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acquire-codebase-knowledge获取代码库知识

Agent Skill

acquire-codebase-knowledge 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

10,584

周安装

441

GitHub Stars

31,714

下载量

3,528
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:acquire-codebase-knowledge(获取代码库知识)
来源仓库:https://github.com/github/awesome-copilot
仓库路径:skills/acquire-codebase-knowledge
安装命令:
npx skills add https://github.com/github/awesome-copilot --skill acquire-codebase-knowledge
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/github/awesome-copilot --skill acquire-codebase-knowledge

简介

该技能用于从代码库中提取结构化知识,生成项目所需的七份文档。

  • 适用于需要快速理解项目架构、依赖关系和开发规范的场景。
  • 通过分析文件系统和终端输出生成文档,输出前会列出需用户确认的问题。
  • 安装时需确认权限范围,避免触发不必要的网络或文件操作。
  • 建议核对来源仓库维护状态,确保技能行为符合预期用途。

SKILL.md

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.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.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

36.74%
按下载量换算1,296

Claude

30.48%
按下载量换算1,075

Cursor

20.59%
按下载量换算726

Gemini CLI

9.31%
按下载量换算328

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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