acquire-codebase-knowledge

von github

Verwenden Sie diese Fähigkeit, wenn der Benutzer explizit darum bittet, eine bestehende Codebasis zu kartieren, zu dokumentieren oder sich in sie einzuarbeiten. Auslöser für Aufforderungen wie „diese Codebasis kartieren“, „dokumentieren…

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

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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:

Exactly these files exist in docs/codebase/: STACK.md, STRUCTURE.md, ARCHITECTURE.md, CONVENTIONS.md, INTEGRATIONS.md, TESTING.md, CONCERNS.md.
Every claim is traceable to source files, config, or terminal output.
Unknowns are marked as [TODO]; intent-dependent decisions are marked [ASK USER].
Every document includes a short "evidence" list with concrete file paths.
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"):

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

Phase 1: Scan and Read Intent

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

Search for PRD, TRD, README, ROADMAP, SPEC, DESIGN files and read them.
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:

STACK.md — language, runtime, frameworks, all dependencies
STRUCTURE.md — directory layout, entry points, key files
ARCHITECTURE.md — layers, patterns, data flow
CONVENTIONS.md — naming, formatting, error handling, imports
INTEGRATIONS.md — external APIs, databases, auth, monitoring
TESTING.md — frameworks, file organization, mocking strategy
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:

Validate each doc against references/inquiry-checkpoints.md.
For each non-trivial claim, confirm at least one evidence reference exists.
If any required section is missing or unsupported:

Fix the document.
Re-run validation.

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 `dbUrl`	Check manifest for `pg`, `mysql2`, `mongoose`, `prisma`, etc.
Document `dist/` or `build/` naming patterns as conventions	Source 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

Asset	When to load
`scripts/scan.py`	Phase 1 — run first, before reading any code (Python 3.8+ required)

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.