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Acquire Codebase Knowledge

GitHubOfficialFirst committed by SriSatyaLokeshDistributed by GitHub

  • Skill
  • Workflow
  • Software development
  • MIT
  • Low risk
  • Has scripts

From the author

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.
— the description by SriSatyaLokesh

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

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/github/acquire-codebase-knowledge/download
  2. Unzip 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:

  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.

  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 — 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 dbUrlCheck manifest for pg, mysql2, mongoose, prisma, etc.
Document dist/ or build/ naming patterns as conventionsSource 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

AssetWhen to load
scripts/scan.pyPhase 1 — run first, before reading any code (Python 3.8+ required)
references/inquiry-checkpoints.mdPhase 2 — load for per-template investigation questions
references/stack-detection.mdPhase 2 — only if stack is ambiguous
assets/templates/STACK.mdPhase 3 step 1
assets/templates/STRUCTURE.mdPhase 3 step 2
assets/templates/ARCHITECTURE.mdPhase 3 step 3
assets/templates/CONVENTIONS.mdPhase 3 step 4
assets/templates/INTEGRATIONS.mdPhase 3 step 5
assets/templates/TESTING.mdPhase 3 step 6
assets/templates/CONCERNS.mdPhase 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

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, Docker FROM line)

  • 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) vs devDependencies (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 main in package.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.test in package.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
GitHub avatar of SriSatyaLokeshSriSatyaLokesh
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

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download acquire-codebase-knowledge.zip