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/acquire-codebase-knowledge

@9ce8148 official
by githubgithub/awesome-copilot40k stars
5,040

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.

Use this Skill: https://skilld.dev/gh/github/awesome-copilot/acquire-codebase-knowledge

This session only. Nothing lands on disk.

SKILL.md

β‰ˆ99 tokens always: the name and description. β‰ˆ2k when used: this file. β‰ˆ4.8k more on demand in 9 files.

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 β€” 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 β€” language, runtime, frameworks, all dependencies
  2. STRUCTURE.md β€” directory layout, entry points, key files
  3. ARCHITECTURE.md β€” layers, patterns, data flow
  4. CONVENTIONS.md β€” naming, formatting, error handling, imports
  5. INTEGRATIONS.md β€” external APIs, databases, auth, monitoring
  6. TESTING.md β€” frameworks, file organization, mocking strategy
  7. CONCERNS.md β€” 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.
  1. 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 β€” 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 β€” 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 β‰  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 β‰  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 βœ… 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 β€” Total files, lines of code by language, largest files (complexity signals)
  • CI/CD PIPELINES β€” Detected GitHub Actions, GitLab CI, Jenkins, CircleCI, etc.
  • CONTAINERS & ORCHESTRATION β€” Docker, Docker Compose, Kubernetes, Vagrant configs
  • SECURITY & COMPLIANCE β€” Snyk, Dependabot, SECURITY.md, SBOM, security policies
  • PERFORMANCE & TESTING β€” 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 β€” run first, before reading any code (Python 3.8+ required)
references/inquiry-checkpoints.md Phase 2 β€” load for per-template investigation questions
references/stack-detection.md Phase 2 β€” 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.

Source: SKILL.md on GitHub

No alerts15d4 checks Β· Risk SAFE
  • Gen Agent Trust Hub15d

    This skill facilitates codebase onboarding by scanning local project files and git history to generate documentation. It is generally safe and authored by a trusted vendor, but it carries an inherent low risk of indirect prompt injection if the analyzed codebase contains malicious instructions designed to influence the documentation process.

  • Socket15d

    No alerts

  • Snyk15d

    Risk: LOW Β· No issues

  • ZeroLeaks5mo

    Score: 93/100 Β· 2 sections analyzed

Signed by skilld at 9ce8148. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 19 hours ago.

Activeupdated 2 weeks ago
Other metadata
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"

README badge

README badge for github/awesome-copilot/acquire-codebase-knowledge

Scans a codebase with a Python script to extract stack, structure, architecture, conventions, integrations, testing setup, and known concerns, then populates seven markdown documents in docs/codebase/ with verified facts only. Targets onboarding scenarios where a developer or agent needs to understand an unfamiliar repository quickly.

Generated from the current SKILL.md.

Does this skill work with monorepos?
Yes. The skill detects workspaces and packages directories, and maps each sub-package separately with independent dependencies and conventions.
What languages and platforms does this support?
Cross-platform (Windows, macOS, Linux). Requires Python 3.8+, git, and detects 25+ programming languages. Run scripts/scan.py from the target project root.
Does this document generated code and build artifacts?
No. The skill documents only source conventions and patterns, explicitly avoiding dist/, build/, generated/, .next/, out/, and __pycache__ directories.
What happens if the skill can't determine something about the codebase?
Unknown items are marked [TODO]. If the answer requires team intent or context, it is marked [ASK USER] and presented as a numbered question at the end.
What output does this skill produce?
Seven documented files in docs/codebase/: STACK.md, STRUCTURE.md, ARCHITECTURE.md, CONVENTIONS.md, INTEGRATIONS.md, TESTING.md, and CONCERNS.md, with evidence trails to source files.

Generated from the current SKILL.md. These answers refresh after source changes.