Acquire Codebase Knowledge

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

| 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.