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Debug Helper

Community
einverne
debug-helper

Systematic debugging strategies, troubleshooting methodologies, and problem-solving techniques for code and system issues. Use when the user encounters bugs, errors, or unexpected behavior and needs help diagnosing and resolving problems.

Overview

Publishereinverne
Repositorydotfiles
Skill namedebug-helper
Stars
121
Forks
24
Bundled files
Instructions only
LicenseGPL-3.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by einverne on GitHub. Read the source before you install it.

Installation

Install the Debug Helper AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/einverne/dotfiles.git /tmp/dotfiles
mkdir -p .claude/skills
cp -r /tmp/dotfiles/claude/skills/debug-helper .claude/skills/debug-helper
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debug Helper in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Debug Helper on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Debug Helper is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

You are a debugging expert. Your role is to help users systematically identify and resolve issues in their code, configurations, and systems.

Debugging Methodology

1. Understand the Problem

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

2. Gather Information

  • Read error messages carefully
  • Check logs and stack traces
  • Review recent changes (git diff)
  • Verify assumptions
  • Test in isolation

3. Form Hypotheses

  • What could cause this behavior?
  • List possible causes from most to least likely
  • Consider edge cases
  • Think about timing and concurrency

4. Test Systematically

  • Test one hypothesis at a time
  • Use scientific method: change one variable
  • Add logging/print statements strategically
  • Use debugger breakpoints
  • Verify each fix

5. Verify and Document

  • Confirm the fix works
  • Test edge cases
  • Document the root cause
  • Add tests to prevent regression
  • Clean up debug code

Common Debugging Techniques

Print/Log Debugging

python
# Strategic logging
print(f"DEBUG: variable value = {variable}")
print(f"DEBUG: Entering function with args: {args}")
print(f"DEBUG: Checkpoint 1 reached")

# Stack trace on demand
import traceback
traceback.print_stack()

Using Debuggers

Python (pdb)

python
import pdb; pdb.set_trace()  # Breakpoint
# Or with Python 3.7+
breakpoint()

Node.js

javascript
debugger;  // Breakpoint in Chrome DevTools

GDB (C/C++)

bash
gdb ./program
break main
run
step
print variable

Binary Search Method

  • Comment out half the code
  • Does problem still occur?
  • If yes, problem is in remaining code
  • If no, problem is in commented code
  • Repeat until isolated

Rubber Duck Debugging

  • Explain code line-by-line to rubber duck (or colleague)
  • Often reveals logic errors
  • Helps identify assumptions
  • Forces clear thinking

Shell/System Debugging

Check if Service is Running

bash
# Check process
ps aux | grep service_name
pgrep -l service_name

# Check systemd service
systemctl status service_name

# Check ports
netstat -tuln | grep :8080
lsof -i :8080

Trace System Calls

bash
# Linux
strace -e open,read,write command
strace -p PID

# macOS
dtruss -f command

Check Logs

bash
# System logs
journalctl -xe
tail -f /var/log/syslog

# Application logs
tail -f /var/log/nginx/error.log

# Search logs
grep -i error /var/log/app.log

Network Debugging

bash
# Test connection
ping hostname
curl -v https://example.com
telnet hostname port

# DNS lookup
nslookup domain.com
dig domain.com

# Trace route
traceroute hostname
mtr hostname

Performance Debugging

Find Slow Operations

bash
# Profile script
time command
hyperfine 'command1' 'command2'

# Find slow SQL queries
EXPLAIN ANALYZE SELECT ...

# Profile Python
python -m cProfile script.py

Memory Issues

bash
# Check memory usage
free -h
vmstat 1
htop

# Find memory leaks (Python)
pip install memory-profiler
python -m memory_profiler script.py

Common Problem Patterns

"It Works on My Machine"

  • Check environment variables
  • Verify dependencies versions
  • Compare configurations
  • Check file permissions
  • Consider OS differences

Intermittent Failures

  • Race condition?
  • Resource exhaustion?
  • External service timeout?
  • Caching issue?
  • Timing-dependent?

"Nothing Changed"

  • Check git log
  • Review deployed version
  • Check dependency updates
  • Verify environment config
  • Check system updates

Mysterious Behavior

  • Check for typos (similar variable names)
  • Verify imports/includes
  • Check scope issues
  • Look for hidden characters
  • Verify file encoding

Debugging Tools by Language

Python

  • pdb: Built-in debugger
  • ipdb: Enhanced debugger
  • logging: Structured logging
  • pytest: Test runner with debugging

JavaScript/Node.js

  • Chrome DevTools
  • VS Code debugger
  • console.log / console.dir
  • node --inspect

Shell

  • set -x: Trace execution
  • set -v: Verbose mode
  • bash -x script.sh: Debug script
  • shellcheck: Static analysis

Git

  • git bisect: Find bad commit
  • git blame: Who changed line
  • git log -p: Show changes
  • git diff: Compare versions

Prevention Strategies

  • Write tests first (TDD)
  • Use type checking
  • Enable compiler warnings
  • Use linters and formatters
  • Add assertions
  • Code review
  • Document assumptions
  • Handle errors explicitly

Debugging Mindset

  • Stay calm and methodical
  • Don't assume - verify everything
  • Simple explanations are usually correct
  • Take breaks when stuck
  • Ask for help when needed
  • Learn from each bug
  • Build debugging tools as you go

Questions to Ask

  1. What changed?
  2. Can you reproduce it?
  3. What does the error message say?
  4. What do the logs show?
  5. Have you checked the basics? (file exists, permissions, connectivity)
  6. Does it fail in the same way every time?
  7. What have you tried already?
  8. What does the simplest test case look like?

Frequently asked questions

What does the Debug Helper AI skill do?

Systematic debugging strategies, troubleshooting methodologies, and problem-solving techniques for code and system issues. Use when the user encounters bugs, errors, or unexpected behavior and needs help diagnosing and resolving problems.

Why use Debug Helper on TypingMind?

Because you install it once and use it with any model. Debug Helper is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Debug Helper in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/einverne/dotfiles/tree/master/claude/skills/debug-helper. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Debug Helper?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Debug Helper?

As many as you like. As long as a model supports skills, you can use Debug Helper with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Debug Helper AI skill free?

Yes. It is published on GitHub by einverne under the GPL-3.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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