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codebase-recon代码库侦察

Agent Skill

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。

总安装

2,070

周安装

88

GitHub Stars

26

下载量

725
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

3

许可证

MIT

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-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

简介

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。

SKILL.md

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 analysisoutfitter:patterns
  • Root cause investigationoutfitter:find-root-causes
  • Research synthesisoutfitter: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

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

能力 5

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

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

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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

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来源信息

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