Debugging logo

Debugging

Organization
LangConfig
debugging

Expert guidance for debugging code, analyzing errors, and systematic problem-solving. Use when troubleshooting bugs, understanding error messages, or investigating unexpected behavior.

Overview

PublisherLangConfig
Repositorylangconfig
Skill namedebugging
Stars
69
Forks
19
Bundled files
Instructions only
LicenseMIT
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 LangConfig on GitHub. Read the source before you install it.

Installation

Install the Debugging 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/LangConfig/langconfig.git /tmp/langconfig
mkdir -p .claude/skills
cp -r /tmp/langconfig/backend/skills/builtin/debugging .claude/skills/debugging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debugging 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 Debugging 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 Debugging 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.

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
bash
# 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
python
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
javascript
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
python
# 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
# 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
python
# 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
python
# 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
python
# 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
python
# 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
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
python
from memory_profiler import profile

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

Frequently asked questions

What does the Debugging AI skill do?

Expert guidance for debugging code, analyzing errors, and systematic problem-solving. Use when troubleshooting bugs, understanding error messages, or investigating unexpected behavior.

Why use Debugging on TypingMind?

Because you install it once and use it with any model. Debugging 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 Debugging in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LangConfig/langconfig/tree/main/backend/skills/builtin/debugging. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Debugging?

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 Debugging?

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

Is the Debugging AI skill free?

Yes. It is published on GitHub by LangConfig under the MIT 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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