Maps, documents, and provides onboarding intelligence for existing codebases by generating seven structured markdown files.
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- 41,038
- Files
- 4
- Last commit
- Nov 11, 2026
- License
- MIT
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What does the acquire-codebase-knowledge skill do?
acquire-codebase-knowledge is an agent skill. Maps, documents, and provides onboarding intelligence for existing codebases by generating seven structured markdown files.
Summary generated from the skill's SKILL.md.
About the acquire-codebase-knowledge skill
Mapping and documenting existing codebases for developer onboarding is what this skill accomplishes. It is designed for users who need to understand a new repository, its architecture, or its technical constraints without manual exploration. An agent should load this skill when explicitly asked to map, document, or provide onboarding intelligence for a project.
The process begins by running a scan script from the project root to generate a baseline report. The skill then investigates the codebase to populate seven specific markdown files in a dedicated directory. These documents cover the technology stack, directory structure, architectural patterns, coding conventions, external integrations, testing strategies, and technical concerns.
Unlike general documentation tools, this skill relies on verifiable evidence from source files, configuration manifests, and terminal output. It avoids making assumptions by using specific markers like [TODO] for unknown information and [ASK USER] for decisions that require human intent. It supports over 25 languages and detects various CI/CD pipelines, container orchestration, and security configurations.
What an agent can do with it
- 1Onboard to a new repositoryGenerate a complete set of documentation to help a developer understand a new codebase quickly.
- 2Perform targeted documentation tasksFocus the discovery process on specific areas like testing or architecture rather than the whole repo.
Written from the skill's SKILL.md. Every use case is checked against a quote from it.
How to install the acquire-codebase-knowledge skill
Each command copies the skill folder into the client's skills directory. Restart the client or start a new session to load it.Install the acquire-codebase-knowledge skill in Claude Code
~/.claude/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.claude/skills/acquire-codebase-knowledgeInstall the acquire-codebase-knowledge skill in Claude Code (project)
.claude/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge .claude/skills/acquire-codebase-knowledgeInstall the acquire-codebase-knowledge skill in Codex
~/.agents/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.agents/skills/acquire-codebase-knowledgeInstall the acquire-codebase-knowledge skill in Gemini CLI
~/.gemini/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.gemini/skills/acquire-codebase-knowledgeInstall the acquire-codebase-knowledge skill in Claude.ai
npx degit github/awesome-copilot/skills/acquire-codebase-knowledge acquire-codebase-knowledge && zip -r acquire-codebase-knowledge.zip acquire-codebase-knowledge # claude.ai → Customize → Skills → + → Create skill → Upload a skill → acquire-codebase-knowledge.zip
Automated checks
Ran Nov 13, 2026Full report →Passed our automated checks. That is not a security audit: read the scripts before you let an agent run them. How checks work
- Source repository linked and reachablegithub/awesome-copilot/skills/acquire-codebase-knowledge is public and reachablegithub.com/github/awesome-copilot/tree/HEAD/skills/acquire-c ↗Pass
- Open source license (OSI)MITspdx.org/licenses/MIT.html ↗Pass
- Commit or release in the last yearLast commit 2026-11-11github.com/github/awesome-copilot/commits ↗Pass
- No download-and-run or credential theft in scripts1 script read; no download-and-run, remote eval, base64 execution or credential uploadPass
- No hidden instructions in SKILL.mdNo override phrases, safeguard switches, credential requests or hidden charactersgithub.com/github/awesome-copilot/blob/HEAD/skills/acquire-c ↗Pass
- Pre-approved tools are scopedNo pre-approved tools; the agent asks before using onegithub.com/github/awesome-copilot/blob/HEAD/skills/acquire-c ↗Pass
SKILL.md
View on GitHubAcquire 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:
- 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.
Files in this skill
4acquire-codebase-knowledge skill questions
How do I install the acquire-codebase-knowledge skill in Claude Code?
Run npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.claude/skills/acquire-codebase-knowledge. Claude Code loads skills from ~/.claude/skills (all projects) or .claude/skills (one project) and uses a skill when its description matches the task.
Which clients can use the acquire-codebase-knowledge skill?
Any client that reads the Agent Skills format: Claude Code, Codex, Gemini CLI and claude.ai (upload as a ZIP). Scripts in the skill run only where the client allows code execution.
What files are generated by this skill?
The skill produces seven specific documents in the docs/codebase/ directory: STACK.md, STRUCTURE.md, ARCHITECTURE.md, CONVENTIONS.md, INTEGRATIONS.md, TESTING.md, and CONCERNS.md.
How does the skill handle unknown information or ambiguity?
Unknowns are explicitly marked with [TODO], and decisions that depend on user intent are marked with [ASK USER].
Can I focus the documentation on a specific area?
Yes, you can provide a focus area such as 'architecture only' or 'testing and concerns' to prioritize specific documentation.