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Agent Skill · 研究检索

trace(trace 搜索)

trace 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。

trace 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,权限为执行命令;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 18,503。

总安装 6,048周安装 252GitHub Stars 31,980下载 2,016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:trace(trace 搜索)
来源仓库:https://github.com/yeachan-heo/oh-my-claudecode
仓库路径:skills/trace
安装命令:
npx skills add https://github.com/yeachan-heo/oh-my-claudecode --skill trace
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/yeachan-heo/oh-my-claudecode --skill trace

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。

用于查找、检索和筛选相关信息。trace 属于研究检索类 Skill,可作为该场景下的辅助能力补充。适合根据关键词或任务场景快速定位候选结果。可结合来源仓库和原始 README 核验具体用法。安装前建议确认权限范围和维护状态。

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。注意是否会触发联网、命令执行或文件读写。

来源仓库内路径:skills/trace

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/yeachan-heo/oh-my-claudecode --skill trace;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

    它需要执行命令,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

trace 怎么安装?

在终端执行 npx skills add https://github.com/yeachan-heo/oh-my-claudecode --skill trace。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

trace 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。

trace 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/yeachan-heo/oh-my-claudecode --skill trace 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

trace 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。

trace 用的人多吗?

公开数据是累计安装 6,048 次,近一周安装 252 次,下载 2,016 次,GitHub 31,980 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。

trace 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。
  • 来源仓库有 31,980 星,维护活跃度相对可查。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 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

  • 不能接受 Skill 在你机器上执行命令的人。
  • 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
trace

trace 搜索 · 本页

6,04831,980执行命令未明确通过

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

Trace Skill

Use this skill for ambiguous, causal, evidence-heavy questions where the goal is to explain why an observed result happened, not to jump directly into fixing or rewriting code.

This is the orchestration layer on top of the built-in tracer agent. The goal is to make tracing feel like a reusable OMC operating lane: restate the observation, generate competing explanations, gather evidence in parallel, rank the explanations, and propose the next probe that would collapse uncertainty fastest.

Good entry cases

Use /oh-my-claudecode:trace when the problem is:

  • ambiguous
  • causal
  • evidence-heavy
  • best answered by exploring competing explanations in parallel

Examples:

  • runtime bugs and regressions
  • performance / latency / resource behavior
  • architecture / premortem / postmortem analysis
  • scientific or experimental result tracing
  • config / routing / orchestration behavior explanation
  • “given this output, trace back the likely causes”

Core tracing contract

Always preserve these distinctions:

  1. Observation -- what was actually observed
  2. Hypotheses -- competing explanations
  3. Evidence For -- what supports each explanation
  4. Evidence Against / Gaps -- what contradicts it or is still missing
  5. Current Best Explanation -- the leading explanation right now
  6. Critical Unknown -- the missing fact keeping the top explanations apart
  7. Discriminating Probe -- the highest-value next step to collapse uncertainty

Do not collapse into:

  • a generic fix-it coding loop
  • a generic debugger summary
  • a raw dump of worker output
  • fake certainty when evidence is incomplete

Evidence strength hierarchy

Treat evidence as ranked, not flat.

From strongest to weakest:

  1. Controlled reproductions / direct experiments / uniquely discriminating artifacts
  2. Primary source artifacts with tight provenance (trace events, logs, metrics, benchmark outputs, configs, git history, file:line behavior)
  3. Multiple independent sources converging on the same explanation
  4. Single-source code-path or behavioral inference
  5. Weak circumstantial clues (timing, naming, stack order, resemblance to prior bugs)
  6. Intuition / analogy / speculation

Explicitly down-rank hypotheses that depend mostly on lower tiers when stronger contradictory evidence exists.

Strong falsification / disconfirmation rules

Every serious /trace run must try to falsify its own favorite explanation.

For each top hypothesis:

  • collect evidence for it
  • collect evidence against it
  • state what distinctive prediction it makes
  • state what observation would be hard to reconcile with it
  • identify the cheapest probe that would discriminate it from the next-best alternative

Down-rank a hypothesis when:

  • direct evidence contradicts it
  • it survives only by adding new unverified assumptions
  • it makes no distinctive prediction compared with rivals
  • a stronger alternative explains the same facts with fewer assumptions
  • its support is mostly circumstantial while the rival has stronger evidence tiers

Team-mode orchestration shape

Use Claude built-in team mode for /trace.

The lead should:

  1. Restate the observed result or “why” question precisely
  2. Extract the tracing target
  3. Generate multiple deliberately different candidate hypotheses
  4. Spawn 3 tracer lanes by default in team mode
  5. Assign one tracer worker per lane
  6. Instruct each tracer worker to gather evidence for and against its lane
  7. Run a rebuttal round between the leading hypothesis and the strongest remaining alternative
  8. Detect whether the top lanes genuinely differ or actually converge on the same root cause
  9. Merge findings into a ranked synthesis with an explicit critical unknown and discriminating probe

Important: workers should pursue deliberately different explanations, not the same explanation in parallel.

Default hypothesis lanes for v1

Unless the prompt strongly suggests a better partition, use these 3 default lanes:

  1. Code-path / implementation cause
  2. Config / environment / orchestration cause
  3. Measurement / artifact / assumption mismatch cause

These defaults are intentionally broad so the first slice works across bug, performance, architecture, and experiment tracing.

Mandatory cross-check lenses

After the initial evidence pass, pressure-test the leaders with these lenses when relevant:

  • Systems lens -- queues, retries, backpressure, feedback loops, upstream/downstream dependencies, boundary failures, coordination effects
  • Premortem lens -- assume the current best explanation is incomplete or wrong; what failure mode would embarrass the trace later?
  • Science lens -- controls, confounders, measurement bias, alternative variables, falsifiable predictions

These lenses are not filler. Use them when they can surface a missed explanation, hidden dependency, or weak inference.

Worker contract

Each worker should be a tracer lane owner, not a generic executor.

Each worker must:

  • own exactly one hypothesis lane
  • restate its lane hypothesis explicitly
  • gather evidence for the lane
  • gather evidence against the lane
  • rank the evidence strength behind its case
  • call out missing evidence, failed predictions, and remaining uncertainty
  • name the critical unknown for the lane
  • recommend the best lane-specific discriminating probe
  • avoid collapsing into implementation unless explicitly told to do so

Useful evidence sources include:

  • relevant code, tests, configs, docs, logs, outputs, and benchmark artifacts
  • existing trace artifacts via trace_timeline
  • existing aggregate trace evidence via trace_summary

Recommended worker return structure:

  1. Lane
  2. Hypothesis
  3. Evidence For
  4. Evidence Against / Gaps
  5. Evidence Strength
  6. Critical Unknown
  7. Best Discriminating Probe
  8. Confidence

Leader synthesis contract

The final /trace answer should synthesize, not just concatenate.

Return:

  1. Observed Result
  2. Ranked Hypotheses
  3. Evidence Summary by Hypothesis
  4. Evidence Against / Missing Evidence
  5. Rebuttal Round
  6. Convergence / Separation Notes
  7. Most Likely Explanation
  8. Critical Unknown
  9. Recommended Discriminating Probe
  10. Additional Trace Lanes (optional, only if uncertainty remains high)

Preserve a ranked shortlist even if one explanation is currently dominant.

Rebuttal round and convergence detection

Before closing the trace:

  • let the strongest non-leading lane present its best rebuttal to the current leader
  • force the leader to answer the rebuttal with evidence, not assertion
  • if the rebuttal materially weakens the leader, re-rank the table
  • if two “different” hypotheses reduce to the same underlying mechanism, merge them and say so explicitly
  • if two hypotheses still imply different next probes, keep them separate even if they sound similar

Do not claim convergence just because multiple workers use similar language. Convergence requires either:

  • the same root causal mechanism, or
  • independent evidence streams pointing to the same explanation

Explicit down-ranking guidance

The lead should explicitly say why a hypothesis moved down:

  • contradicted by stronger evidence
  • lacks the observation it predicted
  • requires extra ad hoc assumptions
  • explains fewer facts than the leader
  • lost the rebuttal round
  • converged into a stronger parent explanation

This is important because /trace should teach the reader why one explanation outranks another, not just present a final table.

Suggested lead prompt skeleton

Use a team-oriented orchestration prompt along these lines:

  1. “Restate the observation exactly.”
  2. “Generate 3 deliberately different hypotheses.”
  3. “Create one tracer lane per hypothesis using Claude built-in team mode.”
  4. “For each lane, gather evidence for and against, rank evidence strength, and name the critical unknown plus best discriminating probe.”
  5. “Apply systems, premortem, and science lenses to the leaders if useful.”
  6. “Run a rebuttal round between the top two explanations.”
  7. “Return a ranked explanation table, convergence notes, the critical unknown, and the single best discriminating probe.”

Output quality bar

Good /trace output is:

  • evidence-backed
  • concise but rigorous
  • skeptical of premature certainty
  • explicit about missing evidence
  • practical about the next action
  • explicit about why weaker explanations were down-ranked

Example final synthesis shape

Observed Result

[What happened]

Ranked Hypotheses

RankHypothesisConfidenceEvidence StrengthWhy it leads
1...High / Medium / LowStrong / Moderate / Weak...

Evidence Summary by Hypothesis

  • Hypothesis 1:...
  • Hypothesis 2:...
  • Hypothesis 3:...

Evidence Against / Missing Evidence

  • Hypothesis 1:...
  • Hypothesis 2:...
  • Hypothesis 3:...

Rebuttal Round

  • Best rebuttal to leader:...
  • Why leader held / failed:...

Convergence / Separation Notes

  • ...

Most Likely Explanation

[Current best explanation]

Critical Unknown

[Single missing fact keeping uncertainty open]

Recommended Discriminating Probe

[Single next probe]

Additional Trace Lanes

[Only if uncertainty remains high]

平台分布

Codex

33.98%
按下载量换算685

Claude

27.84%
按下载量换算561

Cursor

19.39%
按下载量换算391

Gemini CLI

9.43%
按下载量换算190

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/yeachan-heo/oh-my-claudecode --skill trace 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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