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Agent Skill · 研究检索

codebase-scanning(代码库扫描)

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。

codebase-scanning 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 71,596。

总安装 194周安装 8GitHub Stars 80下载 63核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

来源仓库内路径:skills/codebase-scanning

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/slgoodrich/agents --skill codebase-scanning;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

codebase-scanning 怎么安装?

在终端执行 npx skills add https://github.com/slgoodrich/agents --skill codebase-scanning。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

codebase-scanning 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

codebase-scanning 需要什么权限?

权限级别是需要联网。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。

codebase-scanning 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

codebase-scanning 用的人多吗?

公开数据是累计安装 194 次,近一周安装 8 次,下载 63 次,GitHub 80 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

codebase-scanning 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
codebase-scanning

代码库扫描 · 本页

19480需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

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。目前只有一个公开来源,建议同时核对源仓库。

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