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Agent Skill · 研究检索

codebase-recon(代码库侦察)

codebase-recon 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-recon 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,权限为只读;许可证为 MIT,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 35,529。

总安装 2,070周安装 88GitHub Stars 26下载 725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
MIT

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-recon(代码库侦察)
来源仓库:https://github.com/outfitter-dev/agents
仓库路径:skills/codebase-recon
安装命令:
npx skills add https://github.com/outfitter-dev/agents --skill codebase-recon
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/outfitter-dev/agents --skill codebase-recon

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-recon 基于证据的调查方法,从多源提取信息并形成置信度标注的结论。流程包含证据收集、模式加载与分级报告三个阶段,确保推理可追溯。适用于架构分析、模式识别与根因调查等专业研判任务。自动衔接 outfitter:patterns 与 outfitter:report-findings 等下游技能。

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-recon

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/outfitter-dev/agents --skill codebase-recon;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

codebase-recon 怎么安装?

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

codebase-recon 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-recon 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

codebase-recon 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

codebase-recon 用的人多吗?

公开数据是累计安装 2,070 次,近一周安装 88 次,下载 725 次,GitHub 26 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-recon 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 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-recon

代码库侦察 · 本页

2,07026只读MIT有提醒

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 Analysis

Evidence-based investigation → findings → confidence-tracked conclusions.

Steps

  1. Gather evidence from multiple sources (code, docs, tests, history)
  2. Track confidence level as investigation progresses
  3. Based on findings:

- If pattern analysis needed → load the outfitter:patterns skill - If root cause investigation → load the outfitter:find-root-causes skill - If ready to report → load the outfitter:report-findings skill

  1. Deliver findings with confidence level and caveats

<when_to_use>

  • Codebase exploration and understanding
  • Architecture analysis and mapping
  • Pattern extraction and recognition
  • Technical research within code
  • Performance or security analysis

NOT for: wild guessing, assumptions without evidence, conclusions before investigation

</when_to_use>

BarLvlNameAction
░░░░░0GatheringCollect initial evidence
▓░░░░1SurveyingBroad scan, surface patterns
▓▓░░░2InvestigatingDeep dive, verify patterns
▓▓▓░░3AnalyzingCross-reference, fill gaps
▓▓▓▓░4SynthesizingConnect findings, high confidence
▓▓▓▓▓5ConcludedDeliver findings

*Calibration: 0=0–19%, 1=20–39%, 2=40–59%, 3=60–74%, 4=75–89%, 5=90–100%*

Start honest. Clear codebase + focused question → level 2–3. Vague or complex → level 0–1.

At level 4: "High confidence in findings. One more angle would reach full certainty. Continue or deliver now?"

Below level 5: include △ Caveats section.

Core Methodology

Evidence over assumption — investigate when you can, guess only when you must.

Multi-source gathering — code, docs, tests, history, web research, runtime behavior.

Multiple angles — examine from different perspectives before concluding.

Document gaps — flag uncertainty with △, track what's unknown.

Show your work — findings include supporting evidence, not just conclusions.

Calibrate confidence — distinguish fact from inference from assumption.

<evidence_gathering>

Source Priority

  1. Direct observation — read code, run searches, examine files
  2. Documentation — official docs, inline comments, ADRs
  3. Tests — reveal intended behavior and edge cases
  4. History — git log, commit messages, PR discussions
  5. External research — library docs, Stack Overflow, RFCs
  6. Inference — logical deduction from available evidence
  7. Assumption — clearly flagged when other sources unavailable

Investigation Patterns

Start broad, then narrow:

  • File tree → identify relevant areas
  • Search patterns → locate specific code
  • Code structure → understand without full content
  • Read targeted files → examine implementation
  • Cross-reference → verify understanding

Layer evidence:

  • What does the code do? (direct observation)
  • Why was it written this way? (history, comments)
  • How does it fit the system? (architecture, dependencies)
  • What are the edge cases? (tests, error handling)

Follow the trail:

  • Function calls → trace execution paths
  • Imports/exports → map dependencies
  • Test files → understand usage patterns
  • Error messages → reveal assumptions
  • Comments → capture historical context

</evidence_gathering>

<output_format>

During Investigation

After each evidence-gathering step emit:

  • Confidence: {BAR} {NAME}
  • Found: {key discoveries}
  • Patterns: {emerging themes}
  • Gaps: {what's still unclear}
  • Next: {investigation direction}

At Delivery (Level 5)

Findings

{numbered list of discoveries with supporting evidence}

  1. {FINDING} — evidence: {SOURCE}
  2. {FINDING} — evidence: {SOURCE}

Patterns

{recurring themes or structures identified}

Implications

{what findings mean for the question at hand}

Confidence Assessment

Overall: {BAR} {PERCENTAGE}%

High confidence areas:

  • {AREA} — {REASON}

Lower confidence areas:

  • {AREA} — {REASON}

Supporting Evidence

  • Code: {file paths and line ranges}
  • Docs: {references}
  • Tests: {relevant test files}
  • History: {commit SHAs if relevant}
  • External: {URLs if applicable}

Below Level 5

△ Caveats

Assumptions:

  • {ASSUMPTION} — {why necessary, impact if wrong}

Gaps:

  • {GAP} — {what's missing, how to fill}

Unknowns:

  • {UNKNOWN} — {noted for future investigation}

</output_format>

<specialized_techniques>

Load skills for specialized analysis (see Steps section):

  • Pattern analysis → outfitter:patterns
  • Root cause investigation → outfitter:find-root-causes
  • Research synthesis → outfitter:report-findings
  • Architecture analysis → see architecture-analysis.md

</specialized_techniques>

Loop: Gather → Analyze → Update Confidence → Next step

  1. Calibrate starting confidence — what do we already know?
  2. Identify evidence sources — where can we look?
  3. Gather systematically — collect from multiple angles
  4. Cross-reference findings — verify patterns hold
  5. Flag uncertainties — mark gaps with △
  6. Synthesize conclusions — connect evidence to insights
  7. Deliver with confidence level — clear about certainty

At each step:

  • Document what you found (evidence)
  • Note what it means (interpretation)
  • Track what's still unclear (gaps)
  • Update confidence bar

Before concluding (level 4+):

Check evidence quality:

  • ✓ Multiple sources confirm pattern?
  • ✓ Direct observation vs inference clearly marked?
  • ✓ Assumptions explicitly flagged?
  • ✓ Counter-examples considered?

Check completeness:

  • ✓ Original question fully addressed?
  • ✓ Edge cases explored?
  • ✓ Alternative explanations ruled out?
  • ✓ Known unknowns documented?

Check deliverable:

  • ✓ Findings supported by evidence?
  • ✓ Confidence calibrated honestly?
  • ✓ Caveats section included if <100%?
  • ✓ Next steps clear if incomplete?

ALWAYS:

  • Investigate before concluding
  • Cite evidence sources with file paths/URLs
  • Use confidence bars to track certainty
  • Flag assumptions and gaps with △
  • Cross-reference from multiple angles
  • Document investigation trail
  • Distinguish fact from inference
  • Include caveats below level 5

NEVER:

  • Guess when you can investigate
  • State assumptions as facts
  • Conclude from single source
  • Hide uncertainty or gaps
  • Skip validation checks
  • Deliver without confidence assessment
  • Conflate evidence with interpretation

Core methodology:

  • confidence.md — confidence calibration (shared with pathfinding)

Micro-skills (load as needed):

  • outfitter:patterns — extracting and validating patterns
  • outfitter:find-root-causes — systematic problem diagnosis
  • outfitter:report-findings — multi-source research synthesis

Local references:

Related skills:

  • outfitter:pathfinding — clarifying requirements before analysis
  • outfitter:debugging — structured bug investigation

平台分布

Claude Code

33.45%
按下载量换算243

OpenCode

22.68%
按下载量换算164

Cursor

16.73%
按下载量换算121

github-copilot

12.26%
按下载量换算89

Codex

8.59%
按下载量换算62

Gemini CLI

3.46%
按下载量换算25

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。

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