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Agent Skill · 研究检索

investigate(调查)

investigate 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。

investigate 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 53,729。

总安装 524周安装 21GitHub Stars 292下载 170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:investigate(调查)
来源仓库:https://github.com/tobihagemann/turbo
仓库路径:skills/investigate
安装命令:
npx skills add https://github.com/tobihagemann/turbo --skill investigate
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/tobihagemann/turbo --skill investigate

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。

investigate 用于查找、检索和筛选相关信息。适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。通过 npx skills add 命令从指定 GitHub 仓库安装使用。

安装前需确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。建议结合原始 README 核验具体用法和功能边界。

来源仓库内路径:skills/investigate

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/tobihagemann/turbo --skill investigate;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

investigate 怎么安装?

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

investigate 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。

investigate 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/tobihagemann/turbo --skill investigate 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。

investigate 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。

investigate 用的人多吗?

公开数据是累计安装 524 次,近一周安装 21 次,下载 170 次,GitHub 292 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。

investigate 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

  • 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
investigate

调查 · 本页

524292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

Investigate

Systematic methodology for finding the root cause of bugs, failures, and unexpected behavior. Cycle through characterize-isolate-hypothesize-test steps, with oracle escalation for hard problems. Diagnose the root cause — do not apply fixes.

Optional: $ARGUMENTS contains the problem description or error message.

Step 1: Characterize

Gather the symptom and establish what is actually happening:

  1. Collect evidence — error message, stack trace, test output, log entries, or user description of unexpected behavior
  2. Classify the problem type:
SignalType
Stack trace / exceptionRuntime error
Test assertion failureTest failure
Compilation / bundler / build errorBuild failure
Type checker error (tsc, mypy, pyright)Type error
Slow response / high CPU / memory growthPerformance
"It does X instead of Y" / no errorUnexpected behavior
  1. Establish reproduction — run the failing command, test, or operation. If the problem cannot be reproduced (intermittent, environment-specific), document the constraints and proceed with historical evidence.

Record the exact reproduction command and its output for verification. For intermittent or long-running reproductions, use the Monitor tool to tail logs filtered for relevant signals (errors, stack traces, specific identifiers) so failures surface live while you work.

Step 2: Isolate

Narrow from "something is wrong" to "the problem is in this area." Read references/problem-type-playbooks.md for type-specific first moves and tool sequences.

Git Archeology

For all problem types, check what changed recently near the failure point:

git log --oneline -20 -- <file>
git blame -L <start>,<end> <file>

If a known-good state exists (e.g., "this worked yesterday"), consider git bisect to pinpoint the breaking commit.

Scope Narrowing

  • Stack traces: Read the throwing function and its callers — full functions, not just the flagged line
  • Test failures: Read both the test and the system under test
  • Build errors: Read the config file and the referenced source
  • Unexpected behavior: Trace the data flow from input to the unexpected output

Step 3: Hypothesize

Generate 2-4 hypotheses ranked by likelihood. Each hypothesis must be falsifiable — specify what evidence would confirm or refute it.

Format:

H1 (most likely): [description] — confirmed if [X], refuted if [Y]
H2: [description] — confirmed if [X], refuted if [Y]
H3: [description] — confirmed if [X], refuted if [Y]

Parallel Investigation

For complex problems with 3+ hypotheses and a non-obvious root cause, spawn parallel investigators simultaneously.

Spawn condition: 3+ hypotheses AND the problem is not a simple typo, missing import, or syntax error.

Skip when 1-2 hypotheses are obvious (e.g., stack trace points directly to the bug).

Use the Agent tool to launch all agents below in a single assistant message so they run concurrently. Each Agent call uses model: "opus" and does not set run_in_background. Expect (one Agent per hypothesis + one Codex Agent) total. State the count explicitly when emitting the calls.

  • Hypothesis Agent (one per hypothesis): Each receives the hypothesis, relevant file paths, what evidence to look for, and instructions to report confirmed / refuted / inconclusive with evidence. Budget: max 5 tool calls per subagent.
  • Codex Agent: Launch one Agent whose prompt instructs the subagent to invoke /consult-codex via the Skill tool with a focused prompt describing the problem, reproduction, and files examined. The multi-turn conversation allows it to dig deeper into patterns the hypothesis-driven subagents miss. Run the /evaluate-findings skill on its output after the Agent returns.

After all investigators complete, merge results. Codex findings that overlap with a subagent's confirmed hypothesis reinforce confidence. Novel codex findings become additional hypotheses to test in Step 4.

Step 4: Test

Verify each hypothesis with minimal, targeted actions:

Action TypeTool
Find usage or patternGrep
Read surrounding codeRead
Check recent changesBash (git log, git blame, git diff)
Run isolated testBash (specific test command)
Check dependency versionBash (npm ls, pip3 show, etc.)
Inspect runtime stateBash (add temporary logging, run, check output)

Record each result:

HypothesisVerdictEvidence
H1confirmed / refuted / inconclusive[what was found]
H2confirmed / refuted / inconclusive[what was found]

Iteration

If all hypotheses are refuted or inconclusive:

  1. Document what was learned — each refuted hypothesis eliminates a possibility and narrows the search
  2. Return to Step 2 with the new information to re-isolate
  3. Generate new hypotheses in Step 3 based on updated understanding

Cycle budget: maximum 2 full cycles (hypothesize → test → learn → repeat) before escalating.

Escalation

After 2 failed hypothesis cycles, offer escalation to /consult-oracle via AskUserQuestion:

Investigation stalled after [N] hypothesis cycles.

Tested: [summary of hypotheses and evidence]
Remaining unknowns: [what is still unclear]

Escalate to Oracle? (consults external model with full context)

Proceed only if the user approves.

Investigation Report

Output results as text:

Investigation Report:

Problem: [one-line description]
Type: [runtime error | test failure | build failure | type error | performance | unexpected behavior]
Root cause: [confirmed cause, or "unresolved" with best hypothesis]

Evidence:
- [what confirmed the root cause]

Suggested fix: [description of what to change, or "needs further investigation"]
Reproduction command: [command to verify the fix once applied]

Hypotheses tested:
1. [hypothesis] — [confirmed/refuted/inconclusive] — [evidence]
2. [hypothesis] — [confirmed/refuted/inconclusive] — [evidence]

Escalation: [none | oracle]

Then use the TaskList tool and proceed to any remaining task.

Rules

  • If the problem turns out to be environmental (wrong Node version, missing dependency, OS-specific), report that clearly — it may not require a code fix.
  • If the problem is in a dependency (not the project's code), document the dependency issue and suggest workaround options rather than patching the dependency.

平台分布

Codex

37.98%
按下载量换算65

Claude

31.36%
按下载量换算53

Cursor

16.63%
按下载量换算28

Gemini CLI

8.85%
按下载量换算15

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/tobihagemann/turbo --skill investigate 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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