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codebase-scanning代码库扫描

Agent Skill

codebase-scanning 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

194

周安装

8

GitHub Stars

80

下载量

63
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:codebase-scanning(代码库扫描)
来源仓库:https://github.com/slgoodrich/agents
仓库路径:skills/codebase-scanning
安装命令:
npx skills add https://github.com/slgoodrich/agents --skill codebase-scanning
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/slgoodrich/agents --skill codebase-scanning

简介

codebase-scanning 自动识别平台类型与技术栈,解析路由与组件以发现产品特性。

  • 覆盖 Web 与移动端,支持 manifest 驱动的框架识别与数据库推断。
  • 输出包含功能列表与技术指标的标准化扫描报告,支撑 PM 决策。
  • 依赖平台特定检测逻辑,对 unsupported manifest 类型返回有限信息。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Codebase Scanning

Discover strategic product context from existing codebases. Covers web and mobile platforms.

Auto-loaded by the context-scanner agent for all codebase scanning operations. Also loaded when pm-setup bootstraps project context from code.

Scanning Process

  1. Detect platform - Identify web, mobile, or hybrid. For mobile, follow the detection order in references/platform-detection.md
  2. Parse tech stack - Read manifests to identify frameworks, languages, databases. See references/manifest-detection.md for supported manifests per platform
  3. Discover features - Scan routes, pages, and components for user-facing functionality
  4. Map integrations - Match dependencies against known 3rd party services. See references/integration-mapping.md for lookup tables
  5. Estimate scale - Count files, estimate LOC, assign complexity and maturity tiers
  6. Present results - Use assets/presentation-template.md format, structured per assets/output-schema.md

Feature Discovery

Scan routes, pages, and components to identify user-facing functionality.

Methods:

  • Parse route files (Express routes, Next.js pages/, API routes)
  • Analyze page/component names
  • Identify API endpoints from route definitions
  • Map features to evidence (file paths)

Return format per feature:

  • name: Feature name (lowercase, descriptive)
  • confidence: high/medium/low
  • evidence: File paths, routes, or patterns

Common features detected: Authentication, project/task management, team collaboration, analytics/reporting, settings/configuration.

Tech Stack Detection

Parse package manifests and project files to identify technologies. Supports 7 web platforms (Node.js, Python, Go, Ruby, PHP, Rust, Java) and 4 mobile platforms (iOS, Android, Flutter, React Native).

Full manifest tables and per-platform detection rules: references/manifest-detection.md

Integration Discovery

Match package dependencies against known 3rd party services across 9 categories: Payments, Email, SMS, Auth, Cloud, Analytics, Monitoring, Push, Maps/Location.

Full lookup tables and cross-platform dependency mapping: references/integration-mapping.md

Platform Detection

For mobile projects, detect platform type using ordered priority checks (Flutter > React Native > iOS Native > Android Native > Hybrid). Includes feature discovery rules per platform.

Detection order and feature discovery methods: references/platform-detection.md

Scale Estimation

Metrics to collect:

  • Total source files (excluding node_modules, dist, build,.git)
  • Lines of code (estimate: average 25 LOC per KB of source file size, excluding binary and generated files)

Complexity tiers:

TierFilesLOCCharacteristics
Simple<50~10kSingle service
Medium50-50010k-100kFew services
Complex>500100k+Many services/repos

Maturity signals:

StageLOCIndicators
Prototype~5kRapid changes
MVP5k-25kCore features present
Established25k-100kFeature-complete
Mature100k+Extensive feature set

Confidence Assignment

  • High: Direct evidence from routes, manifest versions, clear file patterns
  • Medium: Package present but usage not verified; ambiguous naming patterns
  • Low: Indirect evidence, weak signals, potentially deprecated

Package installed does not mean actively used. Assign medium confidence to integrations by default.

Edge Cases & Limitations

Large Codebases (>100k LOC, >1000 files)

  • Limit scanning to primary directories: src/, pages/, routes/, api/, lib/
  • Skip: node_modules/, dist/, build/,.git/, vendor/, test/ (unless small)
  • Sample large directories (first 100 files, warn about remaining)
  • Maximum 60 seconds total scan time
  • Graceful degradation: return partial results with limitation note

Monorepos

Detection: Check for lerna.json, nx.json, turbo.json, pnpm-workspace.yaml. Identify workspace structure (packages/, apps/).

Handling: Scan each workspace individually. Detect mobile + web combinations. Report tech stack per app with shared packages noted.

Hybrid Mobile Apps

  • React Native with custom native modules: ios/ and android/ with custom.swift or.kt files beyond standard RN setup
  • Flutter with platform channels: ios/Runner/ or android/app/ with custom native code
  • Capacitor/Ionic: capacitor.config.json present

Conflicting Manifests

  • If multiple manifests disagree (e.g., package.json has both react-native and expo), report both with evidence and let the user resolve
  • Prefer the more specific indicator (expo in app.json overrides generic react-native in package.json)
  • For monorepos with mixed platforms, report per-app rather than trying to unify

Other Edge Cases

  • Permission issues: Log warning, continue with accessible files, report in scan_limitations
  • Ambiguous patterns: Assign lower confidence, provide evidence, let user decide. Better to under-report than hallucinate
  • Deprecated code: Report what exists (facts), flag as medium/low confidence if evidence is weak
  • Multi-language projects: Scan all tech stacks present, report separately
  • Empty projects: Return empty findings, note "Project appears empty or very early stage". pm-setup falls back to manual questions

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

36.27%
按下载量换算23

Claude

28.99%
按下载量换算18

Cursor

15.99%
按下载量换算10

Gemini CLI

9.06%
按下载量换算6

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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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