Mapeia, documenta e fornece inteligência de onboarding para bases de código existentes através da geração de sete arquivos markdown estruturados.
- Estrelas no GitHub
- 41.038
- Arquivos
- 4
- Último commit
- 11 de nov. de 2026
- Licença
- MIT
- Ferramentas permitidas
- —
O que a skill acquire-codebase-knowledge faz?
acquire-codebase-knowledge é uma Agent Skill. Mapeia, documenta e fornece inteligência de onboarding para bases de código existentes através da geração de sete arquivos markdown estruturados.
Resumo gerado a partir do SKILL.md da skill.
Sobre a skill acquire-codebase-knowledge
O objetivo desta skill é mapear e documentar bases de código existentes para o onboarding de desenvolvedores. Ela foi projetada para usuários que precisam entender um novo repositório, sua arquitetura ou suas restrições técnicas sem a necessidade de exploração manual. Um agente deve carregar esta skill quando for explicitamente solicitado a mapear, documentar ou fornecer inteligência de onboarding para um projeto.
O processo começa com a execução de um script de varredura a partir da raiz do projeto para gerar um relatório de base. Em seguida, a skill investiga a base de código para preencher sete arquivos markdown específicos em um diretório dedicado. Esses documentos cobrem a stack tecnológica, estrutura de diretórios, padrões arquiteturais, convenções de código, integrações externas, estratégias de teste e preocupações técnicas.
Diferente de ferramentas de documentação genéricas, esta skill baseia-se em evidências verificáveis de arquivos de origem, manifestos de configuração e saída do terminal. Ela evita fazer suposições ao utilizar marcadores específicos como [TODO] para informações desconhecidas e [ASK USER] para decisões que exigem a intenção humana. A skill suporta mais de 25 linguagens e detecta diversos pipelines de CI/CD, orquestração de containers e configurações de segurança.
O que um agente pode fazer com ela
- 1Onboarding em um novo repositórioGere um conjunto completo de documentação para ajudar um desenvolvedor a entender uma nova base de código rapidamente.
- 2Realizar tarefas de documentação direcionadasFoque o processo de descoberta em áreas específicas, como testes ou arquitetura, em vez de todo o repositório.
Escrito a partir do SKILL.md da skill. Cada caso de uso é verificado com uma citação dele.
Como instalar a skill acquire-codebase-knowledge
Cada comando copia a pasta da skill para o diretório de skills do cliente. Reinicie o cliente ou inicie uma nova sessão para carregá-la.Instalar a skill acquire-codebase-knowledge no Claude Code
~/.claude/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.claude/skills/acquire-codebase-knowledgeInstalar a skill acquire-codebase-knowledge no Claude Code (project)
.claude/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge .claude/skills/acquire-codebase-knowledgeInstalar a skill acquire-codebase-knowledge no Codex
~/.agents/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.agents/skills/acquire-codebase-knowledgeInstalar a skill acquire-codebase-knowledge no Gemini CLI
~/.gemini/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.gemini/skills/acquire-codebase-knowledgeInstalar a skill acquire-codebase-knowledge no 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
Verificações automatizadas
Executado em 13 de nov. de 2026Relatório completo →Passou nas nossas verificações automáticas. Não é uma auditoria de segurança: leia os scripts antes de deixar um agente executá-los. Como as verificações funcionam
- Repositório de origem vinculado e acessívelgithub/awesome-copilot/skills/acquire-codebase-knowledge is public and reachablegithub.com/github/awesome-copilot/tree/HEAD/skills/acquire-c ↗Passou
- Licença de código aberto (OSI)MITspdx.org/licenses/MIT.html ↗Passou
- Commit ou release no último anoLast commit 2026-11-11github.com/github/awesome-copilot/commits ↗Passou
- Sem baixar e executar nem roubo de credenciais nos scripts1 script read; no download-and-run, remote eval, base64 execution or credential uploadPassou
- Sem instruções ocultas no SKILL.mdNo override phrases, safeguard switches, credential requests or hidden charactersgithub.com/github/awesome-copilot/blob/HEAD/skills/acquire-c ↗Passou
- Ferramentas pré-aprovadas são restritasNo pre-approved tools; the agent asks before using onegithub.com/github/awesome-copilot/blob/HEAD/skills/acquire-c ↗Passou
SKILL.md
Ver no 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.
Arquivos desta skill
4Perguntas sobre a skill acquire-codebase-knowledge
Como instalo a skill acquire-codebase-knowledge no Claude Code?
Execute npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.claude/skills/acquire-codebase-knowledge. O Claude Code carrega skills de ~/.claude/skills (todos os projetos) ou .claude/skills (um projeto) e usa uma skill quando a descrição dela corresponde à tarefa.
Quais clientes podem usar a skill acquire-codebase-knowledge?
Qualquer cliente que leia o formato Agent Skills: Claude Code, Codex, Gemini CLI e claude.ai (envie como ZIP). Scripts da skill são executados apenas onde o cliente permite execução de código.
Quais arquivos são gerados por esta skill?
A skill produz sete documentos específicos no diretório docs/codebase/: STACK.md, STRUCTURE.md, ARCHITECTURE.md, CONVENTIONS.md, INTEGRATIONS.md, TESTING.md e CONCERNS.md.
Como a skill lida com informações desconhecidas ou ambiguidades?
O que for desconhecido é explicitamente marcado com [TODO], e decisões que dependem da intenção do usuário são marcadas com [ASK USER].
Posso focar a documentação em uma área específica?
Sim, você pode fornecer uma área de foco, como 'architecture only' ou 'testing and concerns', para priorizar documentações específicas.