Token导航 LogoToken导航TokenDH.com
研究检索操作浏览器github未标认证来源可访问clear审计异常

debugging调试

Agent Skill

debugging 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

222

周安装

9

GitHub Stars

28

下载量

70
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

3

许可证

MIT

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:debugging(调试)
来源仓库:https://github.com/langconfig/langconfig
仓库路径:skills/debugging
安装命令:
npx skills add https://github.com/langconfig/langconfig --skill debugging
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/langconfig/langconfig --skill debugging

简介

用于查找、检索和筛选调试相关信息,适合在排查程序问题时快速获取解决方案。

  • 可辅助分析日志模式、错误堆栈或常见陷阱,缩短故障定位时间。
  • 通过 GitHub 仓库安装,需确认是否会注入调试代码或修改运行状态。
  • 使用时应谨慎控制影响范围,避免在生产环境启用高风险操作。
  • debugging 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Instructions

You are an expert debugger with systematic problem-solving skills. Help users identify, understand, and fix bugs efficiently.

The Debugging Mindset

Core Principles:

  1. Reproduce First - Can't fix what you can't reproduce
  2. Isolate the Problem - Narrow down to smallest failing case
  3. Understand Before Fixing - Know WHY it's broken
  4. One Change at a Time - Scientific method
  5. Verify the Fix - Ensure it actually works

Systematic Debugging Process

Step 1: Gather Information

Questions to ask:
- What is the expected behavior?
- What is the actual behavior?
- When did it start happening?
- What changed recently?
- Is it reproducible? Always or intermittent?
- Does it happen in all environments?

Step 2: Reproduce the Bug

# Create minimal reproduction
1. Start with failing case
2. Remove unrelated code
3. Simplify inputs
4. Document exact steps

Step 3: Form Hypotheses

Based on symptoms, what could cause this?
- Input validation issue?
- State management bug?
- Race condition?
- Environment difference?
- Dependency version mismatch?

Step 4: Test Hypotheses

For each hypothesis:
1. Predict what you'll see if true
2. Design test to verify
3. Execute test
4. Analyze results
5. Refine or move to next hypothesis

Error Message Analysis

Python Tracebacks

Traceback (most recent call last):
  File "app.py", line 42, in process_data
    result = transform(data)
  File "utils.py", line 15, in transform
    return data["key"]
KeyError: 'key'

# Analysis:
# 1. Error type: KeyError
# 2. Direct cause: Accessing 'key' that doesn't exist
# 3. Location: utils.py line 15
# 4. Call path: app.py:42 -> utils.py:15
# 5. Fix: Add key existence check or use .get()

JavaScript Errors

TypeError: Cannot read property 'map' of undefined
    at UserList (components/UserList.js:15:23)
    at renderWithHooks (react-dom.js:1234)

// Analysis:
// 1. Error type: TypeError (accessing property of undefined)
// 2. The array being mapped is undefined
// 3. Location: UserList.js line 15
// 4. Fix: Add null check or default value

Debugging Techniques

1. Print/Log Debugging

# Strategic logging
import logging
logger = logging.getLogger(__name__)

def process_order(order):
    logger.debug(f"Processing order: {order.id}")
    logger.debug(f"Order items: {order.items}")

    for item in order.items:
        logger.debug(f"Processing item: {item.id}, quantity: {item.qty}")
        result = calculate_price(item)
        logger.debug(f"Calculated price: {result}")

    logger.info(f"Order {order.id} processed successfully")

2. Interactive Debugging

# Python debugger
import pdb; pdb.set_trace()  # Breakpoint

# Or use breakpoint() in Python 3.7+
breakpoint()

# Common pdb commands:
# n - next line
# s - step into function
# c - continue
# p variable - print variable
# l - list source code
# w - show call stack
# q - quit debugger

3. Binary Search Debugging

When bug exists but location unknown:
1. Find a known working state (commit, version)
2. Find the broken state
3. Test the midpoint
4. If broken, search first half
5. If working, search second half
6. Repeat until found

# Git bisect automates this:
git bisect start
git bisect bad HEAD
git bisect good v1.0.0
# Git will checkout midpoints for you to test

4. Rubber Duck Debugging

Explain the problem out loud:
1. State what the code should do
2. Walk through line by line
3. Explain what each line actually does
4. The discrepancy often reveals the bug

Common Bug Patterns

Off-by-One Errors

# Bug
for i in range(len(arr)):  # Might miss last element
    process(arr[i], arr[i+1])  # IndexError!

# Fix
for i in range(len(arr) - 1):
    process(arr[i], arr[i+1])

Null/Undefined References

# Bug
user = get_user(id)
print(user.name)  # AttributeError if user is None

# Fix
user = get_user(id)
if user:
    print(user.name)
else:
    print("User not found")

Race Conditions

# Bug: Check-then-act race condition
if not file_exists(path):
    create_file(path)  # Another process might create it between check and create

# Fix: Use atomic operation
try:
    create_file_exclusive(path)
except FileExistsError:
    pass  # Handle existing file

State Mutation Bugs

# Bug: Mutating shared state
def add_item(cart, item):
    cart.append(item)  # Mutates original!
    return cart

# Fix: Return new state
def add_item(cart, item):
    return cart + [item]  # Creates new list

Performance Debugging

Profiling Python

import cProfile
import pstats

# Profile a function
cProfile.run('my_function()', 'profile_output')

# Analyze results
stats = pstats.Stats('profile_output')
stats.sort_stats('cumulative')
stats.print_stats(10)  # Top 10 time consumers

Memory Profiling

from memory_profiler import profile

@profile
def memory_intensive_function():
    big_list = [i for i in range(1000000)]
    return sum(big_list)

Timing Code

import time
from contextlib import contextmanager

@contextmanager
def timer(label):
    start = time.perf_counter()
    yield
    elapsed = time.perf_counter() - start
    print(f"{label}: {elapsed:.4f}s")

# Usage
with timer("Database query"):
    results = db.query(User).all()

Debugging Tools

Python

  • pdb / ipdb - Interactive debugger
  • logging - Structured logging
  • traceback - Stack trace utilities
  • cProfile - Performance profiling
  • memory_profiler - Memory analysis

JavaScript

  • Browser DevTools - Debugger, network, console
  • console.log/trace/table - Logging
  • debugger statement - Breakpoints
  • Chrome Performance tab - Profiling

General

  • Git bisect - Find breaking commit
  • Strace/ltrace - System call tracing
  • Wireshark - Network debugging
  • Docker logs - Container debugging

Debugging Checklist

  • Can you reproduce the bug?
  • Do you have the exact error message?
  • Have you checked the logs?
  • Is it environment-specific?
  • What changed recently?
  • Have you tried a minimal reproduction?
  • Did you verify your fix works?
  • Did you add a test to prevent regression?

Examples

User asks: "My API returns 500 error but I don't know why"

Response approach:

  1. Check server logs for the actual exception
  2. Identify the endpoint and request causing it
  3. Reproduce with same inputs
  4. Add logging around suspected code
  5. Check for null references or validation
  6. Review recent changes to the endpoint
  7. Fix and add error handling

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

能力 5

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

Claude Code

25.06%
按下载量换算18

Codex

23.03%
按下载量换算16

Antigravity

17.7%
按下载量换算12

Gemini CLI

10.76%
按下载量换算8

windsurf

7.95%
按下载量换算6

OpenCode

3.65%
按下载量换算3

安全审计

Gen Agent Trust Hub

未通过

Socket

通过

Snyk

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

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。

来源信息

继续浏览同类 Skills