acquire-codebase-knowledge

작성자: github

사용자가 기존 코드베이스에 대한 매핑, 문서화, 또는 온보딩을 명시적으로 요청할 때 이 스킬을 사용하세요. "이 코드베이스를 매핑해줘", "문서화해줘"와 같은 프롬프트에서 트리거됩니다.

npx skills add https://github.com/github/awesome-copilot --skill acquire-codebase-knowledge

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.md | Phase 2 — load for per-template investigation questions | | references/stack-detection.md | Phase 2 — only if stack is ambiguous | | assets/templates/STACK.md | Phase 3 step 1 | | assets/templates/STRUCTURE.md | Phase 3 step 2 | | assets/templates/ARCHITECTURE.md | Phase 3 step 3 | | assets/templates/CONVENTIONS.md | Phase 3 step 4 | | assets/templates/INTEGRATIONS.md | Phase 3 step 5 | | assets/templates/TESTING.md | Phase 3 step 6 | | assets/templates/CONCERNS.md | Phase 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.

github의 다른 스킬

console-rendering
github
Go에서 struct 태그 기반 콘솔 렌더링 시스템 사용 지침
official
acreadiness-assess
github
현재 리포
official
acreadiness-generate-instructions
github
AgentRC 명령어를 통해 맞춤형 AI 에이전트 지침 파일을 생성합니다. .github/copilot-instructions.md 파일을 생성합니다(기본값, VS Code의 Copilot에 권장됨).
official
acreadiness-policy
github
사용자가 AgentRC 정책을 선택, 작성 또는 적용할 수 있도록 지원합니다. 정책은 관련 없는 검사를 비활성화하고, 영향/수준을 재정의하며, 설정을 통해 준비 상태 점수를 사용자 지정합니다.
official
add-educational-comments
github
코드 파일에 교육용 주석을 추가하여 효과적인 학습 자료로 변환합니다. 설명의 깊이와 어조를 세 가지 설정 가능한 지식 수준(초급, 중급, 고급)에 맞게 조정합니다. 파일이 제공되지 않으면 자동으로 요청하며, 빠른 선택을 위해 번호 목록 매칭을 제공합니다. 교육용 주석만을 사용하여 파일을 최대 125%까지 확장합니다(엄격한 제한: 새 줄 400개, 1,000줄 초과 파일의 경우 300개). 파일 인코딩, 들여쓰기 스타일, 구문 정확성 등을 유지합니다.
official
adobe-illustrator-scripting
github
Adobe Illustrator 자동화 스크립트를 ExtendScript(JavaScript/JSX)로 작성, 디버깅 및 최적화합니다. 스크립트를 생성하거나 수정하여 조작할 때 사용합니다.
official
agent-governance
github
선언적 정책, 의도 분류, AI 에이전트 도구 접근 및 행동 제어를 위한 감사 추적. 구성 가능한 거버넌스 정책은 허용/차단된 도구, 콘텐츠 필터, 속도 제한, 승인 요구 사항을 정의하며, 코드가 아닌 구성으로 저장됨. 의미론적 의도 분류는 패턴 기반 신호를 사용하여 도구 실행 전에 위험한 프롬프트(데이터 유출, 권한 상승, 프롬프트 인젝션)를 탐지함. 도구 수준 거버넌스 데코레이터는 함수에서 정책을 적용함...
official
agent-owasp-compliance
github
AI 에이전트 시스템을 OWASP Agentic Security Initiative (ASI) Top 10 — 에이전트 보안 태세의 업계 표준 — 에 따라 평가합니다.
official