Memetakan, mendokumentasikan, dan menyediakan informasi onboarding untuk codebase yang sudah ada dengan menghasilkan tujuh file markdown terstruktur.
- Bintang GitHub
- 41.038
- File
- 4
- Commit terakhir
- 11 Nov 2026
- Lisensi
- MIT
- Tool yang diizinkan
- —
Apa fungsi skill acquire-codebase-knowledge?
acquire-codebase-knowledge adalah agent skill. Memetakan, mendokumentasikan, dan menyediakan informasi onboarding untuk codebase yang sudah ada dengan menghasilkan tujuh file markdown terstruktur.
Ringkasan dibuat dari SKILL.md skill ini.
Tentang skill acquire-codebase-knowledge
Skill ini berfungsi untuk memetakan dan mendokumentasikan codebase yang sudah ada guna keperluan onboarding developer. Skill ini dirancang bagi pengguna yang perlu memahami repositori baru, arsitekturnya, atau batasan teknisnya tanpa harus melakukan eksplorasi manual. Seorang agent harus memuat skill ini ketika diminta secara eksplisit untuk memetakan, mendokumentasikan, atau menyediakan informasi onboarding untuk sebuah proyek.
Proses dimulai dengan menjalankan script scan dari root proyek untuk menghasilkan laporan baseline. Selanjutnya, skill ini akan menginvestigasi codebase untuk mengisi tujuh file markdown spesifik di dalam direktori khusus. Dokumen-dokumen ini mencakup technology stack, struktur direktori, pola arsitektur, coding conventions, integrasi eksternal, strategi testing, dan concerns teknis.
Berbeda dengan tool dokumentasi umum, skill ini mengandalkan bukti yang dapat diverifikasi dari source files, manifest konfigurasi, dan output terminal. Skill ini menghindari asumsi dengan menggunakan marker spesifik seperti [TODO] untuk informasi yang tidak diketahui dan [ASK USER] untuk keputusan yang memerlukan input manusia. Skill ini mendukung lebih dari 25 bahasa serta dapat mendeteksi berbagai pipeline CI/CD, orkestrasi container, dan konfigurasi keamanan.
Apa yang bisa dilakukan agent dengannya
- 1Onboarding ke repositori baruMenghasilkan set dokumentasi lengkap untuk membantu developer memahami codebase baru dengan cepat.
- 2Melakukan tugas dokumentasi terarahMemfokuskan proses discovery pada area spesifik seperti testing atau arsitektur daripada seluruh repositori.
Ditulis dari SKILL.md milik skill tersebut. Setiap use case diverifikasi dengan kutipan darinya.
Cara install skill acquire-codebase-knowledge
Setiap perintah menyalin folder skill ke direktori skills client. Restart client atau mulai sesi baru untuk memuatnya.Install skill acquire-codebase-knowledge di Claude Code
~/.claude/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.claude/skills/acquire-codebase-knowledgeInstall skill acquire-codebase-knowledge di Claude Code (project)
.claude/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge .claude/skills/acquire-codebase-knowledgeInstall skill acquire-codebase-knowledge di Codex
~/.agents/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.agents/skills/acquire-codebase-knowledgeInstall skill acquire-codebase-knowledge di Gemini CLI
~/.gemini/skills/acquire-codebase-knowledge/SKILL.md
$ npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.gemini/skills/acquire-codebase-knowledgeInstall skill acquire-codebase-knowledge di 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
Pengecekan otomatis
Berjalan 13 Nov 2026Laporan lengkap →Lolos cek otomatis kami. Ini bukan audit keamanan: baca skripnya sebelum membiarkan agent menjalankannya. Cara kerja checks
- Source repository terhubung dan dapat dijangkaugithub/awesome-copilot/skills/acquire-codebase-knowledge is public and reachablegithub.com/github/awesome-copilot/tree/HEAD/skills/acquire-c ↗Lolos
- Lisensi open source (OSI)MITspdx.org/licenses/MIT.html ↗Lolos
- Ada commit atau release dalam satu tahun terakhirLast commit 2026-11-11github.com/github/awesome-copilot/commits ↗Lolos
- Skrip tanpa unduh-lalu-jalankan atau pencurian kredensial1 script read; no download-and-run, remote eval, base64 execution or credential uploadLolos
- Tidak ada instruksi tersembunyi di SKILL.mdNo override phrases, safeguard switches, credential requests or hidden charactersgithub.com/github/awesome-copilot/blob/HEAD/skills/acquire-c ↗Lolos
- Tool yang diizinkan otomatis dibatasiNo pre-approved tools; the agent asks before using onegithub.com/github/awesome-copilot/blob/HEAD/skills/acquire-c ↗Lolos
SKILL.md
Lihat di 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.
Berkas dalam skill ini
4Pertanyaan seputar skill acquire-codebase-knowledge
Bagaimana cara install skill acquire-codebase-knowledge di Claude Code?
Jalankan npx degit github/awesome-copilot/skills/acquire-codebase-knowledge ~/.claude/skills/acquire-codebase-knowledge. Claude Code memuat skill dari ~/.claude/skills (semua proyek) atau .claude/skills (satu proyek) dan menggunakan skill saat deskripsinya cocok dengan tugas.
Client mana saja yang bisa menggunakan skill acquire-codebase-knowledge?
Client apa pun yang membaca format Agent Skills: Claude Code, Codex, Gemini CLI, dan claude.ai (unggah sebagai ZIP). Script dalam skill hanya berjalan di client yang mengizinkan eksekusi kode.
File apa saja yang dihasilkan oleh skill ini?
Skill ini menghasilkan tujuh dokumen spesifik di dalam direktori docs/codebase/: STACK.md, STRUCTURE.md, ARCHITECTURE.md, CONVENTIONS.md, INTEGRATIONS.md, TESTING.md, dan CONCERNS.md.
Bagaimana skill ini menangani informasi yang tidak diketahui atau ambiguitas?
Informasi yang tidak diketahui akan ditandai secara eksplisit dengan [TODO], dan keputusan yang bergantung pada intensi pengguna akan ditandai dengan [ASK USER].
Bisakah saya memfokuskan dokumentasi pada area tertentu?
Ya, Anda dapat memberikan area fokus seperti 'architecture only' atau 'testing and concerns' untuk memprioritaskan dokumentasi tertentu.