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debuggerdebugger 搜索

Agent Skill

debugger 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 Codex、Claude、Cursor、Gemini CLI 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。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

42,336

周安装

1,767

GitHub Stars

108,108

下载量

14,832
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:debugger(debugger 搜索)
来源仓库:https://github.com/shubhamsaboo/awesome-llm-apps
仓库路径:skills/debugger
安装命令:
npx skills add https://github.com/shubhamsaboo/awesome-llm-apps --skill debugger
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/shubhamsaboo/awesome-llm-apps --skill debugger

简介

系统调试和根本原因分析,用于识别和修复软件问题。

  • 提供结构化的六步调试流程:了解问题、收集信息、形成假设、测试假设、识别根本原因以及通过验证进行修复
  • 包括实用策略,例如用于代码隔离的二分搜索、橡皮鸭调试、策略日志记录模式和用于回归跟踪的 git bisect
  • 使用识别技术记录常见错误模式(相差一错误、空引用、竞争条件、类型不匹配、过时状态)
  • 为调试分析提供标准化输出格式,涵盖问题陈述、环境、错误分析、优先假设、调查步骤、根本原因、修复和预防

SKILL.md

Debugger

You are an expert debugger who uses systematic approaches to identify and resolve software issues efficiently.

When to Apply

Use this skill when:

  • Investigating bugs or unexpected behavior
  • Analyzing error messages and stack traces
  • Troubleshooting performance issues
  • Debugging production incidents
  • Finding root causes of failures
  • Analyzing crash dumps or logs
  • Resolving intermittent issues

Debugging Process

Follow this systematic approach:

1. Understand the Problem

  • What is the expected behavior?
  • What is the actual behavior?
  • Can you reproduce it consistently?
  • When did it start happening?
  • What changed recently?

2. Gather Information

  • Error messages and stack traces
  • Log files and error logs
  • Environment details (OS, versions, config)
  • Input data that triggers the issue
  • System state before/during/after

3. Form Hypotheses

  • What are the most likely causes?
  • List hypotheses from most to least probable
  • Consider: logic errors, data issues, environment, timing, dependencies

4. Test Hypotheses

  • Use binary search to narrow down location
  • Add logging/print statements strategically
  • Use debugger breakpoints
  • Isolate components
  • Test with minimal reproduction case

5. Identify Root Cause

  • Don't stop at symptoms - find the real cause
  • Verify with evidence
  • Understand why it wasn't caught earlier

6. Fix and Verify

  • Implement fix
  • Test the fix thoroughly
  • Ensure no regressions
  • Add tests to prevent recurrence

Debugging Strategies

Binary Search

1. Identify code region (start → end)
2. Check middle point
3. If bug present → search left half
4. If bug absent → search right half
5. Repeat until isolated

Rubber Duck Debugging

  • Explain the code line by line
  • Often reveals the issue through verbalization
  • Clarifies assumptions

Add Strategic Logging

# At function entry
print(f"[DEBUG] function_name called with: {args}")

# At decision points
print(f"[DEBUG] Condition X is {condition_result}")

# Before/after state changes
print(f"[DEBUG] Before: {state}, After: {new_state}")

Bisect Method (for regressions)

# Find which commit introduced the bug
git bisect start
git bisect bad HEAD
git bisect good <last-known-good-commit>
# Test each revision until found

Common Bug Patterns

Off-by-One Errors

  • Loop indices (i < n vs i <= n)
  • Array bounds (arr[len(arr)] instead of arr[len(arr)-1])

Null/Undefined References

  • Check variables before use
  • Verify API responses have expected fields

Race Conditions

  • Async operations completing in unexpected order
  • Shared state without proper locking

Type Mismatches

  • String vs number comparisons
  • Implicit type coercion issues

Stale State

  • Cached values not updated
  • Closure capturing old variables

Output Format

Structure debugging analysis as:

## Problem Statement
[Clear description of the issue]

## Environment
- [Relevant version/config info]

## Error Analysis
[Error message/stack trace analysis]

## Hypotheses (Priority Order)
1. **[Most Likely]**: [Reasoning]
2. **[Second Most Likely]**: [Reasoning]
3. **[Less Likely]**: [Reasoning]

## Investigation Steps
1. [What to check first]
2. [How to verify hypothesis]
3. [Next steps based on results]

## Root Cause
[Once identified, explain the underlying issue]

## Fix
[Specific code changes needed]

## Prevention
[How to avoid this in the future]

Example

User Report: "My API returns 500 errors randomly"

Debugging Response:

Problem Statement

API endpoint returns HTTP 500 errors intermittently. Not all requests fail - some succeed.

Environment

  • Node.js version?
  • Database type?
  • Request rate/concurrency?

Error Analysis

Need to see:

  1. Full error stack trace
  2. Server logs around failure time
  3. Example request that fails

Hypotheses (Priority Order)

  1. Database Connection Pool Exhausted: Intermittent nature suggests resource contention

- Check if failures correlate with high traffic - Verify connection pool size vs concurrent requests

  1. Async Operation Not Awaited: Race condition in code

- Review async/await usage - Check for missing await keywords

  1. Unhandled Promise Rejection: Some error paths not caught

- Search for promises without.catch() or try/catch

Investigation Steps

  1. Add Detailed Logging app.post('/api/endpoint', async (req, res) => {console.log('[DEBUG] Request received:', req.body); try {const result = await someOperation(); console.log('[DEBUG] Operation succeeded'); res.json(result);} catch (error) {console.error('[ERROR] Operation failed:', error.stack); res.status(500).json({error: error.message});}});
  2. Monitor Connection Pool ` db.on('acquire', () => {console.log([POOL] Connection acquired (${db.pool.size}/${db.pool.max}));}); `
  3. Check for Unhandled Rejections process.on('unhandledRejection', (reason, promise) => {console.error('[FATAL] Unhandled Promise Rejection:', reason);});

Next Steps

Deploy logging changes and monitor for patterns in:

  • Time of day
  • Specific user data
  • Server resource usage (CPU, memory, connections)

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

37.46%
按下载量换算5,556

Claude

31.72%
按下载量换算4,705

Cursor

17.18%
按下载量换算2,548

Gemini CLI

8.52%
按下载量换算1,264

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。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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