Acquire Codebase Knowledge
GitHubOfficialFirst committed by SriSatyaLokeshDistributed by GitHub
- Skill
- Workflow
- Software development
- MIT
- Low risk
- Has scripts
From the skill’s files
Taken from the skill’s own files (its description, section headings and stated usage). Not an AutoGineer summary, and not written by the author as one.
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. 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.
What it does
- Output Contract (Required)
- Workflow
- Focus Area Mode
- Gotchas
- Anti-Patterns
- Enhanced Scan Output Sections
When to use it
- 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".
Advantages
The skill’s files do not state this.
Usage examples
The skill’s files do not state this.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/github/acquire-codebase-knowledge/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip acquire-codebase-knowledge.zip -d .agents/skills
Documentation by SriSatyaLokesh
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:
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] itemsFocus 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.txtSearch 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
- ATTRIBUTION.mdAdded by AutoGineer854 B
- LICENSELicence file added by AutoGineer1.0 KiB
- SKILL.md8.9 KiB
- assets/templates/ARCHITECTURE.md1.2 KiB
- assets/templates/CONCERNS.md1.7 KiB
- assets/templates/CONVENTIONS.md1.3 KiB
- assets/templates/INTEGRATIONS.md1.3 KiB
- assets/templates/STACK.md1.3 KiB
- assets/templates/STRUCTURE.md1.1 KiB
- assets/templates/TESTING.md1.3 KiB
- references/inquiry-checkpoints.md4.1 KiB
- references/stack-detection.md5.5 KiB
- scripts/scan.pyScript23.2 KiB
references/inquiry-checkpoints.md
Inquiry Checkpoints
Per-template investigation questions for Phase 2 of the acquire-codebase-knowledge workflow. For each template area, look for answers in the scan output first, then read source files to fill gaps.
1. STACK.md — Tech Stack
What is the primary language and exact version? (check
.nvmrc,go.mod,pyproject.toml, DockerFROMline)What package manager is used? (
npm,yarn,pnpm,go mod,pip,uv)What are the core runtime frameworks? (web server, ORM, DI container)
What do
dependencies(production) vsdevDependencies(dev tooling) contain?Is there a Docker image and what base image does it use?
What are the key scripts in
package.json/Makefile/pyproject.toml?
2. STRUCTURE.md — Directory Layout
Where does source code live? (usually
src/,lib/, or project root for Go)What are the entry points? (check
maininpackage.json,scripts.start,cmd/main.go,app.py)What is the stated purpose of each top-level directory?
Are there non-obvious directories (e.g.,
eng/,platform/,infra/)?Are there hidden config directories (
.github/,.vscode/,.husky/)?What naming conventions do directories follow? (camelCase, kebab-case, domain-based vs layer-based)
3. ARCHITECTURE.md — Patterns
Is the code organized by layer (controllers → services → repos) or by feature?
What is the primary data flow? Trace one request or command from entry to data store.
Are there singletons, dependency injection patterns, or explicit initialization order requirements?
Are there background workers, queues, or event-driven components?
What design patterns appear repeatedly? (Factory, Repository, Decorator, Strategy)
4. CONVENTIONS.md — Coding Standards
What is the file naming convention? (check 10+ files — camelCase, kebab-case, PascalCase)
What is the function and variable naming convention?
Are private methods/fields prefixed (e.g.,
_methodName,#field)?What linter and formatter are configured? (check
.eslintrc,.prettierrc,golangci.yml)What are the TypeScript strictness settings? (
strict,noImplicitAny, etc.)How are errors handled at each layer? (throw vs. return structured error)
What logging library is used and what is the log message format?
How are imports organized? (barrel exports, path aliases, grouping rules)
5. INTEGRATIONS.md — External Services
What external APIs are called? (search for
axios.,fetch(,http.Get(, base URLs in constants)How are credentials stored and accessed? (
.env, secrets manager, env vars)What databases are connected? (check manifest for
pg,mongoose,prisma,typeorm,sqlalchemy)Is there an API gateway, service mesh, or proxy between the app and external services?
What monitoring or observability tools are used? (APM, Prometheus, logging pipeline)
Are there message queues or event buses? (Kafka, RabbitMQ, SQS, Pub/Sub)
6. TESTING.md — Test Setup
What test runner is configured? (check
scripts.testinpackage.json,pytest.ini,go test)Where are test files located? (alongside source, in
tests/, in__tests__/)What assertion library is used? (Jest expect, Chai, pytest assert)
How are external dependencies mocked? (jest.mock, dependency injection, fixtures)
Are there integration tests that hit real services vs. unit tests with mocks?
Is there a coverage threshold enforced? (check
jest.config.js,.nycrc,pyproject.toml)
7. CONCERNS.md — Known Issues
How many TODOs/FIXMEs/HACKs are in production code? (see scan output)
Which files have the highest git churn in the last 90 days? (see scan output)
Are there any files over 500 lines that mix multiple responsibilities?
Do any services make sequential calls that could be parallelized?
Are there hardcoded values (URLs, IDs, magic numbers) that should be config?
What security risks exist? (missing input validation, raw error messages exposed to clients, missing auth checks)
Are there performance patterns that don't scale? (N+1 queries, in-memory caches in multi-instance setups)
Requirements and compatibility
Written for: GitHub Copilot, Claude Code
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Claude Code frontmatter, Needs Python
The author says: Cross-platform. Requires Python 3.8+ and git. Run scripts/scan.py from the target project root.
Security
What our scanner noticed· Low risk
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- First committed by
SriSatyaLokesh
- Publisher
- GitHub (Official)
- Distributed by
- GitHub
- Source
- github/awesome-copilot · skills/acquire-codebase-knowledge · commit 797337f
- Licence
- MIT License (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
- Contributors
- SriSatyaLokesh, aaronpowell
AutoGineer is not affiliated with or endorsed by SriSatyaLokesh or GitHub.
Download ZIP
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