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troubleshoottroubleshoot 搜索

Agent Skill

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。

总安装

490

周安装

20

GitHub Stars

2

下载量

158
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

2

许可证

unknown

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: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

简介

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。

SKILL.md

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

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

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

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

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

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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