跳到正文
cccskills
免费在 GitHub 公开

acquire-codebase-knowledge

Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

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.

集成工具Docker
查看安装方法

适用场景

  • Get oriented in an unfamiliar codebase
  • Prepare documentation for a project handover
  • Compare documented architecture with implementation
  • Organize findings about tests and technical concerns

包含的文件(11)

  • SKILL.md8.9 KB
  • assets/templates/ARCHITECTURE.md1.2 KB
  • assets/templates/CONCERNS.md1.7 KB
  • assets/templates/CONVENTIONS.md1.3 KB
  • assets/templates/INTEGRATIONS.md1.3 KB
  • assets/templates/STACK.md1.3 KB
  • assets/templates/STRUCTURE.md1.1 KB
  • assets/templates/TESTING.md1.3 KB
  • references/inquiry-checkpoints.md4.1 KB
  • references/stack-detection.md5.5 KB
  • scripts/scan.py23.2 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Acquire Codebase Knowledge

Produces seven populated documents in docs/codebase/ covering everything needed to work effectively on the project. Only document what is verifiable from files or terminal output — never infer or assume.

Output Contract (Required)

Before finishing, all of the following must be true:

  1. Exactly these files exist in docs/codebase/: STACK.md, STRUCTURE.md, ARCHITECTURE.md, CONVENTIONS.md, INTEGRATIONS.md, TESTING.md, CONCERNS.md.
  2. Every claim is traceable to source files, config, or terminal output.
  3. Unknowns are marked as [TODO]; intent-dependent decisions are marked [ASK USER].
  4. Every document includes a short "evidence" list with concrete file paths.
  5. Final response includes numbered [ASK USER] questions and intent-vs-reality divergences.

Workflow

Copy and track this checklist:

- [ ] Phase 1: Run scan, read intent documents
- [ ] Phase 2: Investigate each documentation area
- [ ] Phase 3: Populate all seven docs in docs/codebase/
- [ ] Phase 4: Validate docs, present findings, resolve all [ASK USER] items

Focus Area Mode

If the user supplies a focus area (for example: "architecture only" or "testing and concerns"):

  1. Always run Phase 1 in full.
  2. Fully complete focus-area documents first.
  3. For non-focus documents not yet analyzed, keep required sections present and mark unknowns as [TODO].
  4. Still run the Phase 4 validation loop on all seven documents before final output.

Phase 1: Scan and Read Intent

  1. Run the scan script from the target project root:

    python3 "$SKILL_ROOT/scripts/scan.py" --output docs/codebase/.codebase-scan.txt
    

    Where $SKILL_ROOT is the absolute path to the skill folder. Works on Windows, macOS, and Linux.

    Quick start: If you have the path inline:

    python3 /absolute/path/to/skills/acquire-codebase-knowledge/scripts/scan.py --output docs/codebase/.codebase-scan.txt
    
  2. Search for PRD, TRD, README, ROADMAP, SPEC, DESIGN files and read them.

  3. Summarise the stated project intent before reading any source code.

Phase 2: Investigate

Use the scan output to answer questions for each of the seven templates. Load references/inquiry-checkpoints.md for the full per-template question list.

If the stack is ambiguous (multiple manifest files, unfamiliar file types, no package.json), load references/stack-detection.md.

Phase 3: Populate Templates

Copy each template from assets/templates/ into docs/codebase/. Fill in this order:

  1. STACK.md — language, runtime, frameworks, all dependencies
  2. STRUCTURE.md — directory layout, entry points, key files
  3. ARCHITECTURE.md — layers, patterns, data flow
  4. CONVENTIONS.md — naming, formatting, error handling, imports
  5. INTEGRATIONS.md — external APIs, databases, auth, monitoring
  6. TESTING.md — frameworks, file organization, mocking strategy
  7. CONCERNS.md — tech debt, bugs, security risks, perf bottlenecks

Use [TODO] for anything that cannot be determined from code. Use [ASK USER] where the right answer requires team intent.

Phase 4: Validate, Repair, Verify

Run this mandatory validation loop before finalizing:

  1. Validate each doc against references/inquiry-checkpoints.md.
  2. For each non-trivial claim, confirm at least one evidence reference exists.
  3. If any required section is missing or unsupported:
  • Fix the document.
  • Re-run validation.
  1. Repeat until all seven docs pass.

Then 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.

Validation pass criteria:

  • No unsupported claims.
  • No empty required sections.
  • Unknowns use [TODO] rather than assumptions.
  • Team-intent gaps are explicitly marked [ASK USER].

Gotchas

Monorepos: Root package.json may have no source — check for workspaces, packages/, or apps/ directories. Each workspace may have independent dependencies and conventions. Map each sub-package separately.

Outdated README: README often describes intended architecture, not the current one. Cross-reference with actual file structure before treating any README claim as fact.

TypeScript path aliases: tsconfig.json paths config means imports like @/foo don't map directly to the filesystem. Map aliases to real paths before documenting structure.

Generated/compiled output: Never document patterns from dist/, build/, generated/, .next/, out/, or __pycache__/. These are artefacts — document source conventions only.

.env.example reveals required config: Secrets are never committed. Read .env.example, .env.template, or .env.sample to discover required environment variables.

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.

Test TODOs ≠ production debt: TODOs inside test/, tests/, __tests__/, or spec/ are coverage gaps, not production technical debt. Separate them in CONCERNS.md.

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.


Anti-Patterns

❌ Don't✅ Do instead
"Uses Clean Architecture with Domain/Data layers." (when no such directories exist)State only what directory structure actually shows.
"This is a Next.js project." (without checking package.json)Check dependencies first. State what's actually there.
Guess the database from a variable name like dbUrlCheck manifest for pg, mysql2, mongoose, prisma, etc.
Document dist/ or build/ naming patterns as conventionsSource files only.

Enhanced Scan Output Sections

The scan.py script now produce the following sections in addition to the original output:

  • CODE METRICS — Total files, lines of code by language, largest files (complexity signals)
  • CI/CD PIPELINES — Detected GitHub Actions, GitLab CI, Jenkins, CircleCI, etc.
  • CONTAINERS & ORCHESTRATION — Docker, Docker Compose, Kubernetes, Vagrant configs
  • SECURITY & COMPLIANCE — Snyk, Dependabot, SECURITY.md, SBOM, security policies
  • PERFORMANCE & TESTING — Benchmark configs, profiling markers, load testing tools

Use these sections during Phase 2 to inform investigation questions and identify tool-specific patterns.


Bundled Assets

AssetWhen to load
scripts/scan.pyPhase 1 — run first, before reading any code (Python 3.8+ required)
references/inquiry-checkpoints.mdPhase 2 — load for per-template investigation questions
references/stack-detection.mdPhase 2 — only if stack is ambiguous
assets/templates/STACK.mdPhase 3 step 1
assets/templates/STRUCTURE.mdPhase 3 step 2
assets/templates/ARCHITECTURE.mdPhase 3 step 3
assets/templates/CONVENTIONS.mdPhase 3 step 4
assets/templates/INTEGRATIONS.mdPhase 3 step 5
assets/templates/TESTING.mdPhase 3 step 6
assets/templates/CONCERNS.mdPhase 3 step 7

Template usage mode:

  • Default mode: complete only the "Core Sections (Required)" in each template.
  • Extended mode: add optional sections only when the repo complexity justifies them.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Assess a repository’s readiness for AI agents with AgentRC and generate a standalone HTML dashboard. Review scores across nine areas, see prioritized improvements, and optionally apply scoring policies tailored to an organization.

中文简介正在准备中。当前显示英语简介。

  • Assessing a repository’s readiness for AI agents
  • Prioritizing improvements from assessment results
  • Reviewing scores under an organization’s own policy
github/awesome-copilot4万2026年10月17日 更新

Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.

中文简介正在准备中。当前显示英语简介。

  • Create project instructions for VS Code Copilot
  • Refresh agent rules after project changes
  • Split monorepo instructions by project area
github/awesome-copilot4万2026年10月17日 更新

Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.

中文简介正在准备中。当前显示英语简介。

  • Set shared readiness standards across an organization
  • Focus assessments on AI tooling
  • Adjust the importance of individual checks
github/awesome-copilot4万2026年10月17日 更新

Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.

中文简介正在准备中。当前显示英语简介。

  • Identify ads and keywords to pause
  • Find campaigns and audiences worth scaling
  • Assess whether A/B test results are meaningful
github/awesome-copilot4万2026年10月17日 更新

Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.

中文简介正在准备中。当前显示英语简介。

  • Add beginner-friendly explanations to existing code
  • Document the reasoning behind syntax and design choices
  • Adjust comment depth for readers with different experience
github/awesome-copilot4万2026年10月17日 更新

Create and fix Adobe Illustrator automation scripts using ExtendScript, Adobe’s JavaScript-based scripting language. Automate changes to artwork, text, layers, and artboards, process multiple files, and prepare exports such as PDF, SVG, and PNG.

中文简介正在准备中。当前显示英语简介。

  • Automate repeated changes to artwork and text
  • Export multiple Illustrator files
  • Generate artwork variations from data
github/awesome-copilot4万2026年10月17日 更新

查看 github 的全部技能

报告此技能的问题