acquire-codebase-knowledge

name: acquire-codebase-knowledge description: '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.' license: MIT compatibility: 'Cross-platform. Requires Python 3.8+ and git. Run scripts/scan.py from the target project root.' metadata: version: "1.3" enhancements: - Multi-language manifest detection (25+ languages supported) - CI/CD pipeline detection (10+ platforms) - Container & orchestration detection - Code metrics by language - Security & compliance config detection - Performance testing markers argument-hint: 'Optional: specific area to focus on, e.g. "architecture only", "testing and concerns"'

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.

4. 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

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

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.