Acquire Codebase Knowledge
SkillDocs & knowledgeLets your agent scan a codebase and write seven docs covering its stack, structure, architecture, and conventions.
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Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
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About this skill
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 narr
What this skill tells your AI
The instructions your AI receives, as published by github/awesome-copilot in skills/acquire-codebase-knowledge/SKILL.md and read by ahel’s review.
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.txtWhere
$SKILL_ROOTis 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,DESIGNfiles 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) |
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.
Signals
- GitHub stars
- 40k
- Forks
- 5k
- Last commit
- Oct 2026
ahel review
K6low
bundled executables the agent is told to run
Automated review, not a security audit. Ruleset v1+k2.
Others that do the same job
Advanced
- Item type
- skill
- Key
acquire-codebase-knowledge-github- Source
- github.com/github/awesome-copilot
github.com/github/awesome-copilot
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