acquire-codebase-knowledge
🤖 AI Summary
This agent skill generates seven specific documentation files (`STACK.md`, `STRUCTURE.md`, etc.) in `docs/codebase/` by extracting only verifiable facts from source files, configs, and terminal output, marking any unknowns as `[TODO]` and intent-dependent decisions as `[ASK USER]`.
How to Install
Claude Code:
git clone --depth 1 https://github.com/github/awesome-copilot.git && cp awesome-copilot/skills/acquire-codebase-knowledge ~/.claude/skills/acquire-codebase-knowledge -r# 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:
```bash
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:
```bash
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`](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`](references/stack-detection.md).
### Phase 3: Populate Templates
Copy each template from `assets/templates/` into `docs/codebase/`. Fill in this order:
1. [STACK.md](assets/templates/STACK.md) — language, runtime, frameworks, all dependencies
2. [STRUCTURE.md](assets/templates/STRUCTURE.md) — directory layout, entry points, key files
3. [ARCHITECTURE.md](assets/templates/ARCHITECTURE.md) — layers, patterns, data flow
4. [CONVENTIONS.md](assets/templates/CONVENTIONS.md) — naming, formatting, error handling, imp
Details
| Category | Design → ui |
| Source | github/awesome-copilot |
| SKILL.md | View on GitHub → |
| Repo Stars | ★ 35.6K |
| Est. per Skill | 712 (shared across 50 skills from this repo) |
| Difficulty | Intermediate |
| Risk Level | N/A |
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