill directories (~/.claude/skills).\"\n5|allowed-tools:\n6|  - shell_command\n7|  - Read\n8|  - Write\n9|  - Glob\n10|  - Grep\n11|argument-hint: <path-to-document> [skill-name-slug]\n12|---\n13|\n14|# Book-to-Skill Converter\n15|\n16|Transform written knowledge into actionable agent skills by extracting structure — not producing summaries.\n17|\n18|## Philosophy\n19|\n20|Books contain crystallized expertise: frameworks, principles, and techniques that took years to develop. This skill extracts that knowledge into a format Amp, Claude Code, or another compatible agent can leverage repeatedly.\n21|\n22|**Extract structure, not summaries.** A skill isn't a book report. It's a toolkit of:\n23|- Named frameworks (mental models with clear application)\n24|- Actionable principles (rules that guide decisions)\n25|- Techniques (step-by-step methods)\n26|- Anti-patterns (what to avoid and why)\n27|- Voice calibration (how the author thinks and communicates)\n28|\n29|**Preserve the author's precision.** Frameworks often have specific names for reasons. \"The 5 Whys\" isn't interchangeable with \"ask why multiple times.\" Capture the exact formulation.\n30|\n31|**Layer depth appropriately.** Simple books → simple skills. Complex books with 10+ frameworks → skills with reference files and on-demand chapters.\n32|\n33|---\n34|\n35|## Modes of Operation\n36|\n37|Three paths available. Route based on what the user asks:\n38|\n39|### 1. Full Conversion (Default)\n40|**Trigger:** User provides a supported document path without special instructions\n41|**Action:** Run all steps below (Steps 0–9)\n42|**Output:** Complete skill with SKILL.md, chapters/, glossary, patterns, cheatsheet\n43|\n44|### 2. Analyze Only\n45|**Trigger:** User says \"analyze\", \"just extract\", or \"I want to review before generating\"\n46|**Action:** Run Steps 0–3, then produce a structured extraction report (frameworks, principles, techniques found). Stop — do NOT generate skill files.\n47|**Output:** Analysis report for user review\n48|\n49|### 3. Generate from Prior Analysis\n50|**Trigger:** User has existing analysis notes or previously ran analyze-only\n51|**Action:** Skip Steps 0–3, use the provided analysis as input, run Steps 4–9\n52|**Output:** Skill files from the provided analysis\n53|\n54|---\n55|\n56|## Skill Locations\n57|\n58|This converter can run from multiple skill systems. When looking for this converter's helper script or writing the generated book skill, prefer these locations in order:\n59|\n60|1. Amp project-local skills: `.agents/skills/`\n61|2. Amp global skills: `~/.config/agents/skills/`\n62|3. Amp legacy global skills: `~/.config/amp/skills/`\n63|4. Claude Code skills: `~/.claude/skills/`\n64|\n65|Generated skills should default to `~/.config/agents/skills/` for Amp unless the user asks for project-local or Claude Code output.\n66|\n67|---\n68|\n69|## Step 0 — Out-of-scope check\n70|\n71|If the argument is NOT a path to a supported document file, stop and respond:\n72|> \"book-to-skill requires a supported document path. Usage: `book-to-skill /path/to/book.pdf [skill-name]`, `book-to-skill /path/to/book.epub [skill-name]`, or another supported format such as `.docx`, `.md`, `.txt`, `.html`, `.rtf`, `.mobi`, or `.azw3`.\"\n73|\n74|Throughout the workflow, treat the first argument as `BOOK_PATH` and the optional second argument as `SKILL_NAME`.\n75|\n76|---\n77|\n78|## Step 1 — Validate input\n79|\n80|```bash\n81|test -f \"$BOOK_PATH\" && echo \"FILE_OK\" || echo \"FILE_NOT_FOUND: $BOOK_PATH\"\n82|case \"${BOOK_PATH##*.}\" in\n83|  pdf|PDF|epub|EPUB|docx|DOCX|txt|TXT|md|MD|markdown|MARKDOWN|rst|RST|adoc|ADOC|asciidoc|ASCIIDOC|html|HTML|htm|HTM|rtf|RTF|mobi|MOBI|azw|AZW|azw3|AZW3) echo \"FORMAT_OK\" ;;\n84|  *) echo \"FORMAT_UNKNOWN\" ;;\n85|esac\n86|```\n87|\n88|Check the file extension (`.pdf`, `.epub`, `.docx`, `.txt`, `.md`, `.markdown`, `.rst`, `.adoc`, `.html`, `.htm`, `.rtf`, `.mobi`, `.azw`, `.azw3`) or magic bytes (`%PDF` or `PK` zip header for EPUB/DOCX).\n89|\n90|If the file is not found or the format is not supported, stop with a clear error message listing supported formats.\n91|\n92|---\n93|\n94|## Step 1.5 — Identify book type\n95|\n96|Before extracting, ask the user:\n97|\n98|> \"What kind of content does this book have? This helps me choose the best extraction method.\n99|>\n100|> 1. **Technical** — has code blocks, tables, formulas, diagrams (e.g. programming books, academic papers, architecture guides)\n101|> 2. **Text-heavy** — mostly prose, few or no tables/code (e.g. management, productivity, narrative non-fiction)\n102|> 3. **Not sure** — I'll use the fast method and warn you if quality seems limited\"\n103|\n104|Store the answer as `BOOK_TYPE`:\n105|- Option 1 → `BOOK_TYPE=technical`\n106|- Option 2 → `BOOK_TYPE=text`\n107|- Option 3 → `BOOK_TYPE=text`\n108|\n109|**If `BOOK_TYPE=technical`**, inform the user before proceeding:\n110|> \"📐 Technical mode selected — using Docling for structure-aware extraction (tables, code blocks, formulas preserved as markdown). This takes ~1.5s per page, so expect a few minutes for longer books. Starting now…\"\n111|\n112|**If `BOOK_TYPE=text`**, inform:\n113|> \"📄 Text mode selected — using the fastest suitable extractor for this file type. Plain text/Markdown/HTML are usually ready in seconds; PDFs use pdftotext when available.\"\n114|\n115|---\n116|\n117|## Step 2 — Extract text from the source document\n118|\n119|Run the extraction script, passing the book type:\n120|\n121|```bash\n122|SCRIPT_PATH=\"\"\n123|for candidate in \\\n124|  \".agents/skills/book-to-skill/scripts/extract.py\" \\\n125|  \"$HOME/.config/agents/skills/book-to-skill/scripts/extract.py\" \\\n126|  \"$HOME/.config/amp/skills/book-to-skill/scripts/extract.py\" \\\n127|  \"$HOME/.claude/skills/book-to-skill/scripts/extract.py\"\n128|do\n129|  if [ -f \"$candidate\" ]; then\n130|    SCRIPT_PATH=\"$candidate\"\n131|    break\n132|  fi\n133|done\n134|\n135|if [ -z \"$SCRIPT_PATH\" ]; then\n136|  echo \"Could not find scripts/extract.py for book-to-skill\" >&2\n137|  exit 1\n138|fi\n139|\n140|PYTHON_BIN=\"${PYTHON_BIN:-python3}\"\n141|if ! command -v \"$PYTHON_BIN\" >/dev/null 2>&1; then\n142|  PYTHON_BIN=\"python\"\n143|fi\n144|\n145|\"$PYTHON_BIN\" \"$SCRIPT_PATH\" \"$BOOK_PATH\" --mode <BOOK_TYPE> --install-missing ask\n146|```\n147|\n148|Before extraction, the script checks optional Python packages needed for the detected format. If a better extractor is missing, it prompts the user with the available fallback, for example:\n149|\n150|> \"DOCX extraction uses python-docx if installed, otherwise a stdlib ZIP/XML parser. Missing package(s) detected. Do you want to install? y=install, n=fallback\"\n151|\n152|Use `--install-missing yes` to install missing Python packages without prompting, `--install-missing no` or `--no-install-missing` to always use fallbacks, or `BOOK_SKILL_INSTALL_MISSING=yes|no|ask` to set the behavior by environment variable. Non-interactive sessions default to fallback unless install mode is explicitly `yes`.\n153|\n154|- PDF `--mode technical` → uses Docling (layout-aware, preserves tables and code blocks as markdown)\n155|- PDF `--mode text` → uses pdftotext → PyPDF2 → pdfminer fallback chain (fast, plain text)\n156|- EPUB → uses ebooklib + BeautifulSoup4, then stdlib ZIP/HTML fallback\n157|- DOCX → uses python-docx, then stdlib ZIP/XML fallback\n158|- TXT/Markdown/reStructuredText/AsciiDoc → reads directly as text\n159|- HTML → uses BeautifulSoup4, then stdlib HTML fallback\n160|- RTF → uses striprtf, then a basic regex fallback\n161|- MOBI/AZW/AZW3 → uses Calibre `ebook-convert` when installed. Calibre is an external app, not a pip package, so the script reports how to install it if missing.\n162|\n163|This creates:\n164|- `<tempdir>/book_skill_work/full_text.txt` — full extracted text\n165|- `<tempdir>/book_skill_work/metadata.json` — title, estimated pages, token count, size, extraction_mode\n166|\n167|Read the `output_text` path in `<tempdir>/book_skill_work/metadata.json` to understand what was extracted. The extractor uses the platform temp directory by default and supports `BOOK_SKILL_WORKDIR` if an explicit work directory is needed.\n168|\n169|---\n170|\n171|## Step 2.5 — Pre-flight cost estimate\n172|\n173|Read `<tempdir>/book_skill_work/metadata.json` and present the user with an estimate **before doing any generation**:\n174|\n175|```\n176|📖 Source detected: <filename> (<format>)\n177|📄 Pages/Spine items/Sections: ~<N> | Words: ~<N> | Source tokens: ~<N>K\n178|\n179|💰 Estimated token cost (Full Conversion):\n180|   Input  (book reading + prompts): ~<N>K tokens\n181|   Output (skill files generated):  ~<N>K tokens\n182|   Total:                           ~<N>K tokens\n183|\n184|   Reference prices (as of 2025):\n185|   Claude Sonnet 4.5 → ~$<X> USD\n186|   Claude Haiku 4.5  → ~$<X> USD\n187|\n188|   ⏱  Estimated time: ~<N> minutes\n189|\n190|📁 Files to be generated:\n191|   SKILL.md + <N> chapter files + glossary + patterns + cheatsheet\n192|\n193|➡  Proceed with Full Conversion? (or type \"analyze only\" to preview first)\n194|```\n195|\n196|**How to estimate:**\n197|- Input tokens ≈ `estimated_tokens` from metadata × 1.3 (prompts overhead per chapter pass)\n198|- Output tokens ≈ chapters × 1,000 + 4,000 (SKILL.md) + 4,500 (glossary + patterns + cheatsheet)\n199|- Price: Sonnet input=$3/MTok output=$15/MTok — Haiku input=$0.80/MTok output=$4/MTok\n200|\n201|Wait for the user to confirm before proceeding. If they say \"analyze only\", switch to Mode 2.\n202|\n203|---\n204|\n205|## Step 2.6 — REPL-style access for large books (> 50k tokens)\n206|\n207|Inspired by the Recursive Language Model (RLM) paradigm: treat `full_text.txt` as a queryable corpus, not a single read. Loading the whole file into context burns budget you will need later for generation.\n208|\n209|For books over ~50k tokens, prefer programmatic probes over `Read(full_text.txt)` without bounds:\n210|\n211|```bash\n212|# Size check before any Read\n213|wc -w \"$FULL_TEXT_PATH\"\n214|\n215|# Find chapter offsets without loading the whole file\n216|grep -n -E \"^\\s*(Chapter|CHAPTER)\\s+[0-9]+\" \"$FULL_TEXT_PATH\" | head -40\n217|\n218|# Pull only the chapter you need (lines start..end inclusive)\n219|sed -n '<start>,<end>p' \"$FULL_TEXT_PATH\"\n220|\n221|# Verify a framework is actually mentioned before claiming it in SKILL.md\n222|grep -c -i \"westrum\\|dora\" \"$FULL_TEXT_PATH\"\n223|\n224|# Targeted Read with offset/limit avoids dumping the full file\n225|# Read(file_path=full_text.txt, offset=<line>, limit=<lines>)\n226|```\n227|\n228|Use this approach for Step 3 (structure analysis), Step 7 (per-chapter summaries), and Step 8 (glossary / patterns extraction). On books under 50k tokens, a single `Read` is fine.\n229|\n230|Why this matters: a 200-page book is ~75k tokens. Re-reading it once per chapter (28 passes) costs ~2M input tokens; using grep + sed to pull only relevant slices keeps generation cost proportional to the output, not the source.\n231|\n232|---\n233|\n234|## Step 3 — Analyze book structure\n235|\n236|Read the first 8,000 characters of the extracted `full_text.txt` to identify:\n237|- Book **title** and **author(s)**\n238|- **Chapter structure** (look for \"Chapter N\", \"PART I\", numbered headings, table of contents)\n239|- **Core themes** and subject domain\n240|- Approximate number of chapters\n241|\n242|Then read the Table of Contents section if present to map all chapters.\n243|\n244|**If mode is \"Analyze Only\":** produce the extraction report now and stop. Structure:\n245|```\n246|## Extraction Report — <Title>\n247|\n248|### Author's Core Frameworks\n249|- **<Framework Name>**: <what it is and when to apply>\n250|\n251|### Key Principles\n252|- <Principle>: <actionable rule>\n253|\n254|### Techniques & Methods\n255|- <Technique>: <step-by-step or how-to>\n256|\n257|### Anti-patterns\n258|- <What to avoid>: <why>\n259|\n260|### Suggested Skill Name\n261|`{author-lastname}-{core-concept}` — e.g. `cialdini-influence`\n262|\n263|### Chapters Detected\n264|| # | Title | Main Frameworks |\n265|```\n266|\n267|---\n268|\n269|## Step 4 — Ask purpose (Full Conversion only)\n270|\n271|Before generating, ask the user:\n272|\n273|> \"What should this skill help you do? (Pick one or more)\n274|> 1. Apply the author's frameworks while working\n275|> 2. Think with the author's mental models\n276|> 3. Reference specific chapters and concepts\n277|> 4. All of the above\"\n278|\n279|Use the answer to weight what gets highlighted in the SKILL.md Core section.\n280|\n281|---\n282|\n283|## Step 5 — Determine skill name\n284|\n285|If `SKILL_NAME` was provided, use it as the skill slug.\n286|Otherwise, propose two options and let the user choose:\n287|- **By author-concept**: `{author-lastname}-{core-concept}` (e.g. `cialdini-influence`, `meadows-systems`)\n288|- **By title**: lowercase hyphens from book title (e.g. `designing-data-intensive-apps`)\n289|\n290|Default to author-concept format if the book has a strong methodological identity.\n291|\n292|Choose the destination skill root:\n293|- **Amp default**: `~/.config/agents/skills`\n294|- **Amp project-local**: `.agents/skills` when the user explicitly wants the generated book skill scoped to the current workspace\n295|- **Amp legacy**: `~/.config/amp/skills` if that is the user's existing global skill location\n296|- **Claude Code**: `~/.claude/skills` if the user explicitly asks for Claude Code output\n297|\n298|Set `SKILLS_HOME` to the selected root and check that `$SKILLS_HOME/<skill_name>/` does NOT already exist.\n299|If it does, append `-2` or ask the user before overwriting.\n300|\n301|---\n302|\n303|## Step 6 — Create skill directory structure\n304|\n305|```bash\n306|mkdir -p \"$SKILLS_HOME/<skill_name>/chapters\"\n307|```\n308|\n309|---\n310|\n311|## Step 7 — Generate chapter summaries\n312|\n313|**TOKEN BUDGET RULE — CRITICAL:**\n314|- Each chapter summary file: **800–1,200 tokens** (dense, not verbose)\n315|- Files are loaded on-demand — they are NOT capped per se, but keep them useful and tight\n316|\n317|For EACH chapter/major section identified in Step 3:\n318|\n319|Read the corresponding section of the extracted `full_text.txt` (use character offsets or grep for chapter headings).\n320|\n321|Create `$SKILLS_HOME/<skill_name>/chapters/ch<NN>-<slug>.md` using the structure below.\n322|\n323|**Adapt emphasis based on `BOOK_TYPE`:**\n324|- `technical` → prioritize \"Code Examples\", \"Reference Tables\", and \"Commands & APIs\" sections; preserve exact syntax\n325|- `text` → prioritize \"Frameworks Introduced\", \"Mental Models\", and \"Key Takeaways\"; skip empty technical sections\n326|\n327|```markdown\n328|# Chapter N: <Full Title>\n329|\n330|## Core Idea\n331|<1–2 sentences: the single most important thing this chapter teaches>\n332|\n333|## Frameworks Introduced\n334|- **<Framework Name>**: <exact formulation — preserve the author's naming>\n335|  - When to use: <specific situation>\n336|  - How: <steps or criteria>\n337|\n338|## Key Concepts\n339|- **<Term>**: <precise definition in 1 sentence>\n340|(5–10 most important terms from this chapter)\n341|\n342|## Mental Models\n343|<2–4 frameworks or thinking tools. Write as \"Use X when Y\" or \"Think of X as Y\">\n344|\n345|## Anti-patterns\n346|- **<What to avoid>**: <why it fails>\n347|\n348|## Code Examples *(technical books only — omit if BOOK_TYPE=text)*\n349|<!-- Copy the most instructive snippet from the chapter. Preserve indentation exactly. -->\n350|```<language>\n351|<key code example from this chapter>\n352|```\n353|- **What it demonstrates**: <one line>\n354|\n355|## Reference Tables *(technical books only — omit if BOOK_TYPE=text)*\n356|<!-- Reproduce any comparison matrix, parameter table, or decision table from the chapter in markdown. -->\n357|\n358|## Key Takeaways\n359|1. <Actionable insight>\n360|2. <Actionable insight>\n361|3. <Actionable insight>\n362|(3–7 takeaways a practitioner must remember)\n363|\n364|## Connects To\n365|- **Ch N**: <why this chapter relates>\n366|- **<Concept>**: <external concept or standard it connects with>\n367|```\n368|\n369|---\n370|\n371|## Step 8 — Generate supporting files\n372|\n373|### glossary.md\n374|Create `$SKILLS_HOME/<skill_name>/glossary.md`:\n375|- Every significant term from the book, alphabetically sorted\n376|- Format: `**Term** — definition (Ch N)`\n377|- Max 1,500 tokens\n378|\n379|### patterns.md\n380|Create `$SKILLS_HOME/<skill_name>/patterns.md`:\n381|- All concrete techniques, design patterns, algorithms from the book\n382|- Format: `## Pattern Name\\n**When to use**: ...\\n**How**: ...\\n**Trade-offs**: ...`\n383|- Max 2,000 tokens\n384|\n385|### cheatsheet.md\n386|Create `$SKILLS_HOME/<skill_name>/cheatsheet.md`:\n387|- Decision tables, comparison matrices, quick-reference rules\n388|- The content you'd want on a single printed page\n389|- Max 1,000 tokens\n390|\n391|---\n392|\n393|## Step 9 — Generate the master SKILL.md\n394|\n395|**CRITICAL TOKEN BUDGET: Keep SKILL.md body under 4,000 tokens.**\n396|Compaction truncates from the END — put the most important content FIRST.\n397|\n398|Create `$SKILLS_HOME/<skill_name>/SKILL.md`:\n399|\n400|```markdown\n401|---\n402|name: <skill_name>\n403|description: \"Knowledge base from \\\"<Full Title>\\\" by <Author(s)>. Use when applying <author>'s frameworks for <key topics, 3–6 terms>, studying the book, or referencing its concepts.\"\n404|allowed-tools:\n405|  - Read\n406|  - Grep\n407|argument-hint: [topic, framework name, or chapter number]\n408|---\n409|\n410|# <Full Title>\n411|**Author**: <Author(s)> | **Pages**: ~<N> | **Chapters**: <N> | **Generated**: <YYYY-MM-DD>\n412|\n413|## How to Use This Skill\n414|\n415|- **Without arguments** — load core frameworks for reference\n416|- **With a topic** — ask about `replication`, `pricing`, or another indexed topic; I find and read the relevant chapter\n417|- **With chapter** — ask for `ch05`; I load that specific chapter\n418|- **Browse** — ask \"what chapters do you have?\" to see the full index\n419|\n420|When you ask about a topic not covered in Core Frameworks below, I will read\n421|the relevant chapter file before answering.\n422|\n423|---\n424|\n425|## Core Frameworks & Mental Models\n426|<!-- ~2,000 tokens: the author's most important named frameworks and principles.\n427|     Preserve exact names. Write as \"Use X when Y\", \"Prefer X over Y because Z\".\n428|     This is a toolkit, not a summary. -->\n429|\n430|<generate 2,000 tokens of the most critical frameworks and insights here>\n431|\n432|---\n433|\n434|## Chapter Index\n435|\n436|| # | Title | Key Frameworks |\n437||---|-------|----------------|\n438|| [ch01](chapters/ch01-<slug>.md) | <Title> | <framework1>, <framework2> |\n439|| [ch02](chapters/ch02-<slug>.md) | <Title> | <framework1>, <framework2> |\n440|...\n441|\n442|## Topic Index\n443|\n444|<!-- Alphabetical. Major terms/frameworks → chapter(s) that cover them. -->\n445|- **<Term>** → ch<N>[, ch<N>]\n446|- **<Term>** → ch<N>\n447|\n448|## Supporting Files\n449|\n450|- [glossary.md](glossary.md) — all key terms with definitions\n451|- [patterns.md](patterns.md) — all techniques and design patterns\n452|- [cheatsheet.md](cheatsheet.md) — quick reference tables and decision guides\n453|\n454|---\n455|\n456|## Scope & Limits\n457|\n458|This skill covers the book content only. For hands-on implementation in your codebase,\n459|combine with project-specific tools. For topics beyond this book, check related skills\n460|or ask the agent directly.\n461|```\n462|\n463|---\n464|\n465|## Step 10 — Cleanup and report\n466|\n467|```bash\n468|PYTHON_BIN=\"${PYTHON_BIN:-python3}\"\n469|if ! command -v \"$PYTHON_BIN\" >/dev/null 2>&1; then\n470|  PYTHON_BIN=\"python\"\n471|fi\n472|\n473|\"$PYTHON_BIN\" - <<'PY'\n474|import os\n475|import shutil\n476|import tempfile\n477|from pathlib import Path\n478|shutil.rmtree(\n479|    os.environ.get(\"BOOK_SKILL_WORKDIR\", Path(tempfile.gettempdir()) / \"book_skill_work\"),\n480|    ignore_errors=True,\n481|)\n482|PY\n483|```\n484|\n485|Then report to the user:\n486|\n487|```\n488|✅ Skill created: $SKILLS_HOME/<skill_name>/\n489|\n490|📚 Book: <Full Title> — <Author>\n491|📄 Pages: ~<N> | Chapters: <N>\n492|\n493|Files generated:\n494|  SKILL.md         — core frameworks + index   (~X tokens)\n495|  chapters/        — <N> chapter summaries     (~X tokens each, ~X total)\n496|  glossary.md      — key terms                 (~X tokens)\n497|  patterns.md      — techniques & patterns     (~X tokens)\n498|  cheatsheet.md    — quick reference           (~X tokens)\n499|  ─────────────────────────────────────────────────────\n500|  Total skill size: ~X tokens (loaded on-demand, not all at once)\n501|", "total_lines": 521, "file_size": 20236, "truncated": true, "hint": "Use offset=501 to continue reading (showing 1-500 of 521 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|#!/usr/bin/env python3\n2|\"\"\"\n3|Extract text from a document file for book-to-skill processing.\n4|\n5|PDF extraction tries methods in order:\n6|  1. pdftotext (poppler-utils) — best quality\n7|  2. PyPDF2 — common Python library\n8|  3. pdfminer.six — thorough fallback\n9|\n10|EPUB extraction tries methods in order:\n11|  1. ebooklib + BeautifulSoup4 — best quality\n12|  2. zipfile + html.parser — stdlib fallback (no extra deps)\n13|\n14|Other supported formats:\n15|  - Plain text / Markdown / reStructuredText / AsciiDoc — direct read\n16|  - HTML — BeautifulSoup4 if available, then stdlib html.parser\n17|  - DOCX — python-docx if available, then stdlib ZIP/XML fallback\n18|  - RTF — striprtf if available, then regex fallback\n19|  - MOBI/AZW/AZW3 — Calibre ebook-convert if available\n20|\n21|Outputs:\n22|  <tempdir>/book_skill_work/full_text.txt  — full extracted text\n23|  <tempdir>/book_skill_work/metadata.json  — stats and metadata\n24|\n25|Set BOOK_SKILL_WORKDIR to override the output directory.\n26|\"\"\"\n27|\n28|import html\n29|import html.parser\n30|import importlib.util\n31|import json\n32|import os\n33|import re\n34|import shutil\n35|import subprocess\n36|import sys\n37|import tempfile\n38|import zipfile\n39|from pathlib import Path\n40|\n41|OUTPUT_DIR = Path(\n42|    os.environ.get(\n43|        \"BOOK_SKILL_WORKDIR\",\n44|        str(Path(tempfile.gettempdir()) / \"book_skill_work\"),\n45|    )\n46|)\n47|OUTPUT_TEXT = OUTPUT_DIR / \"full_text.txt\"\n48|OUTPUT_META = OUTPUT_DIR / \"metadata.json\"\n49|\n50|WORDS_PER_TOKEN = 0.75  # approximate\n51|\n52|TEXT_EXTENSIONS = {\".txt\", \".text\", \".md\", \".markdown\", \".rst\", \".adoc\", \".asciidoc\"}\n53|HTML_EXTENSIONS = {\".html\", \".htm\", \".xhtml\"}\n54|CALIBRE_EBOOK_EXTENSIONS = {\".mobi\", \".azw\", \".azw3\"}\n55|SUPPORTED_EXTENSIONS = {\n56|    \".pdf\", \".epub\", \".docx\", \".rtf\",\n57|    *TEXT_EXTENSIONS,\n58|    *HTML_EXTENSIONS,\n59|    *CALIBRE_EBOOK_EXTENSIONS,\n60|}\n61|\n62|PYTHON_DEPENDENCIES = {\n63|    \"docling\": \"docling\",\n64|    \"PyPDF2\": \"PyPDF2\",\n65|    \"pdfminer\": \"pdfminer.six\",\n66|    \"ebooklib\": \"ebooklib\",\n67|    \"bs4\": \"beautifulsoup4\",\n68|    \"docx\": \"python-docx\",\n69|    \"striprtf\": \"striprtf\",\n70|}\n71|\n72|\n73|def estimate_tokens(text: str) -> int:\n74|    return int(len(text.split()) / WORDS_PER_TOKEN)\n75|\n76|\n77|def supported_formats_message() -> str:\n78|    return \", \".join(sorted(SUPPORTED_EXTENSIONS))\n79|\n80|\n81|def python_module_available(module_name: str) -> bool:\n82|    return importlib.util.find_spec(module_name) is not None\n83|\n84|\n85|def missing_python_packages(module_names: list[str]) -> list[str]:\n86|    missing = []\n87|    for module_name in module_names:\n88|        if not python_module_available(module_name):\n89|            missing.append(PYTHON_DEPENDENCIES[module_name])\n90|    return missing\n91|\n92|\n93|def install_python_packages(packages: list[str]) -> bool:\n94|    if not packages:\n95|        return True\n96|\n97|    print(f\"Installing missing Python package(s): {', '.join(packages)}\")\n98|    try:\n99|        result = subprocess.run(\n100|            [sys.executable, \"-m\", \"pip\", \"install\", *packages],\n101|            text=True,\n102|            timeout=600,\n103|        )\n104|    except Exception as exc:\n105|        print(f\"Package installation failed: {exc}\", file=sys.stderr)\n106|        return False\n107|\n108|    importlib.invalidate_caches()\n109|    return result.returncode == 0\n110|\n111|\n112|def normalize_install_mode(argv: list[str]) -> str:\n113|    mode = os.environ.get(\"BOOK_SKILL_INSTALL_MISSING\", \"ask\").lower()\n114|    if \"--no-install-missing\" in argv:\n115|        return \"no\"\n116|    if \"--install-missing\" in argv:\n117|        idx = argv.index(\"--install-missing\")\n118|        if idx + 1 < len(argv) and not argv[idx + 1].startswith(\"--\"):\n119|            mode = argv[idx + 1].lower()\n120|        else:\n121|            mode = \"yes\"\n122|    if mode in {\"1\", \"true\", \"y\", \"yes\", \"install\"}:\n123|        return \"yes\"\n124|    if mode in {\"0\", \"false\", \"n\", \"no\", \"fallback\", \"skip\"}:\n125|        return \"no\"\n126|    return \"ask\"\n127|\n128|\n129|def offer_dependency_install(\n130|    *,\n131|    feature: str,\n132|    module_names: list[str],\n133|    fallback: str | None,\n134|    install_mode: str,\n135|) -> None:\n136|    packages = missing_python_packages(module_names)\n137|    if not packages:\n138|        return\n139|\n140|    message = f\"{feature} uses {', '.join(packages)} if installed\"\n141|    if fallback:\n142|        message += f\", otherwise {fallback}\"\n143|    message += \".\"\n144|    print(message)\n145|\n146|    should_install = False\n147|    if install_mode == \"yes\":\n148|        should_install = True\n149|    elif install_mode == \"ask\" and sys.stdin.isatty():\n150|        answer = input(\"Missing package(s) detected. Do you want to install? y=install, n=fallback: \").strip().lower()\n151|        should_install = answer in {\"y\", \"yes\", \"install\"}\n152|    else:\n153|        if fallback:\n154|            print(\"Non-interactive mode or install disabled; using fallback.\")\n155|        else:\n156|            print(\"Non-interactive mode or install disabled; installation skipped.\")\n157|\n158|    if not should_install:\n159|        if fallback:\n160|            print(f\"Using fallback: {fallback}.\")\n161|        return\n162|\n163|    if install_python_packages(packages):\n164|        still_missing = missing_python_packages(module_names)\n165|        if not still_missing:\n166|            print(\"Package installation complete.\")\n167|            return\n168|        print(f\"Package installation incomplete; still missing: {', '.join(still_missing)}\", file=sys.stderr)\n169|    else:\n170|        print(\"Package installation failed.\", file=sys.stderr)\n171|\n172|    if fallback:\n173|        print(f\"Using fallback: {fallback}.\")\n174|\n175|\n176|def prepare_dependencies(ext: str, extraction_mode: str, install_mode: str) -> None:\n177|    if ext == \".pdf\" and extraction_mode == \"technical\":\n178|        offer_dependency_install(\n179|            feature=\"Technical PDF extraction\",\n180|            module_names=[\"docling\"],\n181|            fallback=\"the PDF text fallback chain\",\n182|            install_mode=install_mode,\n183|        )\n184|\n185|    if ext == \".pdf\" and not shutil.which(\"pdftotext\"):\n186|        offer_dependency_install(\n187|            feature=\"PDF text extraction\",\n188|            module_names=[\"PyPDF2\", \"pdfminer\"],\n189|            fallback=\"any installed Python PDF parser; extraction fails if none are available\",\n190|            install_mode=install_mode,\n191|        )\n192|\n193|    if ext == \".epub\":\n194|        offer_dependency_install(\n195|            feature=\"EPUB extraction\",\n196|            module_names=[\"ebooklib\", \"bs4\"],\n197|            fallback=\"a stdlib ZIP/HTML parser\",\n198|            install_mode=install_mode,\n199|        )\n200|\n201|    if ext in HTML_EXTENSIONS:\n202|        offer_dependency_install(\n203|            feature=\"HTML extraction\",\n204|            module_names=[\"bs4\"],\n205|            fallback=\"a stdlib HTML parser\",\n206|            install_mode=install_mode,\n207|        )\n208|\n209|    if ext == \".docx\":\n210|        offer_dependency_install(\n211|            feature=\"DOCX extraction\",\n212|            module_names=[\"docx\"],\n213|            fallback=\"a stdlib ZIP/XML parser\",\n214|            install_mode=install_mode,\n215|        )\n216|\n217|    if ext == \".rtf\":\n218|        offer_dependency_install(\n219|            feature=\"RTF extraction\",\n220|            module_names=[\"striprtf\"],\n221|            fallback=\"a basic regex cleanup fallback\",\n222|            install_mode=install_mode,\n223|        )\n224|\n225|\n226|def read_text_file(path: str) -> str | None:\n227|    for encoding in (\"utf-8-sig\", \"utf-8\", \"cp1252\", \"latin-1\"):\n228|        try:\n229|            return Path(path).read_text(encoding=encoding)\n230|        except UnicodeDecodeError:\n231|            continue\n232|        except Exception:\n233|            return None\n234|    return None\n235|\n236|\n237|def extract_html_content(raw_html: str) -> str:\n238|    try:\n239|        from bs4 import BeautifulSoup\n240|        soup = BeautifulSoup(raw_html, \"html.parser\")\n241|        for element in soup([\"script\", \"style\", \"head\"]):\n242|            element.decompose()\n243|        return soup.get_text(separator=\"\\n\")\n244|    except ImportError:\n245|        parser = _HTMLTextExtractor()\n246|        parser.feed(raw_html)\n247|        return parser.get_text()\n248|\n249|\n250|def extract_html_file(path: str) -> str | None:\n251|    raw = read_text_file(path)\n252|    if raw is None:\n253|        return None\n254|    return extract_html_content(raw)\n255|\n256|\n257|def extract_docx_with_python_docx(docx_path: str) -> str | None:\n258|    try:\n259|        import docx\n260|        document = docx.Document(docx_path)\n261|        parts = [paragraph.text for paragraph in document.paragraphs if paragraph.text]\n262|        for table in document.tables:\n263|            for row in table.rows:\n264|                cells = [cell.text.strip() for cell in row.cells]\n265|                if any(cells):\n266|                    parts.append(\"\\t\".join(cells))\n267|        return \"\\n\".join(parts)\n268|    except ImportError:\n269|        return None\n270|    except Exception:\n271|        return None\n272|\n273|\n274|def extract_docx_with_zipfile(docx_path: str) -> str | None:\n275|    try:\n276|        import xml.etree.ElementTree as ET\n277|\n278|        with zipfile.ZipFile(docx_path) as zf:\n279|            xml_bytes = zf.read(\"word/document.xml\")\n280|        root = ET.fromstring(xml_bytes)\n281|        namespace = \"{http://schemas.openxmlformats.org/wordprocessingml/2006/main}\"\n282|        parts: list[str] = []\n283|        for paragraph in root.iter(f\"{namespace}p\"):\n284|            texts = [node.text for node in paragraph.iter(f\"{namespace}t\") if node.text]\n285|            if texts:\n286|                parts.append(\"\".join(texts))\n287|        return \"\\n\".join(parts) if parts else None\n288|    except Exception:\n289|        return None\n290|\n291|\n292|def extract_docx(docx_path: str) -> tuple[str, str]:\n293|    print(\"Trying python-docx...\", end=\" \", flush=True)\n294|    text = extract_docx_with_python_docx(docx_path)\n295|    if text and text.strip():\n296|        print(\"OK\")\n297|        return text, \"python-docx\"\n298|\n299|    print(\"not available\")\n300|    print(\"Trying stdlib DOCX parser...\", end=\" \", flush=True)\n301|    text = extract_docx_with_zipfile(docx_path)\n302|    if text and text.strip():\n303|        print(\"OK\")\n304|        return text, \"zipfile-docx\"\n305|\n306|    print(\"FAILED\")\n307|    print(\n308|        \"\\nERROR: Could not extract text from DOCX.\\n\"\n309|        \"Install python-docx for best results:\\n\"\n310|        \"  pip3 install python-docx\",\n311|        file=sys.stderr,\n312|    )\n313|    sys.exit(1)\n314|\n315|\n316|def strip_rtf_fallback(raw: str) -> str:\n317|    raw = re.sub(r\"\\\\'[0-9a-fA-F]{2}\", \" \", raw)\n318|    raw = re.sub(r\"\\\\par[d]?\", \"\\n\", raw)\n319|    raw = re.sub(r\"\\\\tab\", \"\\t\", raw)\n320|    raw = re.sub(r\"\\\\[a-zA-Z]+-?\\d* ?\", \"\", raw)\n321|    raw = raw.replace(\"{\", \"\").replace(\"}\", \"\")\n322|    return html.unescape(raw)\n323|\n324|\n325|def extract_rtf(rtf_path: str) -> tuple[str, str]:\n326|    raw = read_text_file(rtf_path)\n327|    if raw is None:\n328|        print(\"ERROR: Could not read RTF file\", file=sys.stderr)\n329|        sys.exit(1)\n330|\n331|    try:\n332|        from striprtf.striprtf import rtf_to_text\n333|        text = rtf_to_text(raw)\n334|        if text.strip():\n335|            return text, \"striprtf\"\n336|    except ImportError:\n337|        pass\n338|    except Exception:\n339|        pass\n340|\n341|    return strip_rtf_fallback(raw), \"rtf-regex\"\n342|\n343|\n344|def extract_with_ebook_convert(input_path: str) -> str | None:\n345|    if not shutil.which(\"ebook-convert\"):\n346|        return None\n347|    output_path = OUTPUT_DIR / \"ebook-convert-output.txt\"\n348|    try:\n349|        result = subprocess.run(\n350|            [\"ebook-convert\", input_path, str(output_path)],\n351|            capture_output=True, text=True, timeout=300\n352|        )\n353|        if result.returncode == 0 and output_path.exists():\n354|            text = output_path.read_text(encoding=\"utf-8\", errors=\"replace\")\n355|            if text.strip():\n356|                return text\n357|    except Exception:\n358|        pass\n359|    return None\n360|\n361|\n362|def extract_with_pdftotext(pdf_path: str) -> str | None:\n363|    if not shutil.which(\"pdftotext\"):\n364|        return None\n365|    try:\n366|        result = subprocess.run(\n367|            [\"pdftotext\", \"-layout\", pdf_path, \"-\"],\n368|            capture_output=True, text=True, timeout=120\n369|        )\n370|        if result.returncode == 0 and result.stdout.strip():\n371|            return result.stdout\n372|    except Exception:\n373|        pass\n374|    return None\n375|\n376|\n377|def extract_with_pypdf2(pdf_path: str) -> str | None:\n378|    try:\n379|        import PyPDF2\n380|        text_parts = []\n381|        with open(pdf_path, \"rb\") as f:\n382|            reader = PyPDF2.PdfReader(f)\n383|            for page in reader.pages:\n384|                try:\n385|                    text_parts.append(page.extract_text() or \"\")\n386|                except Exception:\n387|                    text_parts.append(\"\")\n388|        return \"\\n\".join(text_parts)\n389|    except ImportError:\n390|        return None\n391|    except Exception:\n392|        return None\n393|\n394|\n395|def extract_with_pdfminer(pdf_path: str) -> str | None:\n396|    try:\n397|        from pdfminer.high_level import extract_text\n398|        return extract_text(pdf_path)\n399|    except ImportError:\n400|        return None\n401|    except Exception:\n402|        return None\n403|\n404|\n405|def extract_with_ebooklib(epub_path: str) -> str | None:\n406|    try:\n407|        import ebooklib\n408|        from ebooklib import epub\n409|        from bs4 import BeautifulSoup\n410|\n411|        book = epub.read_epub(epub_path)\n412|        parts = []\n413|        for item in book.get_items_of_type(ebooklib.ITEM_DOCUMENT):\n414|            soup = BeautifulSoup(item.get_content(), \"html.parser\")\n415|            parts.append(soup.get_text(separator=\"\\n\"))\n416|        return \"\\n\\n\".join(parts)\n417|    except ImportError:\n418|        return None\n419|    except Exception:\n420|        return None\n421|\n422|\n423|class _HTMLTextExtractor(html.parser.HTMLParser):\n424|    \"\"\"Minimal HTML → plain text converter using stdlib only.\"\"\"\n425|\n426|    SKIP_TAGS = {\"script\", \"style\", \"head\"}\n427|\n428|    def __init__(self):\n429|        super().__init__()\n430|        self._parts: list[str] = []\n431|        self._skip_depth = 0\n432|        self._current_skip: str | None = None\n433|\n434|    def handle_starttag(self, tag, attrs):\n435|        if tag in self.SKIP_TAGS:\n436|            self._skip_depth += 1\n437|        if tag in (\"p\", \"br\", \"h1\", \"h2\", \"h3\", \"h4\", \"h5\", \"h6\", \"li\", \"div\"):\n438|            self._parts.append(\"\\n\")\n439|\n440|    def handle_endtag(self, tag):\n441|        if tag in self.SKIP_TAGS and self._skip_depth:\n442|            self._skip_depth -= 1\n443|\n444|    def handle_data(self, data):\n445|        if not self._skip_depth:\n446|            self._parts.append(data)\n447|\n448|    def get_text(self) -> str:\n449|        return html.unescape(\"\".join(self._parts))\n450|\n451|\n452|def extract_with_zipfile(epub_path: str) -> str | None:\n453|    \"\"\"stdlib-only EPUB extractor: unzip → parse HTML files.\"\"\"\n454|    try:\n455|        with zipfile.ZipFile(epub_path) as zf:\n456|            names = zf.namelist()\n457|            # Read OPF spine to get reading order, fall back to sorted xhtml files\n458|            spine_order: list[str] = []\n459|            opf_files = [n for n in names if n.endswith(\".opf\")]\n460|            if opf_files:\n461|                opf_text = zf.read(opf_files[0]).decode(\"utf-8\", errors=\"replace\")\n462|                spine_order = re.findall(r'href=[\"\\']([^\"\\']+\\.(?:xhtml|html))[\"\\']', opf_text)\n463|\n464|            html_files = spine_order or sorted(\n465|                n for n in names if n.endswith((\".html\", \".xhtml\"))\n466|            )\n467|            if not html_files:\n468|                return None\n469|\n470|            parts = []\n471|            for name in html_files:\n472|                try:\n473|                    raw = zf.read(name).decode(\"utf-8\", errors=\"replace\")\n474|                    parser = _HTMLTextExtractor()\n475|                    parser.feed(raw)\n476|                    parts.append(parser.get_text())\n477|                except Exception:\n478|                    continue\n479|            return \"\\n\\n\".join(parts) if parts else None\n480|    except Exception:\n481|        return None\n482|\n483|\n484|def extract_epub(epub_path: str) -> tuple[str, str]:\n485|    \"\"\"Return (text, method) for an EPUB file.\"\"\"\n486|    print(\"Trying ebooklib + BeautifulSoup4...\", end=\" \", flush=True)\n487|    text = extract_with_ebooklib(epub_path)\n488|    if text and text.strip():\n489|        print(\"OK\")\n490|        return text, \"ebooklib\"\n491|\n492|    print(\"not available\")\n493|    print(\"Trying stdlib zipfile parser...\", end=\" \", flush=True)\n494|    text = extract_with_zipfile(epub_path)\n495|    if text and text.strip():\n496|        print(\"OK\")\n497|        return text, \"zipfile\"\n498|\n499|    print(\"FAILED\")\n500|    print(\n501|", "total_lines": 818, "file_size": 27239, "truncated": true, "hint": "Use offset=501 to continue reading (showing 1-500 of 818 lines)", "is_binary": false, "is_image": false} read_file 


c0:
  # Análise do repositório `virgiliojr94/book-to-skill`

  ## Problema
  A dificuldade de reaproveitar conhecimento de livros técnicos após a leitura. Os workarounds usuais não funcionam bem:
  - Procurar no PDF retorna páginas, não respostas.
  - Perguntar a um LLM geralmente gera alucinação ou ausência do conteúdo.
  - Fazer anotações resulta em documentos que raramente são revisitados.
  - Colocar o livro todo no contexto queima o orçamento de tokens e fica caro.

  ## Tema principal
  Transformar livros/documentos técnicos (PDF, EPUB, DOCX, etc.) em **skills** estruturadas para Claude Code (e Amp), prontas para estudo, consulta e uso durante o trabalho. Em vez de jogar o texto cru no contexto, o projeto extrai: frameworks nomeados pelo autor, princípios, técnicas, anti-patterns, glossário, cheatsheet e índices de capítulos/tópicos — em arquivos compactos e sob demanda.

  ## Razão do hype
  - **Custo/token reduzido:** chapters ficam em disco e são carregados sob demanda, evitando custo de 200K tokens de um livro inteiro.
  - **Compilação vs RAG:** alega ser diferente de RAG porque faz uma análise profunda única (extraindo a estrutura do autor) em vez de busca por similaridade.
  - **Versatilidade:** funciona com muitos formatos e tem fallbacks inteligentes quando dependências estão ausentes.
  - **Aderência ao fluxo do desenvolvedor:** integra diretamente no CLI do Claude Code como `/book-to-skill ...`.

  ## Tecnologias principais
  - Python
  - Extração PDF: `pdftotext` (poppler), `PyPDF2`, `pdfminer.six`, `docling`
  - Extração EPUB: `ebooklib`, `beautifulsoup4`, fallback stdlib `zipfile`
  - Outros formatos: `python-docx`, `beautifulsoup4`, `striprtf`, Calibre `ebook-convert`
  - Markdown estruturado para saída (`SKILL.md`, `chapters/`, `glossary.md`, `patterns.md`, `cheatsheet.md`)
  - Sem dependências externas obrigatórias para formatos básicos (`txt`, `md`, `html`, etc.)  


c0:
    [{"id": "chatcmpl-tool-88390f2415147d26", "call_id": "chatcmpl-tool-88390f2415147d26", "response_item_id": "fc_chatcmpl-tool-88390f2415147d26", "type": "function", "function": {"name": "terminal", "arguments": "{\"command\": \"git clone https://github.com/thananon/9arm-skills.git /home/ice/9arm-skills 2>&1\", \"timeout\": 60}"}}]


c0:
  {"output": "Cloning into '/home/ice/9arm-skills'...", "exit_code": 0, "error": null}

  [Subdirectory context discovered: 9arm-skills/CLAUDE.md]
  Skills are organized into bucket folders under `skills/`:

  - `engineering/` — daily code work
  - `productivity/` — daily non-code workflow tools
  - `misc/` — kept around but rarely used
  - `personal/` — tied to my own setup, not promoted
  - `in-progress/` — drafts not yet ready to ship
  - `deprecated/` — no longer used

  Every skill in `engineering/`, `productivity/`, or `misc/` must have a reference in the top-level `README.md` and an entry in `.claude-plugin/plugin.json`. Skills in `personal/`, `in-progress/`, and `deprecated/` must not appear in either.

  Each skill entry in the top-level `README.md` must link the skill name to its `SKILL.md`.

  Each bucket folder has a `README.md` that lists every skill in the bucket with a one-line description, with the skill name linked to its `SKILL.md`. terminal 


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c0:
  {"content": "1|# 9arm-skills\n2|\n3|Agent skills loaded by Claude Code.\n4|\n5|## Layout\n6|\n7|Skills live under `skills/`, grouped into buckets:\n8|\n9|- `engineering/` — daily code work\n10|- `productivity/` — daily non-code workflow tools\n11|- `misc/` — kept around but rarely used\n12|- `personal/` — tied to my own setup, not promoted\n13|- `in-progress/` — drafts not yet ready to ship\n14|- `deprecated/` — no longer used\n15|\n16|Each skill is its own directory containing a `SKILL.md` (with YAML frontmatter — `name` and `description`) and any bundled scripts or reference files.\n17|\n18|## Install\n19|\n20|### With `npx skills` (Recommended - works for every agent)\n21|\n22|```bash\n23|npx skills add thananon/9arm-skills\n24|```\n25|\n26|### Alternative - Bash script\n27|\n28|For my own dev loop — symlink every shippable skill into `~/.claude/skills/`:\n29|\n30|```bash\n31|./scripts/link-skills.sh\n32|```\n33|\n34|List every `SKILL.md` in the repo:\n35|\n36|```bash\n37|./scripts/list-skills.sh\n38|```\n39|\n40|## Reference\n41|\n42|### Engineering\n43|\n44|- **[debug-mantra](./skills/engineering/debug-mantra/SKILL.md)** — Four-mantra debugging discipline: reproduce → trace the fail path → falsify the hypothesis → cross-reference every breadcrumb. Recites verbatim at session start, then applies in order before any fix.\n45|- **[post-mortem](./skills/engineering/post-mortem/SKILL.md)** — Write the canonical engineering record of a fixed bug — root cause, mechanism, fix, validation, how it slipped through. Engineer-audience; refuses to draft without a reliable repro, known cause, and validated fix.\n46|- **[scrutinize](./skills/engineering/scrutinize/SKILL.md)** — Outsider-perspective end-to-end review of a plan, PR, or code change. Questions intent (is there a simpler way?), traces the actual code path, and verifies the change does what it claims. Output is concise, actionable, with rationale.\n47|\n48|### Productivity\n49|\n50|- **[management-talk](./skills/productivity/management-talk/SKILL.md)** — Rewrite engineer-to-engineer content for engineering-org leadership and shape it for the channel it's going to (JIRA, Slack, async standup, email, meeting talking-points).\n51|\n52|### Misc\n53|\n54|_(none yet)_\n55|", "total_lines": 54, "file_size": 2065, "truncated": false, "is_binary": false, "is_image": false} read_file 


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c0:
  {"content": "1|---\n2|name: debug-mantra\n3|description: Four-mantra debugging discipline — reproduce, trace the fail path, falsify the hypothesis, cross-reference every breadcrumb. Recite the mantra block verbatim at the start of any debugging session, then apply the four steps in order before proposing any fix. Trigger on /debug-mantra and proactively whenever debugging starts — user reports a bug, says something is broken/throwing/failing, asks to debug/diagnose/investigate an issue, or pastes a stack trace or error log.\n4|---\n5|\n6|# Debug Mantra\n7|\n8|Four-step discipline for any debug session. Recite verbatim, then apply in order.\n9|\n10|## Recite this — verbatim, as the first thing in your first response\n11|\n12|> **Mantra:**\n13|> 1. **First is reproducibility.** Can the issue be reproduced reliably?\n14|> 2. **Know the fail path.** Debugger first; then source trace + knob enumeration; then in-code instrumentation.\n15|> 3. **Question your hypothesis.** What would disprove it?\n16|> 4. **Every run is a breadcrumb.** Cross-reference all of them.\n17|\n18|Then begin work.\n19|\n20|---\n21|\n22|## 1. Reproduce reliably\n23|\n24|Build a runnable repro before anything else.\n25|\n26|- **Reliable repro** → capture the exact steps, inputs, and environment as a runnable artifact: failing test, curl script, CLI invocation, replay harness.\n27|- **Flaky repro** → the bug is not yet debuggable. Raise the rate first: loop the trigger, parallelise, add stress, narrow timing windows, inject sleeps. 50% flake is debuggable; 1% is not.\n28|- **No repro at all** → stop. Say so explicitly. Ask the user for env access, captured artifacts (HAR, log dump, core), or permission to instrument. Do **not** proceed to hypothesise.\n29|\n30|Target: a fast (1–5 s), deterministic pass/fail signal. Pin time, seed the RNG, freeze network, isolate filesystem.\n31|\n32|## 2. Know the fail path\n33|\n34|Once reproducible, find *where* the code breaks and *what stops it from breaking*. The differential narrows the search. Try in this order — escalate only when the prior tactic fails.\n35|\n36|1. **Attach a debugger.** If the env supports it, attach and step to the failure site. One breakpoint beats ten logs. Do this **before** turning any knobs.\n37|2. **Source trace + knob enumeration.** If no debugger (or it can't reach the bug), trace the code path end-to-end and list every knob that can influence the outcome:\n38|   - config flags, env vars, feature toggles\n39|   - branch conditions, input shape\n40|   - timing, concurrency, build options\n41|   Each knob is a candidate axis to flip in the differential. Flip one at a time.\n42|3. **In-code instrumentation.** If outside knobs can't move the failure, go inside: `printf` / log statements at the suspected fail site, dump the relevant internal state. Tag every probe with a unique prefix (e.g. `[DBG-a4f2]`) so cleanup is a single grep. Let the trace show where reality diverges from your model.\n43|\n44|## 3. Falsify the hypothesis\n45|\n46|When a candidate root cause surfaces, scrutinise it **before** testing it.\n47|\n48|- Does it actually explain the symptom end-to-end? Walk it through.\n49|- What is the simplest **proof**? What is the cleanest **disproof**?\n50|- Run the **disproof first**. If the hypothesis survives, it's real. If it dies, you saved yourself from chasing a phantom.\n51|- Generate 3–5 ranked hypotheses, not one. Single-hypothesis thinking anchors on the first plausible idea.\n52|\n53|## 4. Every run is a breadcrumb\n54|\n55|Maintain a running **ledger** of every experiment in this session. Each entry: what changed, what happened, what it ruled in or out.\n56|\n57|- When a new hypothesis surfaces, walk the ledger. Does it hold for **every** prior observation, not just the most recent?\n58|- If any past run contradicts it, the hypothesis is wrong or incomplete — refine or discard.\n59|- When in doubt, design the **single experiment** whose outcome makes it certain. Run that next, instead of churning on adjacent runs.\n60|- Update the ledger after every run. It is your memory across the session.\n61|\n62|---\n63|\n64|## Operating rules\n65|\n66|- Recite the mantra block **once** per debug session, in your first response. Do not re-recite mid-session.\n67|- Recite **verbatim**. Never paraphrase, shorten, or skip lines of the recital.\n68|- If the user says \"skip the mantra\" → skip the recital but still apply the four steps silently.\n69|- Apply the four steps **in order**:\n70|  - Do not propose a fix before #1 is satisfied (reliable repro exists).\n71|  - Do not start testing hypotheses before #2 has narrowed the fail path.\n72|  - Do not commit to a hypothesis before #3 has tried to disprove it.\n73|  - Do not declare a hypothesis correct until #4 confirms it against every prior breadcrumb.\n74|- If you catch yourself proposing a fix without a reliable repro, stop and return to step 1.\n75|- The mantra is a constraint **you** carry through the session — not advice to deliver back to the user.\n76|", "total_lines": 75, "file_size": 4752, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|---\n2|name: post-mortem\n3|description: Write the canonical engineering record of a fixed bug — root cause, mechanism, fix, validation, and how it slipped through. Engineer-audience, code identifiers welcome. Use after a debug session lands a fix, before closing the ticket. Trigger on /post-mortem, when the user says \"write the post-mortem / postmortem / RCA / root cause analysis\", \"document this fix\", \"write up the root cause\", \"close out this bug with a writeup\", or hands you a fixed-and-validated bug and asks for the writeup.\n4|---\n5|\n6|# Post-mortem\n7|\n8|The canonical engineering record of a bug fix. Written **after** debugging lands a real fix, **for** other engineers (and future-you, who will have forgotten everything in 6 months). Code identifiers are welcome here — this is the artifact that lets the next person recover the mental model fast.\n9|\n10|For the up-the-org version of this same content, hand the finished post-mortem to [`management-talk`](../../productivity/management-talk/SKILL.md). They compose: post-mortem owns the engineering truth, management-talk reframes it for leadership.\n11|\n12|## When to invoke\n13|\n14|- \"/post-mortem\"\n15|- \"write the post-mortem / postmortem / RCA / root-cause analysis\"\n16|- \"document this fix\" / \"write up the root cause\" / \"close out this bug with a writeup\"\n17|- After a debug session has clearly landed a fix, proactively offer to draft one.\n18|\n19|## When NOT to use\n20|\n21|- **Bug not fixed yet, or fix not validated.** A post-mortem of a hypothesis is misleading. Refuse and tell the user what's missing.\n22|- **Customer-visible outage / incident.** Those need a separate incident report (timeline, blast radius, paging history, comms). This skill is bug-fix scope. Flag and confirm before producing one.\n23|- **Trivial fix** (typo, obvious one-liner). The PR description is the record. Don't manufacture ceremony.\n24|\n25|## Required inputs — refuse to draft without these\n26|\n27|Before writing a single line, confirm all four. If any are missing, list what's missing and stop:\n28|\n29|- [ ] **Reliable repro** exists (not \"happens sometimes\" — a deterministic or high-rate-flake repro the next person can run).\n30|- [ ] **Root cause is known** (the mechanism is identified, not a hypothesis).\n31|- [ ] **Fix is identified** (PR / commit / branch pointer).\n32|- [ ] **Fix is validated** (the original repro now passes; the customer workload / failing test now succeeds).\n33|\n34|These map directly to `debug-mantra` steps 1–4. If you came in via `debug-mantra`, the breadcrumb ledger from step 4 is your raw material — pull from it.\n35|\n36|## Structure\n37|\n38|Use these blocks in this order. **Summary, Root cause, Fix, and Validation are mandatory.** The rest are conditional but usually present.\n39|\n40|### 1. Summary _(mandatory)_\n41|One paragraph. What broke, in user/workload terms. What fixed it, in one sentence. JIRA key, PR number, owner. A reader who stops here should have the right answer.\n42|\n43|### 2. Symptom\n44|What was actually observed. Test output, error message, log line, perf number, customer report. Concrete identifiers — don't paraphrase the failure mode.\n45|\n46|### 3. Root cause _(mandatory)_\n47|The actual bug mechanism. **Code identifiers welcome and expected** — function names, file paths, struct fields, branch conditions, commit SHAs of the offending change. Walk the cause chain end-to-end. This is the most expensive section and the reason the post-mortem exists at all. Future-you will live or die by how clearly you write this.\n48|\n49|### 4. Why it produced the symptom\n50|Link the root cause to the symptom. Often non-obvious — the bug is in `tadaLaunchPrepare` but the visible failure is a customer training run hanging hours later. Walk the chain so a reader who only knows the symptom can connect it back to the cause without re-deriving it.\n51|\n52|### 5. Fix _(mandatory)_\n53|What changed and **why this change addresses the root cause** rather than hiding the symptom. Link to PR / commit. If a previous fix attempt papered over the symptom, name it and explain what was wrong with it — that history is part of the cause.\n54|\n55|### 6. How it was found\n56|Short. The debugging path:\n57|- What repro made it deterministic.\n58|- What tools cracked it (debugger, source tracing, knob enumeration, in-code instrumentation — the `debug-mantra` step 2 cascade).\n59|- Hypotheses tried and rejected, with the one-line reason each was rejected. (Pull from the breadcrumb ledger.)\n60|- The single experiment that confirmed the cause.\n61|\n62|This section is for the next debugger — make it learnable.\n63|\n64|### 7. Why it slipped through\n65|What allowed this bug to reach the branch / release / customer. Pick the real reason:\n66|- CI gap (no test exercises this path / configuration).\n67|- Latent code (correct when written, broken by a later change in a different file).\n68|- Workload gap (no real workload reached this code path until now).\n69|- Incomplete prior fix (defensive check hid the symptom; root cause untouched).\n70|- Review miss (the change was reviewable; the implication wasn't).\n71|\n72|If the honest answer is \"no good reason — we should have caught this,\" say so. **Blameless** — describe the gap, not the person.\n73|\n74|### 8. Validation _(mandatory)_\n75|How we know the fix works. Concrete:\n76|- Original failing test now passes (test name, link).\n77|- Customer workload now completes (workload identifier, run link).\n78|- Perf regression resolved (number before, number after).\n79|- Stress / soak / fuzz run completed clean (duration, scale).\n80|- Other affected configurations / workloads also tested.\n81|\n82|If you only validated one configuration, say so explicitly — *\"validated on Llama-2-70B / 8 GPUs / DeepSpeed; not retested on other workloads.\"* Don't imply broader coverage than you actually have.\n83|\n84|### 9. Action items / follow-ups\n85|Concrete next-steps that aren't in the fix PR itself. Each item: what + owner + tracking artifact.\n86|\n87|- Regression test added at \\<seam\\>. (Owner, test name.)\n88|- Refactor to prevent class of bug. (Owner, ticket.)\n89|- CI gap closed: \\<new check\\>. (Owner, PR.)\n90|- Doc / runbook updated. (Owner, link.)\n91|- Related ticket filed for \\<adjacent issue you noticed\\>. (Owner, key.)\n92|\n93|If there are no action items, write *\"None — the fix is sufficient and no class-of-bug follow-up is warranted.\"* Don't manufacture action items to look thorough.\n94|\n95|## Tone\n96|\n97|This is engineer-to-engineer. Different from `management-talk`:\n98|\n99|- **Code identifiers are first-class.** `tadaLaunchPrepare`, `tada/prim.h::syncWaitPeer`, `scratchBuf`, commit SHAs, line numbers — keep them. The whole point is that future engineers can grep their way back to the change.\n100|- **Mechanism over narrative.** Walk the actual cause chain. Don't soften it into \"a synchronization issue\" — say which function skipped which event under which gate.\n101|- **Active voice, concrete subjects, short paragraphs.** Same rule as everywhere else.\n102|- **No hedging.** \"We believe\" / \"appears to\" / \"may have\" — drop. State it or don't write it.\n103|- **Blameless.** Describe the bug, the gap, and the fix. Never \"X should have caught this.\" The CI gap is the failure mode, not the person.\n104|- **No advocacy.** A post-mortem records what happened and what's next. If you want to argue for a refactor, that's a separate proposal — link to it from the action items.\n105|\n106|## Output flow\n107|\n108|1. **Confirm all four required inputs are satisfied.** If any are missing, list them and stop. Do not draft.\n109|2. **Confirm where it goes** (default: JIRA comment on the source ticket). Other valid destinations: PR description, `docs/postmortems/<ticket>.md`, internal wiki page. The shape is the same — only the wrapping changes.\n110|3. **Produce the draft** as a single chat block.\n111|4. **Sign-off before posting.** If posting back to JIRA, show the exact ADF payload, wait for explicit *\"post it\"* / *\"go ahead\"* / *\"yes,\"* then `POST /rest/api/3/issue/<KEY>/comment`. Print-only output needs no approval.\n112|5. **Offer the management-talk handoff:** *\"Want a leadership-flavored version? I can hand this to `management-talk`.\"* Don't do it automatically.\n113|\n114|## Worked example — Tada hang in dumbModel (JIRA-12345)\n115|\n116|> **Summary.** Tada's single-stream fast-path skipped a required cross-stream synchronization, causing kernels to launch before scratch-buffer writes were visible. Triggered reliably by dumbModel on LLM-7B fine-tuning, hanging the workload at every eval step. Fixed by removing the unsafe fast-path and tightening a device-side check. JIRA-12345, PR org/platform#5751, owner Alex (Tada team).\n117|>\n118|> **Symptom.** 8-GPU LLM-7B fine-tuning under dumbModel hung indefinitely at the first eval step. No error, no timeout — busy-spin in `tadaKernel_AllReduce_f32_RING`. Reproduced on every run.\n119|>\n120|> **Root cause.** The single-stream fast-path in `tadaLaunchPrepare` / `tadaLaunchKernel` / `tadaLaunchFinish` (gated on `scheduler->numStreams == 1 && !plan->persistent`) skipped the cross-stream event between `launchStream` and `handle->shared->deviceStream`. dumbModel hits this gate exactly. The kernel was launched before the IPC publish / scratch-buffer writes on `deviceStream` (which populate `scratchBuf`) were visible to `launchStream`. In the kernel: `scratchBuf == NULL` → stray pointer dereference → ring ready-flag read from garbage memory → thread spins forever waiting for a ready signal that will never arrive.\n121|>\n122|> **Why it produced the symptom.** The hang lives in the all-reduce ring waitloop, which is the last visible thing in the call stack — but the actual bug is at launch-prep, several frames earlier. The skipped sync is silent until a workload triggers the exact gate (single-stream, non-persistent), and dumbModel's reduce-scatter pattern hits it at every eval step.\n123|>\n124|> **Fix.** PR #5751 removes the single-stream fast-path entirely (the saving was negligible vs. the safety it bypassed) and adds a device-side null check on `scratchBuf` before dereference, so the same class of bug fails loudly instead of silently spinning. A previous attempt (PR #5612) added a host-side defensive check after IPC publish that hid the symptom in some paths but left the underlying race in place — that change is also reverted.\n125|>\n126|> **How it was found.** Reproducer narrowed from \"8-GPU LLM-7B hangs sometimes\" to a deterministic 30s repro by pinning to a single eval step on a 2-GPU subset. Initial hypothesis: kernel launch ordering on `launchStream`. Disproved by the debugger — the kernel was correctly enqueued. Second hypothesis: scratch-buffer init race. Confirmed by adding `[DBG-7af3]` instrumentation in `tadaLaunchPrepare` printing `scratchBuf` and a `deviceStream` event-record timestamp; the launch happened before the publish completed. Single experiment that nailed it: forcing `numStreams = 2` made the bug disappear, isolating the gate.\n127|>\n128|> **Why it slipped through.** Latent code path. The single-stream fast-path was added in March under the assumption that dumbModel paths always took the multi-stream route. That assumption was true at the time. A May change to dumbModel's launcher began collapsing eval steps to a single stream — at which point the gate flipped. Tada's CI did not exercise the single-stream + IPC + scratch-buffer combination; the customer workload was the first to hit it.\n129|>\n130|> **Validation.** Original LLM-7B / 8-GPU / dumbModel workload now completes a full eval pass cleanly (3 consecutive 2-hour runs). `tada-tests` `all_reduce_perf` regression suite green. Soak run: 6 hours on 8 GPUs, no hang. Not retested on other model sizes or non-dumbModel workloads — both go through the multi-stream path and were never affected.\n131|>\n132|> **Action items.**\n133|> - Regression test added: `tests/single_stream_ipc_publish_test.cpp` exercising the previously-uncovered gate. (Alex, merged in PR #5751.)\n134|> - CI gap: add a single-stream + IPC matrix entry to nightly. (Alex, JIRA-12346.)\n135|> - Doc update: Tada launch-fast-path invariants documented in `docs/launch_synchronization.md`. (Alex, PR #5752.)\n136|> - Related: audit other `numStreams == 1` fast-paths for the same class of bug. (Filed as JIRA-12347.)\n137|\n138|What this post-mortem does that the management-talk version didn't:\n139|\n140|- Names every code identifier (`tadaLaunchPrepare`, `scratchBuf`, `numStreams`, `handle->shared->deviceStream`).\n141|- Walks the cause chain end-to-end so the reader can grep their way to the offending lines.\n142|- Names the *prior fix attempt* (PR #5612) and what was wrong with it.\n143|- Documents the *exact experiment* that nailed the cause (`numStreams = 2` made it disappear).\n144|- States validation coverage honestly — \"not retested on other model sizes\" is information, not a hole.\n145|- Action items have owners and tracking artifacts.\n146|\n147|## Rules\n148|\n149|- **Refuse to draft without all four required inputs.** A post-mortem of a hypothesis is worse than no post-mortem.\n150|- **Never invent root cause, owner, validation runs, or action items.** If a section's facts aren't there, ask. Don't fill the gap with plausible prose.\n151|- **Never strip code identifiers** in the engineering record. They are the index. The leadership reframe is `management-talk`'s job, not yours.\n152|- **Blameless.** Describe gaps and bugs, never people.\n153|- **State validation coverage honestly.** If you only tested one config, say so. Implying broader coverage is the failure mode that breeds repeat regressions.\n154|- **Get sign-off before posting to JIRA.** Print-only output needs no approval. Never post to non-JIRA destinations from this skill.\n155|- **One iteration is normal, three is a smell.** If the user is still revising on the third pass, ask what specific section is wrong — don't keep tweaking blindly.\n156|", "total_lines": 155, "file_size": 13470, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|---\n2|name: scrutinize\n3|description: Outsider-perspective end-to-end review of a plan, PR, or code change. First questions intent and whether a simpler/more elegant approach would achieve the same goal, then traces the actual code path (not just the diff) to verify the change does what it claims. Output is concise, actionable, and every call carries its rationale. Trigger on /scrutinize and proactively whenever the user asks to review, audit, sanity-check, or get a second opinion on a plan, PR, diff, design doc, or proposed code change.\n4|---\n5|\n6|# Scrutinize\n7|\n8|Stand outside the change and ask whether it should exist at all, then verify it actually does what it claims end-to-end.\n9|\n10|## Operating stance\n11|\n12|- **Outsider.** Forget who wrote it and why they think it's right. Read the artifact cold.\n13|- **End-to-end, not diff-local.** The diff is the entry point, not the scope. Follow the call graph through real code paths.\n14|- **Actionable, concise, with rationale.** Every finding states *what to change*, *why*, and *what evidence* led you there. No filler, no restating the diff back.\n15|\n16|## Workflow\n17|\n18|Run these in order. Do not skip ahead.\n19|\n20|### 1. Intent — what is this actually trying to do?\n21|\n22|- State the goal in one sentence, in your own words. If you cannot, the artifact is underspecified — say so and stop.\n23|- Ask: **is there a simpler, smaller, or more elegant way to achieve the same goal?** Consider:\n24|  - Doing nothing (is the problem real / load-bearing?).\n25|  - Using something that already exists in the codebase instead of adding new surface.\n26|  - A smaller change that solves 90% of the goal with 10% of the risk.\n27|  - Solving it at a different layer (config vs code, framework vs app, build vs runtime).\n28|- If a better alternative exists, name it explicitly with rationale. This is the most valuable thing you can output — surface it before the line-by-line review.\n29|\n30|### 2. Trace — walk the actual code path\n31|\n32|- For each behavior the change claims, trace the path end-to-end through the real code, not just the lines in the diff:\n33|  - Entry point → call sites → branches taken → state mutated → exit / return / side effect.\n34|  - Include the unchanged code on either side of the diff. Bugs hide at the seams.\n35|- For a plan or design doc: trace the proposed flow against the existing system. Where does it touch reality? What does it assume that isn't true?\n36|- Note every place the trace surprises you (unexpected branch, dead code reached, state you didn't know existed). Surprises are signal.\n37|\n38|### 3. Verify — does it actually do what it claims?\n39|\n40|For each claim the change/plan makes, answer:\n41|\n42|- **Does the code path you just traced actually produce that behavior?** Walk it explicitly. \"It claims X. Path: A → B → C. At C, [observation]. Therefore [holds / doesn't hold].\"\n43|- **What inputs / states would break it?** Edge cases, concurrent callers, error paths, partial failures, retries, empty/null/unicode/huge inputs, ordering assumptions.\n44|- **What does it silently change?** Performance, error semantics, observability, contract for other callers, on-disk / on-wire format.\n45|- **How is it tested?** Do the tests actually exercise the traced path, or do they pass while skipping it (mocks that hide the bug, asserts on intermediate state, happy path only)?\n46|\n47|### 4. Report\n48|\n49|Output one tight section per finding. Order by severity (blocker → major → nit). For each:\n50|\n51|- **Finding** — one sentence, specific. Cite `file:line` when applicable.\n52|- **Why it matters** — the consequence, not the principle.\n53|- **Evidence** — the trace step or input that exposes it.\n54|- **Suggested change** — concrete, minimal.\n55|\n56|Close with a one-line verdict: ship / fix-then-ship / rework / reject — with the single biggest reason.\n57|\n58|## Operating rules\n59|\n60|- **No rubber-stamps.** \"LGTM\" is not an output. If you genuinely find nothing, say what you traced and what you checked, so the user can judge whether your review covered the surface they cared about.\n61|- **Cite or it didn't happen.** Every claim about the code references a specific path, file, or line. No vague \"this might break under load.\"\n62|- **Distinguish claim from verification.** \"The PR says X\" and \"I traced X and confirmed / refuted it\" are different — keep them separate in the output.\n63|- **One simpler-alternative pass is mandatory.** Even on small changes, spend one breath asking if the whole thing is necessary. Skip only if the user explicitly says \"don't question scope.\"\n64|- **Don't pad with style nits when there's a structural problem.** If step 1 or step 2 surfaces a real issue, lead with it; defer nits or drop them.\n65|- **No flattery, no hedging.** \"This is a great PR but...\" adds nothing. State the finding.\n66|", "total_lines": 65, "file_size": 4692, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|---\n2|name: management-talk\n3|description: Rewrite engineer-to-engineer content for engineering-org leadership (VPs, directors, PMs, release managers, execs in an engineering-savvy company) and shape it for the channel it is going to — JIRA comment, Slack post, async standup line, email, or meeting talking-points. Trigger when the user asks to write/rewrite for management / exec / VP / director / PM / release manager, asks for an \"executive summary / leadership update / status update\", says \"make this less technical / less jargony\", or asks for a slack / email / standup / meeting version of work originally written engineer-to-engineer.\n4|---\n5|\n6|# Management Talk\n7|\n8|Same audience and translation rules as a written status report, but **shaped for the channel** — JIRA comment, Slack post, async standup, email, or meeting talking-points. The audience reads code names but not code. The channel decides the length, formatting, and how much structure to leave on the page.\n9|\n10|Use this any time engineering content needs to flow up the org, sideways into product/release, or into a non-engineering meeting — regardless of the destination.\n11|\n12|## When to invoke\n13|\n14|- \"write something for management / exec / VP / director / PM / release manager\"\n15|- \"rewrite this for [non-eng audience]\"\n16|- \"make this non-technical\" / \"less techy\" / \"less jargony\"\n17|- \"send a slack update / standup note / email\" *about a piece of engineering work*\n18|- \"executive summary\" / \"exec summary\" / \"leadership update\" / \"status update\"\n19|- \"talking points for [meeting]\" *based on an engineering update*\n20|\n21|If the channel is unclear after the trigger, ask one short question — *\"JIRA, Slack, standup, or email?\"* — and stop.\n22|\n23|## Audience — what \"engineering-org leadership\" means\n24|\n25|Engineering-savvy non-engineers: VPs, directors, PMs, release managers, execs in companies that ship technical products. They read product/framework names and cross-reference JIRA keys and PRs. They do not read code.\n26|\n27|They want: *what's the state, what does it mean for customers, who owns it, what's next.* They do not want: how the bug works at the function level.\n28|\n29|This is **not** for marketing, finance, customer-facing, or true ELI5 audiences — those need a different rewrite. Flag and confirm before producing one.\n30|\n31|## Tone\n32|\n33|**Keep.** Product names, framework names, team-owned component names, JIRA keys, PR numbers, customer/workload identifiers (`Tada`, `DeepSpeed`, `PyTorch`, `Llama-2-70B`, `vLLM`, `JIRA-12345`, `PR #5751`). These are the bridge between engineering and leadership tracking.\n34|\n35|**Strip.** Function names, file paths, struct fields, commit SHAs, code expressions, env var names, line numbers, internal data-structure jargon (`tadaLaunchPrepare`, `tada/prim.h::syncWaitPeer`, `scratchBuf`, `0e0a6bac`). None of this is actionable to the audience.\n36|\n37|**Translate.** Mechanism into one or two sentences of plain-English cause-and-effect. Not *\"the kernel reads from `scratchBuf == NULL`\"* but *\"the GPUs end up reading from an uninitialized buffer and wait forever for a signal that never arrives.\"* Translate without lying — a race stays a race; a regression stays a regression.\n38|\n39|**Don't over-strip.** Engineering-org leadership reads concept-level technical vocabulary fluently — *race condition, synchronization, uninitialized buffer, fast-path, workaround, registration, queue, driver, kernel* (in the GPU sense). The line is between *concept exists and matters here* (keep) and *here's the function/struct/file/SHA* (strip). Replacing \"race\" with \"timing issue\" patronizes the reader.\n40|\n41|**Bias toward** active voice, concrete subjects, short paragraphs. *\"We found the bug. Alex wrote the fix. PR is up for review.\"* beats *\"The root cause has been identified and a fix has been authored and submitted for review.\"*\n42|\n43|**Avoid:**\n44|\n45|- Hedging that isn't really hedging (*\"we believe,\" \"appears to,\" \"may have\"*). State it or don't.\n46|- Re-stating the obvious for thoroughness (*\"This bug is in Tada, which is used for GPU communication, which is important for distributed training, which...\"*).\n47|- Telling leadership how to do their job (*\"you should prioritize,\" \"this needs to land before X\"*). Give them the facts; they decide.\n48|- Engineering-process minutiae: bisect runs, debug iterations, GDB sessions. They care that you found it, not how. (Exception: when the *process* itself is the story — *\"we burned three weeks before realising the bisect was misleading\"* — then a single sentence as a learning, not a play-by-play.)\n49|\n50|## Channel shapes\n51|\n52|Same content, different shell. Pick the shape that matches where it's going.\n53|\n54|### JIRA comment / written status report\n55|\n56|Full structured block. Bolded section labels. Easy to scan from the ticket page.\n57|\n58|Building blocks (use as many as fit):\n59|\n60|- **Status / TL;DR.** One bolded line. Reader can stop here and have the right answer. *\"Fixed pending merge.\"* / *\"Root cause unknown — investigating.\"* / *\"Blocked on vendor.\"* / *\"Customer-visible regression in 7.2; rollback in flight.\"*\n61|- **Impact.** Who's affected, how badly, what they see. Customer / workload / product terms, not test-suite terms. *\"Llama-2-70B fine-tuning hangs on every eval step\"* > *\"the test fails.\"*\n62|- **What broke.** Short paragraph. Plain-English mechanism, one level of why, no code identifiers.\n63|- **Why now / how it slipped through.** Optional. Include when leadership will ask anyway: latent regression, CI gap, prior incomplete fix, change that landed during a freeze.\n64|- **Owner.** Person + team + their PR/branch/JIRA artifact. One link, not five.\n65|- **Next steps.** Concrete, near-term, ordered. *\"Code review → merge → backport to 7.2.\"*\n66|- **Workaround / mitigation.** If customers are hitting it now, what can they do today? One sentence.\n67|- **Risk.** Optional. Real risks only — *\"fix touches the hot path; perf regression possible until benchmarked.\"* Don't manufacture risk to look thorough.\n68|\n69|Order by what matters most for *this* item.\n70|\n71|### Slack — channel post or DM\n72|\n73|Single message, no walls of text. Heavy bolded section labels read as \"I escaped from JIRA\" — don't.\n74|\n75|- One **bolded TL;DR** as the first line.\n76|- 2–4 short bullets underneath: impact, owner+link, next step. Drop blocks that don't apply.\n77|- One link, embedded inline (`JIRA-12345` / `PR #5751`). Not a link wall.\n78|- No greeting, no signoff. The channel is the context.\n79|- If it's a **thread reply** rather than a new post, lose the TL;DR — just lead with the answer.\n80|\n81|Length target: under ~80 words for a top-level post; under ~40 for a thread reply.\n82|\n83|### Async standup note\n84|\n85|The audience scans 10 of these in 30 seconds. Front-load the verb.\n86|\n87|- 1–3 lines, max.\n88|- Pattern: *\"\\<state\\> \\<thing\\>. \\<owner if not me\\>. \\<next\\>.\"*\n89|- Examples:\n90|  - *\"Fixed Tada hang affecting dumbModel runs (JIRA-12345). PR #5751 in review. Backport to v7.2 next.\"*\n91|  - *\"Still chasing the LLM-7B eval-step hang. Reproducer is reliable now; bisecting. No ETA yet.\"*\n92|- No bullets, no bolded labels. The format **is** the sentence.\n93|\n94|### Email — internal exec / cross-team\n95|\n96|Subject line is half the value.\n97|\n98|- **Subject:** the TL;DR rewritten as a noun phrase. *\"Tada hang in dumbModel: fix in review (JIRA-12345).\"*\n99|- **Greeting:** match the recipient register (*Hi Sam,* / *Hi all,*).\n100|- **Body:** the JIRA-comment shape, but as flowing paragraphs separated by blank lines rather than bolded section labels. Two or three paragraphs is plenty.\n101|- **Sign off** with the next decision point that needs the recipient's attention, if any. If none, a plain *\"— [Name]\"* is fine.\n102|\n103|### Meeting talking-points\n104|\n105|You're going to *say* this, not show it.\n106|\n107|- Bullet list, max one short clause per bullet.\n108|- Order is the order you'll speak in.\n109|- Include the numbers/keys you want to reference out loud, in the bullet itself, so you don't fumble.\n110|- Skip prose. *\"dumbModel LLM-7B fine-tuning was hanging.\"* / *\"Root cause: skipped sync in Tada fast-path.\"* / *\"Alex's fix in review, PR #5751.\"* / *\"Backport to v7.2 once it lands.\"*\n111|\n112|## Source material\n113|\n114|The input is one of:\n115|\n116|1. **A JIRA ticket key** (e.g. `JIRA-12345`) → fetch via `GET /rest/api/3/issue/<KEY>?fields=summary,status,priority,assignee,comment` plus any custom fields your instance uses for technical evaluation — usually the cleanest source of current state. The most recent substantive comment is what to reframe; don't dump the full thread.\n117|2. **Pasted technical text** → use directly.\n118|3. **The current conversation** → if you (or the user) just produced engineering content and the user now says *\"now in slack\"* / *\"now for the VP,\"* reuse what's in context.\n119|\n120|If the source is ambiguous, ask one question and stop.\n121|\n122|## Output flow\n123|\n124|1. **Confirm the channel** if it's not stated.\n125|2. **Produce the draft** as a single chat block, formatted as the channel would render it.\n126|3. **Ask where it goes:**\n127|   - Default: print-only — the user copies it.\n128|   - JIRA back-post: only if the user explicitly says so. Show the exact ADF payload, wait for explicit *\"post it\"* / *\"go ahead\"* / *\"yes,\"* then `POST /rest/api/3/issue/<KEY>/comment`.\n129|   - **Never post to Slack, email, or any non-JIRA channel from this skill.** Hand the draft to the user; they post it.\n130|4. **One iteration is normal, three is a smell.** If the user is on the third revision, ask what specific framing/audience assumption you're missing — don't keep tweaking blindly.\n131|\n132|## Worked example — same bug, three channels\n133|\n134|**Source (engineering JIRA comment):**\n135|\n136|> **Mechanism:** the single-stream fast-path in `tadaLaunchPrepare` / `tadaLaunchKernel` / `tadaLaunchFinish` (gated on `scheduler->numStreams == 1 && !plan->persistent`) skipped the cross-stream event between `launchStream` and `handle->shared->deviceStream`. dumbModel hits this gate exactly. Kernel launched before deviceStream's IPC publish / scratch-buffer writes (the ones that populate `scratchBuf`) were visible to launchStream → `scratchBuf == NULL` in the kernel → stray pointer dereference → ring ready-flag read from garbage → thread spins forever.\n137|\n138|### As a JIRA comment\n139|\n140|> **Status: Fixed pending merge.** Bug found, fix validated, PR up for review.\n141|>\n142|> **Impact:** LLM-7B fine-tuning on 8 GPUs would hang every time it tried to evaluate the model — blocking the entire workload. Affects customers using dumbModel (a popular framework for training large models that don't fit on a single GPU), which means most large-model fine-tuning runs on the platform were exposed.\n143|>\n144|> **What broke:** Our GPU communication library (Tada) skipped an internal synchronization step under a specific configuration that dumbModel happens to trigger. The GPUs ended up reading from an uninitialized buffer and got stuck waiting for a signal that would never arrive. The unsafe shortcut had been in the code for months but wasn't reached by any real workload until now.\n145|>\n146|> **A previous fix attempt** added a defensive check that hid the symptom in some paths but left the underlying race in place. This new fix removes the unsafe shortcut entirely and tightens the safety check on the device side.\n147|>\n148|> **Owner:** Alex (Tada team). PR org/platform#5751.\n149|>\n150|> **Next steps:** code review → merge. Customers hitting this today can disable IPC registration as a temporary workaround.\n151|\n152|### As a Slack post\n153|\n154|> **Tada hang affecting dumbModel LLM-7B fine-tuning is fixed pending merge.** (JIRA-12345)\n155|>\n156|> - Skipped synchronization in the comms fast-path → GPUs read uninitialized memory → hang. Latent for months; dumbModel was the first workload to hit it.\n157|> - Owner: Alex, PR #5751 in review.\n158|> - Workaround until merge: disable IPC registration.\n159|\n160|### As a standup note\n161|\n162|> Fixed Tada hang on dumbModel LLM-7B (JIRA-12345). Alex's PR #5751 in review. Workaround posted in the ticket; backport to v7.2 next.\n163|\n164|What changed between channels: same diagnosis, same owner, same next step. JIRA gets every block. Slack drops \"why now\" and \"previous fix attempt\" — too much for the channel. Standup keeps just state + key + owner + next. None of them mention `scratchBuf` or `tadaLaunchPrepare`.\n165|\n166|## Rules\n167|\n168|- **Never invent facts** to make the rewrite cleaner. If the engineering source says \"root cause unknown,\" the rewrite says \"root cause unknown\" — do not promote a speculation to a finding for narrative tidiness.\n169|- **Never strip a JIRA key, PR number, or customer/workload name** during de-jargoning. They're the cross-reference bridge — losing them breaks tracking.\n170|- **Never invent owners.** If the source doesn't name one, ask the user — don't guess from `git blame` or recent commits.\n171|- **Get sign-off before posting to JIRA.** Reuse the jira-check approval flow. Print-only output needs no approval.\n172|- **Never post to Slack, email, or any non-JIRA channel from this skill.** Hand the draft to the user; they post it.\n173|- **Stay out of advocacy.** This skill produces a status update, not a recommendation. If the user wants a recommendation memo, confirm before reframing.\n174|", "total_lines": 173, "file_size": 12948, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  ## Análise do Repositório `thananon/9arm-skills`

  ### O que fiz
  Clonei o repositório para `/home/ice/9arm-skills`, examinei a estrutura principal (`README.md`, `CLAUDE.md` e os 4 arquivos `SKILL.md`).

  ### O que encontrei

  **Problema / Contexto**
  Repositório de *agent skills* para o Claude Code (Anthropic), solucionando a falta de fluxos de trabalho estruturados e disciplinados para tarefas comuns de engenharia. Não é um repo de código executável, mas sim de conhecimento "empacotado" como skills reutilizáveis por um agente de IA.

  **Tema principal**
  Claude Code Agent Skills — coleções de instruções estruturadas para IA executar workflows de engenharia com qualidade consistente.

  **Razão do hype (popularidade)**
  - **2.549 ⭐**: bastante popular no ecossistema Claude Code, provavelmente pelo timing (comunidade ainda formando boas práticas de agentes)
  - Skills são acionáveis via slash commands (ex: `/debug-mantra`, `/post-mortem`)
  - São methodologias de engenharia tradicionais adaptadas para agentes de IA, o que gera identificação rápida com times técnicos
  - Separação clara entre engineering, productivity, personal, deprecated → artigo de skill é um "plugin" bem segmentado

  **Tecnologias utilizadas**
  | Item | Descrição |
  |------|-----------|
  | Markdown + YAML frontmatter | Interface principal das skills |
  | `.claude-plugin/plugin.json` | Orquestração Claude Code |
  | Shell scripts | Installer (`npx skills add` + `scripts/link-skills.sh`) |
  | JSON | Configuração do plugin |

  **Categorias de skills**
  - **engineering/**: `debug-mantra`, `post-mortem`, `scrutinize`
  - **productivity/**: `management-talk`
  - **misc/**: vazio
  - **(outras pastas)**: `personal/`, `in-progress/`, `deprecated/` conforme `CLAUDE.md`

  ### Estrutura resumida
  ```
  9arm-skills/
  ├── .claude-plugin/plugin.json
  ├── skills/
  │   ├── engineering/
  │   │   ├── debug-mantra/
  │   │   ├── post-mortem/
  │   │   └── scrutinize/
  │   ├── productivity/
  │   │   └── management-talk/
  │   ├── misc/
  │   ├── personal/
  │   ├── in-progress/
  │   └── deprecated/
  └── scripts/
  ```  


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  {"output": "Cloning into '/home/ice/petdex'...", "exit_code": 0, "error": null}

  [Subdirectory context discovered: petdex/AGENTS.md]
  # Petdex Agent Notes

  ## Commands
  - Use Bun for repo work: `bun install`, not `npm install`. Published CLI docs mention `npm`/`npx` for end users only.
  - Recommended local app dev is `bun run dev:mock`; it needs no `.env.local`, loads `.env.mock`, auto-signs in as `contributor@petdex.local`, seeds PGlite from `pets/ideas.json`, and runs `next dev --webpack` because PGlite wasm/workers break under Turbopack.
  - Real-service dev is `bun dev` with `.env.local` copied from `.env.example`; this path expects Clerk, Postgres/Neon, R2, Upstash, Resend, OpenAI, and ElevenLabs credentials.
  - Root checks: `bun run check` for Biome, `bun run format` to write Biome fixes, `bun run build` for the Next app, `bun test` for Bun tests, and `bun run i18n:check` after editing translation messages. There is no root `typecheck` script.
  - Focused tests use `bun test path/to/file.test.ts`. Tests that import `src/lib/db/client.ts` directly or indirectly, including `src/lib/pet-search.test.ts` and `src/lib/security.test.ts`, need a usable `DATABASE_URL` or mock env: `bun test --env-file=.env.mock src/lib/security.test.ts`.
  - `packages/petdex-cli` and `packages/discord-bot` are independent packages, not root workspaces; run their own `bun install` and package scripts from inside each directory. Root `tsconfig.json` excludes `packages`.
  - CLI package verification, run from `packages/petdex-cli`, is `bun run build && bun run typecheck`; the build emits ignored `dist/petdex.js` and prepends the node shebang in `postbuild`.
  - Discord bot work stays in `packages/discord-bot`; use `bun run register`, `bun run dev`, or `bun run start` there with env from `packages/discord-bot/.env.example`.

  ## Architecture
  - Main app routes live in `src/app/[locale]`; API routes live in `src/app/api`; shared server/domain code lives in `src/lib`; DB schema is `src/lib/db/schema.ts` with SQL migrations in `drizzle/`.
  - This is Next `16.2.4` with newer conventions: `src/proxy.ts` is the middleware/proxy entrypoint, route/layout `params` are promises, and `src/app/layout.tsx` intentionally returns children while `[locale]/layout.tsx` owns `<html>`/`<body>` for locale `lang`. If touching framework APIs, prefer the installed Next docs under `node_modules/next/dist/docs/` over older assumptions.
  - i18n locales are `en`, `es`, and `zh`; message files are under `src/i18n/messages/`, and `next-intl` is wired through `src/i18n/request.ts` plus the locale layout.
  - Mock DB bootstrap tolerates migration/schema drift via fixups in `src/lib/mock/db.ts`; if schema fields are added and mock pages fail, update those fixups until a real migration lands.
  - `drizzle.config.ts` hard-fails without `DATABASE_URL`; there are no root package scripts for migration generation or application, and many `scripts/apply-*` / `backfill-*` files are real-DB one-offs.
  - Automated review/tag/sound paths use AI SDK model strings through Vercel AI Gateway (`AI_GATEWAY_API_KEY` in `.env.example`); avoid adding raw OpenAI client wiring to server paths.

  ## Invariants
  - Submission identity and credit data must come from verified Clerk session or CLI bearer token, never from request bodies. Keep this split in `src/lib/submissions.ts`, `/api/submit`, and `/api/cli/submit*`.
  - State-changing browser endpoints should use `requireSameOrigin` from `src/lib/same-origin.ts`; CLI/server callers authenticate separately by bearer or service-side checks.
  - User-supplied asset/avatar/external URLs are allowlisted in `src/lib/url-allowlist.ts`. Adding a new host usually also needs CSP updates in `next.config.ts` and regression coverage in `src/lib/security.test.ts`.
  - CSP is explicit in `next.config.ts`; new Clerk/R2/UploadThing/analytics/media hosts must be added to the matching `script-src`, `connect-src`, `img-src`, `frame-src`, or `media-src` directive.
  - Tailwind v4 tokens live in `src/app/globals.css` via `@theme inline` rather than `tailwind.config.*`; components should use semantic tokens such as `bg-surface`, `text-foreground`, and `border-border-base` instead of hardcoded `bg-white dark:*` pairs.
  - `bun run generate-assets` rewrites app/public icons and OG images from `public/brand/petdex-mark.svg`, including `src/app/favicon.ico` because Next 16 prefers the app favicon. terminal 


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c0:
  {"content": "1|{\n2|  \"name\": \"petdex\",\n3|  \"version\": \"0.1.0\",\n4|  \"private\": true,\n5|  \"scripts\": {\n6|    \"dev\": \"next dev\",\n7|    \"dev:docker\": \"bun scripts/dev-docker.ts\",\n8|    \"dev:mock\": \"bun scripts/dev-mock.ts\",\n9|    \"docker:down\": \"docker compose -f docker-compose.dev.yml down || podman compose -f docker-compose.dev.yml down\",\n10|    \"docker:nuke\": \"docker compose -f docker-compose.dev.yml down -v || podman compose -f docker-compose.dev.yml down -v\",\n11|    \"seed:dev\": \"bun scripts/seed-dev.ts\",\n12|    \"build\": \"bun scripts/write-build-version.ts && next build\",\n13|    \"start\": \"next start\",\n14|    \"check\": \"biome check .\",\n15|    \"format\": \"biome check --write .\",\n16|    \"generate-assets\": \"bun scripts/generate-assets.ts\",\n17|    \"auto-tag\": \"bun scripts/auto-tag.ts\",\n18|    \"release:desktop\": \"bun scripts/release-desktop.ts\",\n19|    \"i18n:check\": \"bun scripts/i18n-check.ts\",\n20|    \"test\": \"bun test\",\n21|    \"test:db\": \"bun test ./src/lib/pet-search.integration.ts\"\n22|  },\n23|  \"dependencies\": {\n24|    \"@aws-sdk/client-s3\": \"^3.1041.0\",\n25|    \"@aws-sdk/s3-request-presigner\": \"^3.1041.0\",\n26|    \"@base-ui/react\": \"^1.4.1\",\n27|    \"@clerk/localizations\": \"^4.5.8\",\n28|    \"@clerk/nextjs\": \"^7.3.0\",\n29|    \"@clerk/themes\": \"^2.4.57\",\n30|    \"@dnd-kit/core\": \"^6.3.1\",\n31|    \"@dnd-kit/sortable\": \"^10.0.0\",\n32|    \"@dnd-kit/utilities\": \"^3.2.2\",\n33|    \"@electric-sql/pglite\": \"^0.4.5\",\n34|    \"@neondatabase/serverless\": \"^1.1.0\",\n35|    \"@phosphor-icons/react\": \"^2.1.10\",\n36|    \"@uploadthing/react\": \"^7.3.3\",\n37|    \"@upstash/ratelimit\": \"^2.0.8\",\n38|    \"@upstash/redis\": \"^1.37.0\",\n39|    \"@vercel/analytics\": \"^2.0.1\",\n40|    \"@vercel/speed-insights\": \"^2.0.0\",\n41|    \"ai\": \"^6.0.175\",\n42|    \"ali-oss\": \"^6.23.0\",\n43|    \"class-variance-authority\": \"^0.7.1\",\n44|    \"clsx\": \"^2.1.1\",\n45|    \"drizzle-orm\": \"^0.45.2\",\n46|    \"gifenc\": \"^1.0.3\",\n47|    \"jszip\": \"^3.10.1\",\n48|    \"lucide-react\": \"^1.14.0\",\n49|    \"next\": \"16.2.6\",\n50|    \"next-intl\": \"^4.11.0\",\n51|    \"next-themes\": \"^0.4.6\",\n52|    \"node-vibrant\": \"^4.0.4\",\n53|    \"openai\": \"^6.35.0\",\n54|    \"postcss\": \"^8.5.13\",\n55|    \"react\": \"19.2.6\",\n56|    \"react-dom\": \"19.2.6\",\n57|    \"recharts\": \"3.8.1\",\n58|    \"resend\": \"^6.12.2\",\n59|    \"shadcn\": \"^4.7.0\",\n60|    \"sharp\": \"^0.34.5\",\n61|    \"stripe\": \"^22.1.0\",\n62|    \"tailwind-merge\": \"^3.5.0\",\n63|    \"tw-animate-css\": \"^1.4.0\",\n64|    \"uploadthing\": \"^7.7.4\"\n65|  },\n66|  \"devDependencies\": {\n67|    \"@biomejs/biome\": \"^2.4.14\",\n68|    \"@tailwindcss/postcss\": \"^4.2.4\",\n69|    \"@types/ali-oss\": \"^6.23.3\",\n70|    \"@types/node\": \"^25.6.0\",\n71|    \"@types/react\": \"^19.2.14\",\n72|    \"@types/react-dom\": \"^19.2.3\",\n73|    \"bun-types\": \"^1.3.13\",\n74|    \"drizzle-kit\": \"^0.31.10\",\n75|    \"postgres\": \"^3.4.9\",\n76|    \"tailwindcss\": \"^4.2.4\",\n77|    \"typescript\": \"^6.0.3\"\n78|  },\n79|  \"ignoreScripts\": [\n80|    \"sharp\",\n81|    \"unrs-resolver\"\n82|  ],\n83|  \"trustedDependencies\": [\n84|    \"sharp\",\n85|    \"unrs-resolver\"\n86|  ]\n87|}\n88|", "total_lines": 87, "file_size": 2721, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|<div align=\"center\">\n2|\n3|<img src=\"public/brand/petdex-desktop-icon.png\" alt=\"Petdex\" width=\"120\" />\n4|\n5|<h1>Petdex</h1>\n6|\n7|<p>\n8|  The public gallery of animated companions for Codex.\n9|  <br />\n10|  Browse, install, and submit pets with one command.\n11|</p>\n12|\n13|<p>\n14|  <a href=\"https://petdex.crafter.run\"><strong>petdex.crafter.run</strong></a>\n15|  &nbsp;·&nbsp;\n16|  <a href=\"https://petdex.crafter.run/built-with\">Built with Petdex</a>\n17|  &nbsp;·&nbsp;\n18|  <a href=\"https://discord.gg/byhubdyBTe\">Discord</a>\n19|  &nbsp;·&nbsp;\n20|  <a href=\"https://www.npmjs.com/package/petdex\">npm</a>\n21|</p>\n22|\n23|<p>\n24|  <a href=\"https://www.npmjs.com/package/petdex\"><img src=\"https://img.shields.io/npm/v/petdex?style=flat-square&label=cli&color=000000\" alt=\"npm version\" /></a>\n25|  <a href=\"https://github.com/crafter-station/petdex/stargazers\"><img src=\"https://img.shields.io/github/stars/crafter-station/petdex?style=flat-square&color=000000\" alt=\"GitHub stars\" /></a>\n26|  <a href=\"https://github.com/crafter-station/petdex/blob/main/LICENSE\"><img src=\"https://img.shields.io/github/license/crafter-station/petdex?style=flat-square&color=000000\" alt=\"MIT license\" /></a>\n27|  <a href=\"https://github.com/crafter-station/petdex/issues\"><img src=\"https://img.shields.io/github/issues/crafter-station/petdex?style=flat-square&color=000000\" alt=\"GitHub issues\" /></a>\n28|</p>\n29|\n30|</div>\n31|\n32|---\n33|\n34|## What is Petdex\n35|\n36|Petdex is three things working together:\n37|\n38|1. **A web gallery** at [petdex.crafter.run](https://petdex.crafter.run) where the community submits, reviews, and showcases animated pets in the Codex sprite format.\n39|2. **A CLI** that installs any pet on your machine with one command and ships them straight into Codex.\n40|3. **A desktop app** that floats a pet on your screen and reacts to your coding agent's activity in real time.\n41|\n42|Every pet is a folder. Every folder is a Pokédex entry. Every entry is one `npx petdex install` away.\n43|\n44|## Quick start\n45|\n46|```sh\n47|# Pick a pet. Install it. Your Codex desktop app picks it up automatically.\n48|npx petdex install boba\n49|\n50|# Or run the full Petdex desktop app with bubble UI and agent hooks.\n51|npx petdex init\n52|```\n53|\n54|Open Codex, go to **Settings → Appearance → Pets**, and pick the one you just installed.\n55|\n56|## For users\n57|\n58|| You want to... | Do this |\n59|| --- | --- |\n60|| Browse pets | Visit [petdex.crafter.run](https://petdex.crafter.run) |\n61|| Install a pet | `npx petdex install <slug>` |\n62|| Run the desktop floater | `npx petdex init` (downloads the `.dmg` and wires Codex/Claude Code hooks) |\n63|| Make a pet | Use the `hatch-pet` skill inside Codex, or build one with the [Petdex creator tools](https://petdex.crafter.run/create) |\n64|| Submit a pet | `npx petdex submit ./my-pet/` or drop it through the web submitter |\n65|| Join the community | [Discord](https://discord.gg/byhubdyBTe) |\n66|\n67|Full CLI reference: [`packages/petdex-cli/README.md`](./packages/petdex-cli/README.md).\n68|\n69|## For builders\n70|\n71|If you want to build on top of Petdex (a desktop client, a wearable, an SDK, a Discord bot, anything), you have two stable surfaces:\n72|\n73|- **The HTTP API.** `petdex.crafter.run/api/manifest` returns every approved pet with its slug, spritesheet URL, animation states, and metadata.\n74|- **The pet package format.** Every pet is a `pet.json` plus a `spritesheet.{webp,png}` rendered as an 8×9 grid of 192×208 frames.\n75|\n76|13 open-source projects already build on these. See [petdex.crafter.run/built-with](https://petdex.crafter.run/built-with) for the catalog, then [submit yours via the issue template](https://github.com/crafter-station/petdex/issues/new?template=built-with.yml).\n77|\n78|## Architecture\n79|\n80|```text\n81|crafter-station/petdex\n82|├── src/\n83|│   ├── app/[locale]/          Public site: gallery, /pets/<slug>, /collections, /built-with, /community, /create, /download, /submit, /u/<handle>, ...\n84|│   ├── app/api/cli/           CLI endpoints: OAuth config, submit (zip → presigned R2), dedup check, register\n85|│   ├── app/api/manifest/      Public manifest: every approved pet with its spritesheet URL\n86|│   ├── app/api/admin/         Admin review surface for submissions, edits, collection requests\n87|│   └── lib/db/schema.ts       Drizzle schema (Postgres)\n88|├── packages/\n89|│   ├── petdex-cli/            npm `petdex` (auth, list, install, submit, hooks, init)\n90|│   ├── petdex-desktop/        Zig + WebKit floating mascot for macOS\n91|│   └── discord-bot/           Discord.js bot for the Petdex server\n92|├── public/built-with/         Screenshots for the community page\n93|├── public/brand/              Logos, OS icons, Discord icon\n94|└── drizzle/                   SQL migrations (Postgres schema history)\n95|```\n96|\n97|**Web stack**: Next.js 16, React 19, Tailwind, Drizzle, Postgres, Redis, Clerk, R2.<br />\n98|**CLI**: Bun + TypeScript, ships as a single npm binary. Auth via Clerk OAuth + PKCE.<br />\n99|**Desktop**: Zig on a fork of [`vercel-labs/zero-native`](https://github.com/vercel-labs/zero-native), HTTP sidecar in Node for agent hooks.\n100|\n101|## Develop locally\n102|\n103|Three paths, pick the one that matches what you want to change.\n104|\n105|| Goal | Command | Setup |\n106|| --- | --- | --- |\n107|| UI, copy, i18n, CSS | `bun run dev:mock` | Zero credentials. In-process Postgres + stubbed Clerk. |\n108|| DB queries, auth, submit, likes | `bun run dev:docker` | Docker or Podman, ~30s warm-up. |\n109|| Run against real services | `bun run dev` | `.env.local` filled (maintainers only). |\n110|\n111|```sh\n112|git clone https://github.com/crafter-station/petdex.git\n113|cd petdex\n114|bun install\n115|bun run dev:mock\n116|```\n117|\n118|Open [localhost:3000](http://localhost:3000). You're auto-signed-in as `contributor@petdex.local`. Full guide in [`CONTRIBUTING.md`](./CONTRIBUTING.md).\n119|\n120|## Pet package format\n121|\n122|Every pet is two files:\n123|\n124|```text\n125|my-pet/\n126|├── pet.json                Metadata: name, slug, tags, vibes, kind, frame size, animation states\n127|└── spritesheet.webp        8 rows × 9 cols = 72 frames of 192×208 px each (or .png)\n128|```\n129|\n130|Animation states are the rows: `idle`, `wave`, `run`, `failed`, `review`, `jump`, `extra1`, `extra2`. Codex maps these to its agent activity hooks. Loop timing defaults to 1100ms at 6 frames per state.\n131|\n132|## Contribute\n133|\n134|- **Submit a pet:** [petdex.crafter.run/submit](https://petdex.crafter.run/submit) or `npx petdex submit <path>`.\n135|- **List your project:** open a [Built with Petdex issue](https://github.com/crafter-station/petdex/issues/new?template=built-with.yml).\n136|- **Fix a bug or add a feature:** read [`CONTRIBUTING.md`](./CONTRIBUTING.md), then open a PR.\n137|- **Hang out:** [Discord](https://discord.gg/byhubdyBTe) has channels for shipping (`#wip`, `#ship-or-sink`), feedback (`#cli-feedback`), and showcases (`#showcase`).\n138|\n139|## Pet IP and takedowns\n140|\n141|Pets are user-submitted fan art. Petdex does not claim rights to any underlying IP. If you hold rights to a character and want a pet removed, file a [takedown request](https://github.com/crafter-station/petdex/issues/new?template=takedown.yml) and we review within 48 hours.\n142|\n143|## License\n144|\n145|The source code is [MIT](./LICENSE). Pet assets are owned by their submitters under whatever license they choose to declare.\n146|\n147|---\n148|\n149|<div align=\"center\">\n150|\n151|Made by <a href=\"https://crafter.run\">Crafter Station</a>.\n152|Lead: <a href=\"https://x.com/RaillyHugo\">@RaillyHugo</a>.\n153|\n154|</div>\n155|", "total_lines": 154, "file_size": 7236, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  {"content": "1|# Petdex Agent Notes\n2|\n3|## Commands\n4|- Use Bun for repo work: `bun install`, not `npm install`. Published CLI docs mention `npm`/`npx` for end users only.\n5|- Recommended local app dev is `bun run dev:mock`; it needs no `.env.local`, loads `.env.mock`, auto-signs in as `contributor@petdex.local`, seeds PGlite from `pets/ideas.json`, and runs `next dev --webpack` because PGlite wasm/workers break under Turbopack.\n6|- Real-service dev is `bun dev` with `.env.local` copied from `.env.example`; this path expects Clerk, Postgres/Neon, R2, Upstash, Resend, OpenAI, and ElevenLabs credentials.\n7|- Root checks: `bun run check` for Biome, `bun run format` to write Biome fixes, `bun run build` for the Next app, `bun test` for Bun tests, and `bun run i18n:check` after editing translation messages. There is no root `typecheck` script.\n8|- Focused tests use `bun test path/to/file.test.ts`. Tests that import `src/lib/db/client.ts` directly or indirectly, including `src/lib/pet-search.test.ts` and `src/lib/security.test.ts`, need a usable `DATABASE_URL` or mock env: `bun test --env-file=.env.mock src/lib/security.test.ts`.\n9|- `packages/petdex-cli` and `packages/discord-bot` are independent packages, not root workspaces; run their own `bun install` and package scripts from inside each directory. Root `tsconfig.json` excludes `packages`.\n10|- CLI package verification, run from `packages/petdex-cli`, is `bun run build && bun run typecheck`; the build emits ignored `dist/petdex.js` and prepends the node shebang in `postbuild`.\n11|- Discord bot work stays in `packages/discord-bot`; use `bun run register`, `bun run dev`, or `bun run start` there with env from `packages/discord-bot/.env.example`.\n12|\n13|## Architecture\n14|- Main app routes live in `src/app/[locale]`; API routes live in `src/app/api`; shared server/domain code lives in `src/lib`; DB schema is `src/lib/db/schema.ts` with SQL migrations in `drizzle/`.\n15|- This is Next `16.2.4` with newer conventions: `src/proxy.ts` is the middleware/proxy entrypoint, route/layout `params` are promises, and `src/app/layout.tsx` intentionally returns children while `[locale]/layout.tsx` owns `<html>`/`<body>` for locale `lang`. If touching framework APIs, prefer the installed Next docs under `node_modules/next/dist/docs/` over older assumptions.\n16|- i18n locales are `en`, `es`, and `zh`; message files are under `src/i18n/messages/`, and `next-intl` is wired through `src/i18n/request.ts` plus the locale layout.\n17|- Mock DB bootstrap tolerates migration/schema drift via fixups in `src/lib/mock/db.ts`; if schema fields are added and mock pages fail, update those fixups until a real migration lands.\n18|- `drizzle.config.ts` hard-fails without `DATABASE_URL`; there are no root package scripts for migration generation or application, and many `scripts/apply-*` / `backfill-*` files are real-DB one-offs.\n19|- Automated review/tag/sound paths use AI SDK model strings through Vercel AI Gateway (`AI_GATEWAY_API_KEY` in `.env.example`); avoid adding raw OpenAI client wiring to server paths.\n20|\n21|## Invariants\n22|- Submission identity and credit data must come from verified Clerk session or CLI bearer token, never from request bodies. Keep this split in `src/lib/submissions.ts`, `/api/submit`, and `/api/cli/submit*`.\n23|- State-changing browser endpoints should use `requireSameOrigin` from `src/lib/same-origin.ts`; CLI/server callers authenticate separately by bearer or service-side checks.\n24|- User-supplied asset/avatar/external URLs are allowlisted in `src/lib/url-allowlist.ts`. Adding a new host usually also needs CSP updates in `next.config.ts` and regression coverage in `src/lib/security.test.ts`.\n25|- CSP is explicit in `next.config.ts`; new Clerk/R2/UploadThing/analytics/media hosts must be added to the matching `script-src`, `connect-src`, `img-src`, `frame-src`, or `media-src` directive.\n26|- Tailwind v4 tokens live in `src/app/globals.css` via `@theme inline` rather than `tailwind.config.*`; components should use semantic tokens such as `bg-surface`, `text-foreground`, and `border-border-base` instead of hardcoded `bg-white dark:*` pairs.\n27|- `bun run generate-assets` rewrites app/public icons and OG images from `public/brand/petdex-mark.svg`, including `src/app/favicon.ico` because Next 16 prefers the app favicon.\n28|", "total_lines": 27, "file_size": 4256, "truncated": false, "is_binary": false, "is_image": false} read_file 


c0:
  Luiz, já analisei. Aqui o resumo do repositório `crafter-station/petdex`::

  ### 🎯 Problema
  Agentes de código (Codex, Claude Code, OpenCode, Gemini CLI) não tinham uma forma fácil de personalizar a interface com mascotes animados. Não havia nem galeria pública nem instalação com um comando — cada pet era um pacote solto, sem curadoria, distribuição ou ecossistema.

  ---

  ### 🧩 Tema principal
  **Galerias e CLI de mascotes animados para agentes de IA**  
  É um ecossistema em três partes:
  1. A galeria web (petdex.crafter.run) para browse, submit e review de pets  
  2. O `petdex-cli` (`npx petdex install`) para instalar pets com um comando  
  3. O desktop floater (app que exibe o pet na tela e reage à atividade do agente em tempo real)

  Cada pet é uma pasta simples com `pet.json` + `spritesheet`, no estilo Pokédex.

  ---

  ### ✅ Razão do hype (~2.6k stars)
  - **Ecossistema completo**: gallery + CLI + desktop app em um único repo.  
  - **Instalação trivial**: `npx petdex install <slug>` é charmosa e acessível.  
  - **Comunidade-first**: submissão pública, curadoria, Discord ativo, Badge de built-with, modelo de contribuição claro.  
  - **Tokens**: pet como objeto de coleção, “hatch a pet” dentro do Codex, vibe gamificada/colecionável.  
  - **Projetos derivados**: já tem 13+ projetos open-source construídos em cima da API/manifesto público.  
  - **Desktop "fofo"**: floater no macOS com Zig + WebKit que reage a eventos do agente (hooks real-time) — recurso diferenciador.

  ---

  ### 🛠️ Tecnologias
  **Web / App**  
  - Next.js 16, React 19, Tailwind v4, next-intl (i18n: en/es/zh)  
  - Drizzle ORM + Postgres (via PGlite em dev/mock ou Neon em produção)  
  - Clerk (auth OAuth/PKCE), Upstash Redis, UploadThing/R2/S3  
  - Vercel AI Gateway, OpenAI, ElevenLabs, Stripe, Resend  

  **CLI**  
  - Bun + TypeScript, publicado como npm package `petdex` (`petdex-cli`)  
  - Auth via Clerk OAuth + PKCE  

  **Desktop**  
  - Zig + WebKit (fork do `vercel-labs/zero-native`)  
  - Node sidecar para agent hooks  

  **Dev / Infra**  
  - Biome (lint/format), Bun test, `dev:mock` com PGlite (zero credenciais), Docker/Podman para dev real  

  ---

  Se quiser, eu posso descer em algum ponto específico: arquitetura, análise de código, ou montar um pet fictício para ver como o schema funciona.  


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c0:
  {"content": "1|# 3D Model Studio\n2|\n3|[English](README.md) | [中文](README.zh-CN.md)\n4|\n5|AI-powered interactive 3D model generation, inspection, and presentation studio.\n6|\n7|3D Model Studio is a React + Three.js prototype for turning uploaded reference images or GLB files into a polished interactive 3D workspace. It supports live WebGL orbit controls, a left model library / center stage / right tools workbench, screenshots, GLB export, collapsed upload history, demo presentation mode, a generation queue, and optional image-to-3D providers for generating real 3D models from uploaded reference images.\n8|\n9|## Demo\n10|\n11|[![3D Model Studio demo](docs/demo/3DCellForge-demo-cover.jpg)](docs/demo/3DCellForge-demo-2026-05-10.mp4)\n12|\n13|Open the demo video: [Demo MP4](docs/demo/3DCellForge-demo-2026-05-10.mp4)\n14|\n15|## Features\n16|\n17|- Interactive model viewer built with React Three Fiber.\n18|- Three-column workbench: Model Library on the left, WebGL stage in the center, asset/generation tools on the right.\n19|- Drag to rotate, scroll to zoom, isolate structure parts, inspect model details, and export the current scene.\n20|- Object-aware inspector with inferred category, source, provider state, material focus, demo value, and tags for vehicles, aircraft, vessels, products, artifacts, and organic specimens.\n21|- Model quality score for generated GLBs, including file size, triangle count, texture count, and demo readiness.\n22|- Demo Mode for screenshots and screen recordings: hides side panels, uses object-aware cinematic camera paths, and shows a clean presentation overlay.\n23|- Productized Model Library drawer with source thumbnails, provider/status, task id, GLB URL actions, comparison, and delete controls.\n24|- Saved Assets stays collapsed by default, while the active generated/imported asset stays pinned and clickable.\n25|- Generated/imported models are restored after refresh through IndexedDB, with localStorage as a compact fallback.\n26|- Generic part detail drawer, asset references, comparison panel, notes, gallery actions, logs, saved projects, and a compact generation queue.\n27|- Hyper3D, Tripo, Fal.ai, Hunyuan3D, JS Depth, and Local GLB generation/import modes.\n28|- Cached demo GLB models for offline-friendly screenshots and demos.\n29|- Auxiliary Khronos glTF reference models for GLB loader and PBR material checks.\n30|- API key stays server-side in `.env.local`; it is never exposed to the frontend bundle.\n31|\n32|## Tech Stack\n33|\n34|- React\n35|- Vite\n36|- Three.js\n37|- React Three Fiber\n38|- Drei\n39|- Framer Motion\n40|- Tripo API optional backend\n41|- Fal.ai optional backend\n42|- Hunyuan3D local API optional backend\n43|\n44|## Quick Start\n45|\n46|```bash\n47|npm install\n48|npm run dev\n49|```\n50|\n51|Open the Vite URL shown in the terminal.\n52|\n53|## Workbench Workflow\n54|\n55|The default screen is intentionally quiet:\n56|\n57|- Pick the active generated/imported asset from the left `Model Library` rail.\n58|- Earlier generated/imported models are tucked under `Saved Assets` until expanded.\n59|- Use the right `Asset Source` rail to choose the generation provider or import a local `.glb` / `.gltf`.\n60|- Watch upload/generation/import state in the left `Generation Queue` panel.\n61|- Click `Info` or `Inspect` only when you need the part detail drawer.\n62|- Open top-nav `Library` for the full asset catalog with previews, provider state, task ids, GLB URL copy, provider comparison, and deletion.\n63|- Click `Demo` in the top navigation to enter a clean presentation mode for screenshots and recordings.\n64|- Check the quality card on the stage before recording; low scores usually mean the source image or provider result is not demo-ready.\n65|- Demo animation adapts to the model name and metadata: cars use a road push-in, aircraft use a flight pass, ships/carriers use a naval cruise, and organic/specimen assets use a studio orbit.\n66|\n67|Useful validation commands:\n68|\n69|```bash\n70|npm run lint\n71|npm run build\n72|npm run test\n73|npm run test:visual\n74|```\n75|\n76|`npm run test:visual` runs Playwright layout and screenshot regression checks for the workbench, the Model Library drawer, and Demo Mode. Use `npm run test:visual:update` only when an intentional UI change needs new screenshot baselines.\n77|\n78|## Optional Image-to-3D Backend\n79|\n80|To enable image-to-3D generation, create `.env.local`:\n81|\n82|```bash\n83|cp .env.example .env.local\n84|```\n85|\n86|Then set:\n87|\n88|```bash\n89|TRIPO_API_KEY=your_tripo_key\n90|FAL_API_KEY=your_fal_key\n91|RODIN_API_KEY=your_rodin_api_key\n92|OPENAI_API_KEY=your_openai_key\n93|API_HOST=127.0.0.1\n94|```\n95|\n96|`OPENAI_API_KEY` enables optional image understanding through `/api/3d/analyze`. When configured, uploads are classified by vision into asset type, material focus, inspection notes, scene profile, tags, and a better image-to-3D prompt. Without it, the app keeps using local filename/metadata heuristics.\n97|\n98|For Hunyuan3D local backup mode, start your local Hunyuan3D API server and set:\n99|\n100|```bash\n101|HUNYUAN_API_BASE=http://127.0.0.1:8081\n102|HUNYUAN_CREATE_PATH=/send\n103|HUNYUAN_STATUS_PATH=/status\n104|```\n105|\n106|The 3D generation backend supports these provider paths:\n107|\n108|```text\n109|Hyper3D  Hyper3D Rodin cloud generation only (default)\n110|Tripo    Tripo cloud generation only\n111|Fal      Fal.ai queue generation; model is selected in Settings\n112|Auto     Hyper3D first, then Tripo, Fal, Hunyuan, and JS Depth backup\n113|Hunyuan  Local Hunyuan3D generation only\n114|```\n115|\n116|The upload panel exposes the full generation mode choice before picking a file:\n117|\n118|```text\n119|Hyper3D     Hyper3D Rodin GLB generation\n120|Tripo       Tripo cloud GLB generation\n121|Fal         Fal.ai queue GLB generation\n122|Hunyuan     Local Hunyuan3D GLB generation\n123|JS Depth    Browser-side image relief with layered PNG fallback\n124|Auto        Hyper3D, Tripo, Fal, Hunyuan, then JS Depth fallback\n125|Local GLB   Import an existing .glb or self-contained .gltf\n126|```\n127|\n128|Tripo uploads use the current STS object-storage flow (`/upload/sts/token`) before creating an `image_to_model` task.\n129|Fal uploads use the official `@fal-ai/client` storage and queue APIs. Supported Fal models are Hunyuan3D v2, TRELLIS, TripoSR, Tripo3D v2.5, and Hyper3D Rodin. Pick the active Fal model in `Settings`.\n130|Rodin uploads use Hyper3D's multipart `/rodin` task API, then poll `/status` and cache the GLB returned by `/download`.\n131|Generated GLBs are cached by the Node backend under `.generated-models/`, so later views use the local copy instead of temporary provider URLs.\n132|The frontend model library is saved in IndexedDB, so successful generated/imported model records survive page refreshes.\n133|\n134|You can also import a local `.glb` or self-contained `.gltf` from the `New Upload` button. Imported models become custom workspace models and are served from the same local cache.\n135|\n136|Expected Hunyuan3D local API shape:\n137|\n138|```text\n139|POST /send\n140|GET  /status/:uid\n141|```\n142|\n143|The status response can return either a remote model URL or a base64 GLB field such as `model_base64` / `glb_base64`. Base64 GLBs are cached under `.generated-models/` and served by the Node backend.\n144|\n145|Start the backend:\n146|\n147|```bash\n148|npm run dev:api\n149|```\n150|\n151|Then start the frontend:\n152|\n153|```bash\n154|npm run dev\n155|```\n156|\n157|The frontend talks to the local Node backend at `http://127.0.0.1:8787` by default.\n158|\n159|## Demo Models\n160|\n161|The repository includes cached generated GLB files under:\n162|\n163|```text\n164|public/generated-models/\n165|```\n166|\n167|These make the demo usable without spending API credits on every run.\n168|\n169|## Reference Models\n170|\n171|The Library panel includes remote Khronos glTF Sample Models as auxiliary references for material and loader checks:\n172|\n173|- Transmission Test, CC0, Adobe via Khronos.\n174|- Transmission Roughness Test, CC-BY 4.0, Ed Mackey / Analytical Graphics via Khronos.\n175|- Mosquito In Amber, CC-BY 4.0, Loic Norgeot / Geoffrey Marchal / Sketchfab via Khronos.\n176|\n177|These are loaded from the archived Khronos sample repository and are not bundled into this repo.\n178|\n179|## Security\n180|\n181|Do not put real API keys in frontend code. Keep secrets in `.env.local`, which is ignored by git.\n182|\n183|## License\n184|\n185|MIT\n186|", "total_lines": 185, "file_size": 7725, "truncated": false, "is_binary": false, "is_image": false} read_file 


