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Agent Skill · 研究检索

troubleshoot(troubleshoot 搜索)

troubleshoot 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。

troubleshoot 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,权限为接触敏感数据;许可证信息还不明确,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 55,093。

总安装 490周安装 20GitHub Stars 2下载 158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:troubleshoot(troubleshoot 搜索)
来源仓库:https://github.com/laststance/skills
仓库路径:skills/troubleshoot
安装命令:
npx skills add https://github.com/laststance/skills --skill troubleshoot
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/laststance/skills --skill troubleshoot

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。

troubleshoot 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。适用于问题诊断、故障排除等技术支持场景。通过关键词输入触发搜索,返回结构化结果列表供进一步处理。安装前需确认权限范围和维护状态,注意可能涉及联网和数据读取操作。

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/troubleshoot

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/laststance/skills --skill troubleshoot;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

    它需要接触敏感数据,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

troubleshoot 怎么安装?

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

troubleshoot 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。

troubleshoot 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

troubleshoot 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。

troubleshoot 用的人多吗?

公开数据是累计安装 490 次,近一周安装 20 次,下载 158 次,GitHub 2 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。

troubleshoot 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。

先想清楚的地方

  • 需要接触敏感数据,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。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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
troubleshoot

troubleshoot 搜索 · 本页

4902接触敏感数据未明确有提醒

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

Troubleshoot — Hypothesis-Driven Debugging & Fix

Systematic issue diagnosis with root cause tracing, validated fixes, and prevention guidance.

<essential_principles>

Serena Think Checkpoints (Mandatory)

Call the following Serena tools at the specified points. Never skip them.

CheckpointToolWhenPurpose
Adherence Gatemcp__serena__think_about_task_adherenceBefore each code edit in Phase 4 (Fix)Confirm fix aligns with identified root cause
Completion Gatemcp__serena__think_about_whether_you_are_doneBefore exiting Phase 5 (Verify)Confirm fix is verified with evidence

After Phase 1 and again after Phase 2, do a short evidence sufficiency self-check in prose (repro steps, observed vs expected, hypotheses tied to concrete evidence). If gaps remain, collect more evidence before continuing—do not rely on a removed Serena tool.

Always Active

  • Hypothesis before fix: Never guess-fix. Always form a hypothesis, gather evidence, then apply the fix. "🤔 I think X because Y" → verify → fix
  • Introspection markers: Make debugging reasoning visible throughout:

- 🤔 Reasoning — "🤔 The stack trace points to a null ref in..." - 🎯 Decision — "🎯 Root cause identified: missing null check at..." - ⚡ Performance — "⚡ This N+1 query causes the slowdown" - 📊 Quality — "📊 This fix also addresses the underlying design issue" - 💡 Insight — "💡 This pattern is error-prone; consider refactoring"

  • Validate every fix: Run lint/typecheck/test after each change. No unverified fixes
  • Destructive changes require confirmation: Deleting files, resetting state, dropping data
  • No project-specific rules: This skill works across all projects and AI agents

</essential_principles>

Phase 1: Reproduce

Understand and reproduce the issue before diagnosing.

  1. Parse the error description from user input
  2. 🤔 Identify the error type: bug / build / test / performance / deployment
  3. Collect evidence:

- Read error messages, stack traces, logs - Run the failing command to see the actual output - Check git diff or git log for recent changes that may have caused it

  1. Confirm reproduction: "I can reproduce this by running X → error Y"

If cannot reproduce: Ask user for more context before proceeding.

Tools: Bash, Read, Grep, Glob

  1. Reproduction evidence self-check — Confirm you have enough to proceed: exact command or steps, full error output, and environment hints if relevant. If reproduction is shaky or incomplete, gather more before Phase 2.

Phase 2: Hypothesize

Form hypotheses about the root cause — do not jump to fixing.

  1. 🤔 List 2-3 candidate hypotheses based on evidence: 🤔 Hypothesis A: Missing dependency after package update 🤔 Hypothesis B: Type mismatch from recent refactor 🤔 Hypothesis C: Environment variable not set
  2. 🎯 Rank by likelihood based on evidence strength
  3. Start investigating the most likely hypothesis first
  4. Hypothesis grounding self-check — Each hypothesis should cite specific evidence (log line, code path, diff, measured behavior). Demote or drop guesses that are not tied to facts.

Phase 3: Investigate

Systematically verify or eliminate each hypothesis.

  1. Read the relevant source code (trace from error location outward)
  2. Follow the call chain: caller → function → dependencies
  3. Check external library behavior with Context7 if the issue involves a framework/library
  4. Narrow down to the root cause with evidence: 🎯 Root cause: X confirmed. Evidence: [specific line/behavior]

Tools: Read, Grep, Glob, mcp__serena__find_symbol, mcp__context7__query-docs

Phase 4: Fix

Apply the fix with adherence checks before each edit.

For each code change:

  1. 🔶 think_about_task_adherence — Is this edit aligned with the identified root cause?
  2. 📊 Describe the fix approach before editing: 📊 Fix: Change X to Y in file:line because [reason]
  3. Apply the minimal fix (don't refactor unrelated code)
  4. If fix requires destructive changes → confirm with user first

Tools: Edit, Write, Bash

Phase 5: Verify

Prove the fix works with concrete evidence. No fix is complete without verification.

Standard Verification (always)

  1. Re-run the reproduction: Execute the same command/action from Phase 1

- Confirm the error no longer occurs - Record the output as evidence

  1. Quality checks — run in parallel where possible: pnpm lint & pnpm typecheck & pnpm test & wait
  2. Evidence collection — at least one of:

- Console/log output showing the fix works - Test results (new or existing tests passing) - Screenshot of corrected behavior - User confirmation request ("Can you verify X now works?")

If any check fails → return to Phase 3 with new evidence.

--frontend-verify (when flag is provided)

Visual verification across platforms. Auto-detect platform from package.json:

DependencyPlatformPreflightVerification Tool
*(default)*Webkill-port <port> && pnpm devagent-browser (open --headed, snapshot -i, screenshot)
electronElectronpnpm electron:dev/electron skill (agent-browser based Electron operation)
expo / react-nativeMobilemcp__ios-simulator__open_simulatoriOS Simulator MCP (screenshot, ui_tap, ui_swipe)
commander / inquirer / oclifCLIshell sessionShellwright MCP (TUI/CLI operation and output verification)

Frontend Verify Workflow:

  1. Preflight: Start dev server / app, confirm MCP connection
  2. Before screenshot: Take screenshot of the broken state (if reproducible in UI)
  3. After fix screenshot: Take screenshot of the corrected state
  4. Compare: Present before/after to user for confirmation
  5. Judge: All pass → continue. Any fail → return to Phase 4

Authentication for Frontend Verify

When verifying authenticated apps (SaaS dashboards, admin panels, OAuth-protected pages), use agent-browser's auth persistence:

StrategyCommandUse Case
state save/loadagent-browser state save auth.jsonSession cookies + localStorage. Best for most web apps
--profile <dir>agent-browser open <url> --profile./browser-dataFull Chromium user data dir. Best for complex OAuth (Google, GitHub SSO)
auth saveagent-browser auth save <name> --url <login-url>Encrypted credential store. Best for CI/shared environments

OAuth Flow:

  1. agent-browser open <login-url> --headed (must be headed for OAuth redirects)
  2. Complete OAuth manually or via snapshot -i + fill + click
  3. agent-browser state save auth.json to persist session
  4. Future runs: agent-browser state load auth.json before navigating to app

Security:

  • Add auth.json, browser-data/ to .gitignore
  • auth save uses AES-256-GCM encryption via AGENT_BROWSER_ENCRYPTION_KEY env var
  • State files auto-expire after 30 days
  • Use --headed for initial OAuth setup (redirects require visible browser)

Completion Gate

🔶 think_about_whether_you_are_done — Is the fix verified with evidence?

Phase 6: Report

Summarize findings for the user.

  1. Root Cause: What was wrong and why
  2. Fix Applied: What was changed (files, lines)
  3. Evidence: Verification results (logs, screenshots, test output)
  4. Prevention: 💡 How to avoid this in the future (optional, only if insightful)

Examples

/troubleshoot "TypeError: Cannot read properties of undefined"
/troubleshoot build is failing after upgrading React
/troubleshoot tests pass locally but fail in CI --frontend-verify
/troubleshoot API response time doubled since last deploy

Phase Flow Diagram

[Reproduce] → 🔶 info gate
     ↓
[Hypothesize] → 🔶 info gate
     ↓
[Investigate]
     ↓
   [Fix] → 🔶 adherence gate (per edit)
     ↓
 [Verify] → 🔶 completion gate
     ↓
 [Report]

Boundaries

Will:

  • Systematically trace root causes with evidence-based reasoning
  • Apply minimal, validated fixes with hypothesis-driven debugging
  • Verify fixes with concrete evidence (logs, screenshots, test results)
  • Explain the debugging process transparently with introspection markers

Will Not:

  • Apply fixes without understanding the root cause
  • Make speculative changes hoping something works
  • Mark a fix as complete without verification evidence
  • Modify production systems without explicit user confirmation

平台分布

Codex

38.66%
按下载量换算61

Claude

28.83%
按下载量换算46

Cursor

20.14%
按下载量换算32

Gemini CLI

9.17%
按下载量换算14

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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。目前只有一个公开来源,建议同时核对源仓库。

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