Mapea, documenta y proporciona inteligencia de onboarding para bases de código existentes mediante la generación de siete archivos markdown estructurados.
- Estrellas en GitHub
- 41.038
- Archivos
- 4
- Último commit
- 11 nov 2026
- Licencia
- MIT
- Herramientas permitidas
- —
¿Qué hace la skill acquire-codebase-knowledge?
acquire-codebase-knowledge es una agent skill. Mapea, documenta y proporciona inteligencia de onboarding para bases de código existentes mediante la generación de siete archivos markdown estructurados.
Resumen generado a partir del SKILL.md de la skill.
Sobre la skill acquire-codebase-knowledge
Este SKILL se encarga de mapear y documentar bases de código existentes para el onboarding de desarrolladores. Está diseñado para usuarios que necesitan entender un nuevo repositorio, su arquitectura o sus restricciones técnicas sin tener que explorarlo manualmente. Un agente debe cargar este SKILL cuando se le pida explícitamente mapear, documentar o proporcionar inteligencia de onboarding para un proyecto.
El proceso comienza ejecutando un script de escaneo desde la raíz del proyecto para generar un informe base. Luego, el SKILL investiga la base de código para completar siete archivos markdown específicos en un directorio dedicado. Estos documentos cubren el stack tecnológico, la estructura de directorios, los patrones arquitectónicos, las convenciones de código, las integraciones externas, las estrategias de testing y las preocupaciones técnicas.
A diferencia de las herramientas de documentación general, este SKILL se basa en evidencia verificable de los archivos fuente, manifiestos de configuración y la salida de la terminal. Evita hacer suposiciones utilizando marcadores específicos como [TODO] para información desconocida y [ASK USER] para decisiones que requieren la intención humana. Soporta más de 25 lenguajes y detecta varios pipelines de CI/CD, orquestación de contenedores y configuraciones de seguridad.
Lo que un agente puede hacer con ella
- 1Onboarding en un nuevo repositorioGenera un conjunto completo de documentación para ayudar a un desarrollador a entender rápidamente una nueva base de código.
- 2Realizar tareas de documentación específicasEnfoca el proceso de descubrimiento en áreas particulares como testing o arquitectura en lugar de todo el repositorio.
Escrito a partir del SKILL.md de la skill. Cada caso de uso se verifica con una cita del mismo.
Cómo instalar la skill acquire-codebase-knowledge
Cada comando copia la carpeta de la skill al directorio de skills del cliente. Reinicia el cliente o inicia una nueva sesión para cargarla.Instalar la skill acquire-codebase-knowledge en Claude Code
~/.claude/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.claude/skills/acquire-codebase-knowledgeInstalar la skill acquire-codebase-knowledge en Claude Code (project)
.claude/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge .claude/skills/acquire-codebase-knowledgeInstalar la skill acquire-codebase-knowledge en Codex
~/.agents/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.agents/skills/acquire-codebase-knowledgeInstalar la skill acquire-codebase-knowledge en Gemini CLI
~/.gemini/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.gemini/skills/acquire-codebase-knowledgeInstalar la skill acquire-codebase-knowledge en 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
Verificaciones automáticas
Ejecutado 13 nov 2026Informe completo →Superó nuestras comprobaciones automáticas. No es una auditoría de seguridad: lee los scripts antes de dejar que un agente los ejecute. Cómo funcionan las verificaciones
- Repositorio de origen vinculado y accesiblegithub/awesome-copilot/skills/acquire-codebase-knowledge is public and reachablegithub.com/github/awesome-copilot/tree/HEAD/skills/acquire-c ↗Aprobado
- Licencia de código abierto (OSI)MITspdx.org/licenses/MIT.html ↗Aprobado
- Commit o lanzamiento en el último añoLast commit 2026-11-11github.com/github/awesome-copilot/commits ↗Aprobado
- Sin descargar y ejecutar ni robo de credenciales en los scripts1 script read; no download-and-run, remote eval, base64 execution or credential uploadAprobado
- Sin instrucciones ocultas en SKILL.mdNo override phrases, safeguard switches, credential requests or hidden charactersgithub.com/github/awesome-copilot/blob/HEAD/skills/acquire-c ↗Aprobado
- Las herramientas preaprobadas están acotadasNo pre-approved tools; the agent asks before using onegithub.com/github/awesome-copilot/blob/HEAD/skills/acquire-c ↗Aprobado
SKILL.md
Ver en 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.
Archivos de esta skill
4Preguntas sobre la skill acquire-codebase-knowledge
¿Cómo instalo la skill acquire-codebase-knowledge en Claude Code?
Ejecuta npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.claude/skills/acquire-codebase-knowledge. Claude Code carga las skills desde ~/.claude/skills (todos los proyectos) o .claude/skills (un proyecto) y usa una skill cuando su descripción coincide con la tarea.
¿Qué clientes pueden usar la skill acquire-codebase-knowledge?
Cualquier cliente que lea el formato Agent Skills: Claude Code, Codex, Gemini CLI y claude.ai (subir como ZIP). Los scripts de la skill se ejecutan solo donde el cliente permite la ejecución de código.
¿Qué archivos genera este SKILL?
El SKILL produce siete documentos específicos en el directorio docs/codebase/: STACK.md, STRUCTURE.md, ARCHITECTURE.md, CONVENTIONS.md, INTEGRATIONS.md, TESTING.md y CONCERNS.md.
¿Cómo maneja el SKILL la información desconocida o la ambigüedad?
Lo desconocido se marca explícitamente con [TODO], y las decisiones que dependen de la intención del usuario se marcan con [ASK USER].
¿Puedo centrar la documentación en un área específica?
Sí, puedes indicar un área de enfoque como 'architecture only' o 'testing and concerns' para priorizar documentación específica.