[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6RB1D91HMNNZ4R01YAWQBE7":3,"skill-same:01M6RB1D91HMNNZ4R01YAWQBE7":85},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":28,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":65,"commitSha":66,"skillDirectory":67,"forksCount":14,"repositoryHtmlUrl":68,"repositoryHomepageUrl":6,"ownerHtmlUrl":69,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":70,"install":71,"resources":84},"01M6RB1D91HMNNZ4R01YAWQBE7","acquire-codebase-knowledge",null,"Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like \"map this codebase\", \"document this architecture\", \"onboard me to this repo\", or \"create codebase docs\". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.","aritra-ghosh-sage\u002Fpython-hol","aritra-ghosh-sage","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F84577750?v=4","01M6RAWNE9ED7PP2MWX01P1FZ2","Aritra Ghosh","01M6RAWNTCTTAVFNTHK5596PXN",0,"MIT","https:\u002F\u002Fgithub.com\u002Faritra-ghosh-sage\u002Fpython-hol\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fcodebase-knowledge\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000037","律师与法务","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"codebase","acquire codebase knowledge","knowledge","acquire","when","testing","markers","project","focus","map","me","feature implementation","only","pipeline","ci","code","create","architecture","orchestration","performance","2026-11-10 10:12:30",false,"---\nname: acquire-codebase-knowledge\ndescription: 'Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like \"map this codebase\", \"document this architecture\", \"onboard me to this repo\", or \"create codebase docs\". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.'\nlicense: MIT\ncompatibility: 'Cross-platform. Requires Python 3.8+ and git. Run scripts\u002Fscan.py from the target project root.'\nmetadata:\n  version: \"1.3\"\n  enhancements:\n    - Multi-language manifest detection (25+ languages supported)\n    - CI\u002FCD pipeline detection (10+ platforms)\n    - Container & orchestration detection\n    - Code metrics by language\n    - Security & compliance config detection\n    - Performance testing markers\nargument-hint: 'Optional: specific area to focus on, e.g. \"architecture only\", \"testing and concerns\"'\n---\n\n# Acquire Codebase Knowledge\n\nProduces seven populated documents in `docs\u002Fcodebase\u002F` covering everything needed to work effectively on the project. Only document what is verifiable from files or terminal output — never infer or assume.\n\n## Output Contract (Required)\n\nBefore finishing, all of the following must be true:\n\n1. Exactly these files exist in `docs\u002Fcodebase\u002F`: `STACK.md`, `STRUCTURE.md`, `ARCHITECTURE.md`, `CONVENTIONS.md`, `INTEGRATIONS.md`, `TESTING.md`, `CONCERNS.md`.\n2. Every claim is traceable to source files, config, or terminal output.\n3. Unknowns are marked as `[TODO]`; intent-dependent decisions are marked `[ASK USER]`.\n4. Every document includes a short \"evidence\" list with concrete file paths.\n5. Final response includes numbered `[ASK USER]` questions and intent-vs-reality divergences.\n\n## Workflow\n\nCopy and track this checklist:\n\n```\n- [ ] Phase 1: Run scan, read intent documents\n- [ ] Phase 2: Investigate each documentation area\n- [ ] Phase 3: Populate all seven docs in docs\u002Fcodebase\u002F\n- [ ] Phase 4: Validate docs, present findings, resolve all [ASK USER] items\n```\n\n## Focus Area Mode\n\nIf the user supplies a focus area (for example: \"architecture only\" or \"testing and concerns\"):\n\n1. Always run Phase 1 in full.\n2. Fully complete focus-area documents first.\n3. For non-focus documents not yet analyzed, keep required sections present and mark unknowns as `[TODO]`.\n4. Still run the Phase 4 validation loop on all seven documents before final output.\n\n### Phase 1: Scan and Read Intent\n\n1. Run the scan script from the target project root:\n   ```bash\n   python3 \"$SKILL_ROOT\u002Fscripts\u002Fscan.py\" --output docs\u002Fcodebase\u002F.codebase-scan.txt\n   ```\n   Where `$SKILL_ROOT` is the absolute path to the skill folder. Works on Windows, macOS, and Linux.\n\n   **Quick start:** If you have the path inline:\n   ```bash\n   python3 \u002Fabsolute\u002Fpath\u002Fto\u002Fskills\u002Facquire-codebase-knowledge\u002Fscripts\u002Fscan.py --output docs\u002Fcodebase\u002F.codebase-scan.txt\n   ```\n\n2. Search for `PRD`, `TRD`, `README`, `ROADMAP`, `SPEC`, `DESIGN` files and read them.\n3. Summarise the stated project intent before reading any source code.\n\n### Phase 2: Investigate\n\nUse the scan output to answer questions for each of the seven templates. Load [`references\u002Finquiry-checkpoints.md`](references\u002Finquiry-checkpoints.md) for the full per-template question list.\n\nIf the stack is ambiguous (multiple manifest files, unfamiliar file types, no `package.json`), load [`references\u002Fstack-detection.md`](references\u002Fstack-detection.md).\n\n### Phase 3: Populate Templates\n\nCopy each template from `assets\u002Ftemplates\u002F` into `docs\u002Fcodebase\u002F`. Fill in this order:\n\n1. [STACK.md](assets\u002Ftemplates\u002FSTACK.md) — language, runtime, frameworks, all dependencies\n2. [STRUCTURE.md](assets\u002Ftemplates\u002FSTRUCTURE.md) — directory layout, entry points, key files\n3. [ARCHITECTURE.md](assets\u002Ftemplates\u002FARCHITECTURE.md) — layers, patterns, data flow\n4. [CONVENTIONS.md](assets\u002Ftemplates\u002FCONVENTIONS.md) — naming, formatting, error handling, imports\n5. [INTEGRATIONS.md](assets\u002Ftemplates\u002FINTEGRATIONS.md) — external APIs, databases, auth, monitoring\n6. [TESTING.md](assets\u002Ftemplates\u002FTESTING.md) — frameworks, file organization, mocking strategy\n7. [CONCERNS.md](assets\u002Ftemplates\u002FCONCERNS.md) — tech debt, bugs, security risks, perf bottlenecks\n\nUse `[TODO]` for anything that cannot be determined from code. Use `[ASK USER]` where the right answer requires team intent.\n\n### Phase 4: Validate, Repair, Verify\n\nRun this mandatory validation loop before finalizing:\n\n1. Validate each doc against `references\u002Finquiry-checkpoints.md`.\n2. For each non-trivial claim, confirm at least one evidence reference exists.\n3. If any required section is missing or unsupported:\n  - Fix the document.\n  - Re-run validation.\n4. Repeat until all seven docs pass.\n\nThen present a summary of all seven documents, list every `[ASK USER]` item as a numbered question, and highlight any Intent vs. Reality divergences from Phase 1.\n\nValidation pass criteria:\n\n- No unsupported claims.\n- No empty required sections.\n- Unknowns use `[TODO]` rather than assumptions.\n- Team-intent gaps are explicitly marked `[ASK USER]`.\n\n---\n\n## Gotchas\n\n**Monorepos:** Root `package.json` may have no source — check for `workspaces`, `packages\u002F`, or `apps\u002F` directories. Each workspace may have independent dependencies and conventions. Map each sub-package separately.\n\n**Outdated README:** README often describes intended architecture, not the current one. Cross-reference with actual file structure before treating any README claim as fact.\n\n**TypeScript path aliases:** `tsconfig.json` `paths` config means imports like `@\u002Ffoo` don't map directly to the filesystem. Map aliases to real paths before documenting structure.\n\n**Generated\u002Fcompiled output:** Never document patterns from `dist\u002F`, `build\u002F`, `generated\u002F`, `.next\u002F`, `out\u002F`, or `__pycache__\u002F`. These are artefacts — document source conventions only.\n\n**`.env.example` reveals required config:** Secrets are never committed. Read `.env.example`, `.env.template`, or `.env.sample` to discover required environment variables.\n\n**`devDependencies` ≠ production stack:** Only `dependencies` (or equivalent, e.g. `[tool.poetry.dependencies]`) runs in production. Document linters, formatters, and test frameworks separately as dev tooling.\n\n**Test TODOs ≠ production debt:** TODOs inside `test\u002F`, `tests\u002F`, `__tests__\u002F`, or `spec\u002F` are coverage gaps, not production technical debt. Separate them in `CONCERNS.md`.\n\n**High-churn files = fragile areas:** Files appearing most in recent git history have the highest modification rate and likely hidden complexity. Always note them in `CONCERNS.md`.\n\n---\n\n## Anti-Patterns\n\n| ❌ Don't | ✅ Do instead |\n|---------|--------------|\n| \"Uses Clean Architecture with Domain\u002FData layers.\" (when no such directories exist) | State only what directory structure actually shows. |\n| \"This is a Next.js project.\" (without checking `package.json`) | Check `dependencies` first. State what's actually there. |\n| Guess the database from a variable name like `dbUrl` | Check manifest for `pg`, `mysql2`, `mongoose`, `prisma`, etc. |\n| Document `dist\u002F` or `build\u002F` naming patterns as conventions | Source files only. |\n\n---\n\n## Enhanced Scan Output Sections\n\nThe `scan.py` script now produce the following sections in addition to the original output:\n\n- **CODE METRICS** — Total files, lines of code by language, largest files (complexity signals)\n- **CI\u002FCD PIPELINES** — Detected GitHub Actions, GitLab CI, Jenkins, CircleCI, etc.\n- **CONTAINERS & ORCHESTRATION** — Docker, Docker Compose, Kubernetes, Vagrant configs\n- **SECURITY & COMPLIANCE** — Snyk, Dependabot, SECURITY.md, SBOM, security policies\n- **PERFORMANCE & TESTING** — Benchmark configs, profiling markers, load testing tools\n\nUse these sections during Phase 2 to inform investigation questions and identify tool-specific patterns.\n\n---\n\n## Bundled Assets\n\n| Asset | When to load |\n|-------|-------------|\n| [`scripts\u002Fscan.py`](scripts\u002Fscan.py) | Phase 1 — run first, before reading any code (Python 3.8+ required) |\n\n| [`references\u002Finquiry-checkpoints.md`](references\u002Finquiry-checkpoints.md) | Phase 2 — load for per-template investigation questions |\n| [`references\u002Fstack-detection.md`](references\u002Fstack-detection.md) | Phase 2 — only if stack is ambiguous |\n| [`assets\u002Ftemplates\u002FSTACK.md`](assets\u002Ftemplates\u002FSTACK.md) | Phase 3 step 1 |\n| [`assets\u002Ftemplates\u002FSTRUCTURE.md`](assets\u002Ftemplates\u002FSTRUCTURE.md) | Phase 3 step 2 |\n| [`assets\u002Ftemplates\u002FARCHITECTURE.md`](assets\u002Ftemplates\u002FARCHITECTURE.md) | Phase 3 step 3 |\n| [`assets\u002Ftemplates\u002FCONVENTIONS.md`](assets\u002Ftemplates\u002FCONVENTIONS.md) | Phase 3 step 4 |\n| [`assets\u002Ftemplates\u002FINTEGRATIONS.md`](assets\u002Ftemplates\u002FINTEGRATIONS.md) | Phase 3 step 5 |\n| [`assets\u002Ftemplates\u002FTESTING.md`](assets\u002Ftemplates\u002FTESTING.md) | Phase 3 step 6 |\n| [`assets\u002Ftemplates\u002FCONCERNS.md`](assets\u002Ftemplates\u002FCONCERNS.md) | Phase 3 step 7 |\n\nTemplate usage mode:\n\n- Default mode: complete only the \"Core Sections (Required)\" in each template.\n- Extended mode: add optional sections only when the repo complexity justifies them.\n","# Acquire Codebase Knowledge\n\nProduces seven populated documents in `docs\u002Fcodebase\u002F` covering everything needed to work effectively on the project. Only document what is verifiable from files or terminal output — never infer or assume.\n\n## Output Contract (Required)\n\nBefore finishing, all of the following must be true:\n\n1. Exactly these files exist in `docs\u002Fcodebase\u002F`: `STACK.md`, `STRUCTURE.md`, `ARCHITECTURE.md`, `CONVENTIONS.md`, `INTEGRATIONS.md`, `TESTING.md`, `CONCERNS.md`.\n2. Every claim is traceable to source files, config, or terminal output.\n3. Unknowns are marked as `[TODO]`; intent-dependent decisions are marked `[ASK USER]`.\n4. Every document includes a short \"evidence\" list with concrete file paths.\n5. Final response includes numbered `[ASK USER]` questions and intent-vs-reality divergences.\n\n## Workflow\n\nCopy and track this checklist:\n\n```\n- [ ] Phase 1: Run scan, read intent documents\n- [ ] Phase 2: Investigate each documentation area\n- [ ] Phase 3: Populate all seven docs in docs\u002Fcodebase\u002F\n- [ ] Phase 4: Validate docs, present findings, resolve all [ASK USER] items\n```\n\n## Focus Area Mode\n\nIf the user supplies a focus area (for example: \"architecture only\" or \"testing and concerns\"):\n\n1. Always run Phase 1 in full.\n2. Fully complete focus-area documents first.\n3. For non-focus documents not yet analyzed, keep required sections present and mark unknowns as `[TODO]`.\n4. Still run the Phase 4 validation loop on all seven documents before final output.\n\n### Phase 1: Scan and Read Intent\n\n1. Run the scan script from the target project root:\n   ```bash\n   python3 \"$SKILL_ROOT\u002Fscripts\u002Fscan.py\" --output docs\u002Fcodebase\u002F.codebase-scan.txt\n   ```\n   Where `$SKILL_ROOT` is the absolute path to the skill folder. Works on Windows, macOS, and Linux.\n\n   **Quick start:** If you have the path inline:\n   ```bash\n   python3 \u002Fabsolute\u002Fpath\u002Fto\u002Fskills\u002Facquire-codebase-knowledge\u002Fscripts\u002Fscan.py --output docs\u002Fcodebase\u002F.codebase-scan.txt\n   ```\n\n2. Search for `PRD`, `TRD`, `README`, `ROADMAP`, `SPEC`, `DESIGN` files and read them.\n3. Summarise the stated project intent before reading any source code.\n\n### Phase 2: Investigate\n\nUse the scan output to answer questions for each of the seven templates. Load [`references\u002Finquiry-checkpoints.md`](references\u002Finquiry-checkpoints.md) for the full per-template question list.\n\nIf the stack is ambiguous (multiple manifest files, unfamiliar file types, no `package.json`), load [`references\u002Fstack-detection.md`](references\u002Fstack-detection.md).\n\n### Phase 3: Populate Templates\n\nCopy each template from `assets\u002Ftemplates\u002F` into `docs\u002Fcodebase\u002F`. Fill in this order:\n\n1. [STACK.md](assets\u002Ftemplates\u002FSTACK.md) — language, runtime, frameworks, all dependencies\n2. [STRUCTURE.md](assets\u002Ftemplates\u002FSTRUCTURE.md) — directory layout, entry points, key files\n3. [ARCHITECTURE.md](assets\u002Ftemplates\u002FARCHITECTURE.md) — layers, patterns, data flow\n4. [CONVENTIONS.md](assets\u002Ftemplates\u002FCONVENTIONS.md) — naming, formatting, error handling, imports\n5. [INTEGRATIONS.md](assets\u002Ftemplates\u002FINTEGRATIONS.md) — external APIs, databases, auth, monitoring\n6. [TESTING.md](assets\u002Ftemplates\u002FTESTING.md) — frameworks, file organization, mocking strategy\n7. [CONCERNS.md](assets\u002Ftemplates\u002FCONCERNS.md) — tech debt, bugs, security risks, perf bottlenecks\n\nUse `[TODO]` for anything that cannot be determined from code. Use `[ASK USER]` where the right answer requires team intent.\n\n### Phase 4: Validate, Repair, Verify\n\nRun this mandatory validation loop before finalizing:\n\n1. Validate each doc against `references\u002Finquiry-checkpoints.md`.\n2. For each non-trivial claim, confirm at least one evidence reference exists.\n3. If any required section is missing or unsupported:\n  - Fix the document.\n  - Re-run validation.\n4. Repeat until all seven docs pass.\n\nThen present a summary of all seven documents, list every `[ASK USER]` item as a numbered question, and highlight any Intent vs. Reality divergences from Phase 1.\n\nValidation pass criteria:\n\n- No unsupported claims.\n- No empty required sections.\n- Unknowns use `[TODO]` rather than assumptions.\n- Team-intent gaps are explicitly marked `[ASK USER]`.\n\n---\n\n## Gotchas\n\n**Monorepos:** Root `package.json` may have no source — check for `workspaces`, `packages\u002F`, or `apps\u002F` directories. Each workspace may have independent dependencies and conventions. Map each sub-package separately.\n\n**Outdated README:** README often describes intended architecture, not the current one. Cross-reference with actual file structure before treating any README claim as fact.\n\n**TypeScript path aliases:** `tsconfig.json` `paths` config means imports like `@\u002Ffoo` don't map directly to the filesystem. Map aliases to real paths before documenting structure.\n\n**Generated\u002Fcompiled output:** Never document patterns from `dist\u002F`, `build\u002F`, `generated\u002F`, `.next\u002F`, `out\u002F`, or `__pycache__\u002F`. These are artefacts — document source conventions only.\n\n**`.env.example` reveals required config:** Secrets are never committed. Read `.env.example`, `.env.template`, or `.env.sample` to discover required environment variables.\n\n**`devDependencies` ≠ production stack:** Only `dependencies` (or equivalent, e.g. `[tool.poetry.dependencies]`) runs in production. Document linters, formatters, and test frameworks separately as dev tooling.\n\n**Test TODOs ≠ production debt:** TODOs inside `test\u002F`, `tests\u002F`, `__tests__\u002F`, or `spec\u002F` are coverage gaps, not production technical debt. Separate them in `CONCERNS.md`.\n\n**High-churn files = fragile areas:** Files appearing most in recent git history have the highest modification rate and likely hidden complexity. Always note them in `CONCERNS.md`.\n\n---\n\n## Anti-Patterns\n\n| ❌ Don't | ✅ Do instead |\n|---------|--------------|\n| \"Uses Clean Architecture with Domain\u002FData layers.\" (when no such directories exist) | State only what directory structure actually shows. |\n| \"This is a Next.js project.\" (without checking `package.json`) | Check `dependencies` first. State what's actually there. |\n| Guess the database from a variable name like `dbUrl` | Check manifest for `pg`, `mysql2`, `mongoose`, `prisma`, etc. |\n| Document `dist\u002F` or `build\u002F` naming patterns as conventions | Source files only. |\n\n---\n\n## Enhanced Scan Output Sections\n\nThe `scan.py` script now produce the following sections in addition to the original output:\n\n- **CODE METRICS** — Total files, lines of code by language, largest files (complexity signals)\n- **CI\u002FCD PIPELINES** — Detected GitHub Actions, GitLab CI, Jenkins, CircleCI, etc.\n- **CONTAINERS & ORCHESTRATION** — Docker, Docker Compose, Kubernetes, Vagrant configs\n- **SECURITY & COMPLIANCE** — Snyk, Dependabot, SECURITY.md, SBOM, security policies\n- **PERFORMANCE & TESTING** — Benchmark configs, profiling markers, load testing tools\n\nUse these sections during Phase 2 to inform investigation questions and identify tool-specific patterns.\n\n---\n\n## Bundled Assets\n\n| Asset | When to load |\n|-------|-------------|\n| [`scripts\u002Fscan.py`](scripts\u002Fscan.py) | Phase 1 — run first, before reading any code (Python 3.8+ required) |\n\n| [`references\u002Finquiry-checkpoints.md`](references\u002Finquiry-checkpoints.md) | Phase 2 — load for per-template investigation questions |\n| [`references\u002Fstack-detection.md`](references\u002Fstack-detection.md) | Phase 2 — only if stack is ambiguous |\n| [`assets\u002Ftemplates\u002FSTACK.md`](assets\u002Ftemplates\u002FSTACK.md) | Phase 3 step 1 |\n| [`assets\u002Ftemplates\u002FSTRUCTURE.md`](assets\u002Ftemplates\u002FSTRUCTURE.md) | Phase 3 step 2 |\n| [`assets\u002Ftemplates\u002FARCHITECTURE.md`](assets\u002Ftemplates\u002FARCHITECTURE.md) | Phase 3 step 3 |\n| [`assets\u002Ftemplates\u002FCONVENTIONS.md`](assets\u002Ftemplates\u002FCONVENTIONS.md) | Phase 3 step 4 |\n| [`assets\u002Ftemplates\u002FINTEGRATIONS.md`](assets\u002Ftemplates\u002FINTEGRATIONS.md) | Phase 3 step 5 |\n| [`assets\u002Ftemplates\u002FTESTING.md`](assets\u002Ftemplates\u002FTESTING.md) | Phase 3 step 6 |\n| [`assets\u002Ftemplates\u002FCONCERNS.md`](assets\u002Ftemplates\u002FCONCERNS.md) | Phase 3 step 7 |\n\nTemplate usage mode:\n\n- Default mode: complete only the \"Core Sections (Required)\" in each template.\n- Extended mode: add optional sections only when the repo complexity justifies them.\n",{"name":5,"license":15,"metadata":54,"description":7,"argument-hint":63,"compatibility":64},{"version":55,"enhancements":56},"1.3",[57,58,59,60,61,62],"Multi-language manifest detection (25+ languages supported)","CI\u002FCD pipeline detection (10+ platforms)","Container & orchestration detection","Code metrics by language","Security & compliance config detection","Performance testing markers","Optional: specific area to focus on, e.g. \"architecture only\", \"testing and concerns\"","Cross-platform. Requires Python 3.8+ and git. Run scripts\u002Fscan.py from the target project root.","main","653cd9f9f499f8f1ba1ff438a3318b6e2b0652b9",".github\u002Fskills\u002Fcodebase-knowledge","https:\u002F\u002Fgithub.com\u002Faritra-ghosh-sage\u002Fpython-hol","https:\u002F\u002Fgithub.com\u002Faritra-ghosh-sage","2026-06-04 15:02:32",{"source":8,"skillName":5,"commands":72,"prompt":82,"zipAvailable":83},[73,76,79],{"tool":74,"command":75},"npx","npx skills add aritra-ghosh-sage\u002Fpython-hol --skill acquire-codebase-knowledge",{"tool":77,"command":78},"bunx","bunx skills add aritra-ghosh-sage\u002Fpython-hol --skill acquire-codebase-knowledge",{"tool":80,"command":81},"pnpm","pnpm dlx skills add aritra-ghosh-sage\u002Fpython-hol --skill acquire-codebase-knowledge","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Faritra-ghosh-sage\u002Fpython-hol\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fcodebase-knowledge\u002FSKILL.md",true,[],[86,115,143],{"skillNo":87,"name":88,"nameZh":6,"description":89,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":90,"categories":91,"keywords":95,"publishedAt":49},"01M6RB47G13AHREK20H7V7J5QE","prd","Generate high-quality Product Requirements Documents (PRDs) for software systems and AI-powered features. Includes executive summaries, user stories, technical specifications, and risk analysis.","https:\u002F\u002Fgithub.com\u002Faritra-ghosh-sage\u002Fpython-hol\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fprd\u002FSKILL.md",[92],{"categoryNo":93,"categoryName":94,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000031","产品经理",[88,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114],"quality","documents","risk analysis","generate","product","risk","analysis","technical","requirements","systems","software","Product Requirements","specifications","user stories","high","for","summaries","features","stories",{"skillNo":116,"name":117,"nameZh":6,"description":118,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":127,"publishedAt":49},"01M6RAYWANRR6908T6QVGTKR3S","autoresearch","Autonomous iterative experimentation loop for any programming task. Guides the user through defining goals, measurable metrics, and scope constraints, then runs an autonomous loop of code changes, testing, measuring, and keeping\u002Fdiscarding results. Inspired by Karpathy's autoresearch. USE FOR: autonomous improvement, iterative optimization, experiment loop, auto research, performance tuning, automated experimentation, hill climbing, try things automatically, optimize code, run experiments, autonomous coding loop. DO NOT USE FOR: one-shot tasks, simple bug fixes, code review, or tasks without a measurable metric.","https:\u002F\u002Fgithub.com\u002Faritra-ghosh-sage\u002Fpython-hol\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Fautoresearch\u002FSKILL.md",[121,124],{"categoryNo":122,"categoryName":123,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":125,"categoryName":126,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000030","开发工程师",[117,128,36,129,130,131,132,133,134,135,136,34,137,138,48,139,140,44,141,142],"optimize","scope","bug","guides","auto","karpathy","loop","autonomous","code review","review","terminal","tasks","run","repository","access",{"skillNo":144,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":149,"publishedAt":49},"01M6RAWSZV5VGTS47YZ88NDTVD","https:\u002F\u002Fgithub.com\u002Faritra-ghosh-sage\u002Fpython-hol\u002Fblob\u002Fmain\u002F.github\u002Fskills\u002Facquire-codebase-knowledge\u002FSKILL.md",[147,148],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},[31,29,32,30,35,48,47,46,45,44,43,42,41,40,39,38,37,36,33,34]]