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Bug Detective

Free

Systematic debugging workflow for code errors.

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Free · Opens the source repo

What Bug Detective does

Bug Detective provides a structured approach to debugging, guiding users through a systematic process to identify and resolve code errors, exceptions, and failures. This skill is particularly useful for developers and designers who encounter issues in their code and need a reliable method to troubleshoot effectively. It emphasizes understanding the problem, gathering evidence, forming hypotheses, verifying those hypotheses, and applying fixes, ensuring a comprehensive debugging experience.

The skill begins by helping users clarify the symptoms of the problem and the expected behavior. It prompts for essential information such as error messages, file locations, and reproduction steps, enabling a thorough understanding of the issue at hand. Once the problem is defined, users can analyze the type of error they are facing, whether it be a syntax error, type error, or network error, and select appropriate debugging strategies based on the characteristics of the error.

To locate the source of the problem, Bug Detective offers various methods including binary search, log tracing, breakpoint debugging, and stack trace analysis. These techniques help users narrow down the problematic code and understand the flow of execution. The skill also provides a framework for forming and verifying hypotheses about the causes of errors, encouraging a scientific approach to problem-solving.

Additionally, Bug Detective includes references to common error patterns across Python, JavaScript/TypeScript, and Bash/Zsh, equipping users with knowledge of frequent pitfalls and how to avoid them. Overall, this skill is an invaluable resource for anyone looking to improve their debugging skills and resolve issues systematically.

When to use it

Use Bug Detective when you encounter an error message or unexpected behavior in your code and need a structured approach to troubleshoot.

When not to use it

This skill may not be suitable for very simple issues that can be resolved with quick fixes or for users who prefer ad-hoc debugging methods.

What you can build with it

Fixing a Syntax Error

When encountering a syntax error in Python, use Bug Detective to analyze the error message and locate the exact line causing the issue.

Troubleshooting a Network Issue

If your application fails to connect to a server, Bug Detective helps you gather evidence and verify network settings systematically.

Resolving Import Errors

When facing an ImportError, Bug Detective guides you through checking module installations and path configurations.

How to install Bug Detective

View source

1. Install with the skills CLI

npx skills add galaxy-dawn/claude-scholar/bug-detective --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by galaxy-dawn

Bug Detective

A systematic debugging workflow for investigating and resolving code errors, exceptions, and failures. Provides structured debugging methods and common error pattern recognition.

Core Philosophy

Debugging is a scientific problem-solving process that requires:

  1. Understand the problem - Clearly define symptoms and expected behavior
  2. Gather evidence - Collect error messages, logs, stack traces
  3. Form hypotheses - Infer possible causes based on evidence
  4. Verify hypotheses - Confirm or eliminate causes through experiments
  5. Resolve the issue - Apply fixes and verify

Debugging Workflow

Step 1: Understand the Problem

Before starting to debug, clarify the following information:

Required information to collect:

  • Complete error message content
  • Exact location of the error (filename and line number)
  • Reproduction steps (how to trigger the error)
  • Expected behavior vs actual behavior
  • Environment info (OS, versions, dependencies)

Question template:

1. What is the exact error message?
2. Which file and line does the error occur at?
3. How can this issue be reproduced? Provide detailed steps.
4. What was the expected result? What actually happened?
5. What recent changes might have introduced this issue?

Step 2: Analyze Error Type

Choose a debugging strategy based on error type:

Error TypeCharacteristicsDebugging Method
Syntax ErrorCode cannot be parsedCheck syntax, bracket matching, quotes
Import ErrorModuleNotFoundErrorCheck module installation, path config
Type ErrorTypeErrorCheck data types, type conversions
Attribute ErrorAttributeErrorCheck if object attribute exists
Key ErrorKeyErrorCheck if dictionary key exists
Index ErrorIndexErrorCheck list/array index range
Null ReferenceNoneType/NullPointerExceptionCheck if variable is None
Network ErrorConnectionError/TimeoutCheck network connection, URL, timeout settings
Permission ErrorPermissionErrorCheck file permissions, user permissions
Resource ErrorFileNotFoundErrorCheck if file path exists

Step 3: Locate the Problem Source

Use the following methods to locate the issue:

1. Binary Search Method

  • Comment out half the code, check if the problem persists
  • Progressively narrow the scope until the problematic code is found

2. Log Tracing

  • Add print/logging statements at key locations
  • Track variable value changes
  • Confirm code execution path

3. Breakpoint Debugging

  • Use debugger breakpoint functionality
  • Step through code execution
  • Inspect variable state

4. Stack Trace Analysis

  • Find the call chain from the stack trace in the error message
  • Determine the direct cause of the error
  • Trace back to the root cause

Step 4: Form and Verify Hypotheses

Hypothesis framework:

Hypothesis: [problem description] causes [error phenomenon]

Verification steps:
1. [verification method 1]
2. [verification method 2]

Expected results:
- If hypothesis is correct: [expected phenomenon]
- If hypothesis is wrong: [expected phenomenon]

Step 5: Apply Fix

After fixing, verify:

  1. The original error is resolved
  2. No new errors have been introduced
  3. Related functionality still works correctly
  4. Tests added to prevent regression

Python Common Error Patterns

1. Indentation Errors

2. Mutable Default Arguments

3. Closure Issues in Loops

4. Modifying a List While Iterating

5. Using is for String Comparison

6. Forgetting to Call super().__init__()

JavaScript/TypeScript Common Error Patterns

1. this Binding Issues

2. Async Error Handling

3. Object Reference Comparison

Bash/Zsh Common Error Patterns

1. Spacing Issues

# ❌ No spaces allowed in assignment
name = "John"  # Error: tries to run 'name' command

# ✅ Correct assignment
name="John"

# ❌ Missing spaces in conditional test
if[$name -eq 1]; then  # Error

# ✅ Correct
if [ $name -eq 1 ]; then

2. Quoting Issues

# ❌ Variables not expanded inside single quotes
echo 'The value is $var'  # Output: The value is $var

# ✅ Use double quotes
echo "The value is $var"  # Output: The value is actual_value

# ❌ Using backticks for command substitution (confusing)
result=`command`

# ✅ Use $()
result=$(command)

3. Unquoted Variables

# ❌ Unquoted variable, empty value causes errors
rm -rf $dir/*  # If dir is empty, deletes all files in current directory

# ✅ Always quote variables
[ -n "$dir" ] && rm -rf "$dir"/*

# Or use set -u to prevent undefined variables
set -u  # or set -o nounset

4. Variable Scope in Loops

# ❌ Pipe creates subshell, outer variable unchanged
cat file.txt | while read line; do
    count=$((count + 1))  # Outer count won't change
done
echo "Total: $count"  # Outputs 0

# ✅ Use process substitution or redirection
while read line; do
    count=$((count + 1))
done < file.txt
echo "Total: $count"  # Correct output

5. Array Operations

# ❌ Incorrect array access
arr=(1 2 3)
echo $arr[1]  # Outputs 1[1]

# ✅ Correct array access
echo ${arr[1]}  # Outputs 2
echo ${arr[@]}  # Outputs all elements
echo ${#arr[@]} # Outputs array length

6. String Comparison

# ✅ Use `=` inside POSIX `[` tests and `==` inside Bash `[[ ]]` tests
if [ "$name" = "John" ]; then
if [[ "$name" == "John" ]]; then

# ❌ Using -eq for numeric comparison instead of =
if [ $age = 18 ]; then  # Wrong

# ✅ Use arithmetic operators for numeric comparison
if [ $age -eq 18 ]; then
if (( age == 18 )); then

7. Command Failure Continues Execution

# ❌ Execution continues after command failure
cd /nonexistent
rm file.txt  # Deletes file.txt in current directory

# ✅ Use set -e to exit on error
set -e  # or set -o errexit
cd /nonexistent  # Script exits here
rm file.txt

# Or check if command succeeded
cd /nonexistent || exit 1

Common Debugging Commands

Python pdb Debugger

python -m pdb script.py
pytest -x -vv tests/test_target.py

Node.js Inspector

node --inspect-brk app.js
node --trace-warnings app.js

Git Bisect

git bisect start
git bisect bad
git bisect good <known-good-commit>

Bash Debugging

# Run script in debug mode
bash -x script.sh  # Print each command
bash -v script.sh  # Print command source
bash -n script.sh  # Syntax check, no execution

# Enable debugging within a script
set -x  # Enable command tracing
set -v  # Enable verbose mode
set -e  # Exit on error
set -u  # Error on undefined variables
set -o pipefail  # Fail if any command in pipe fails

Preventive Debugging

1. Use Type Checking

2. Input Validation

3. Defensive Programming

4. Logging

Debugging Checklist

Before Starting

  • Obtain the complete error message
  • Record the stack trace of the error
  • Confirm reproduction steps
  • Understand expected behavior

During Debugging

  • Check recent code changes
  • Use binary search to locate the issue
  • Add logs to trace variables
  • Verify hypotheses

After Resolution

  • Confirm the original error is fixed
  • Test related functionality
  • Add tests to prevent regression
  • Document the problem and solution

Additional Resources

Reference Files

For detailed debugging techniques and patterns:

  • references/python-errors.md - Python error details
  • references/javascript-errors.md - JavaScript/TypeScript error details
  • references/shell-errors.md - Bash/Zsh script error details
  • references/debugging-tools.md - Debugging tools usage guide
  • references/common-patterns.md - Common error patterns

Example Files

Working debugging examples:

  • examples/debugging-workflow.py - Complete debugging workflow example
  • examples/error-handling-patterns.py - Error handling patterns
  • examples/debugging-workflow.sh - Shell script debugging example

Frequently asked questions about Bug Detective

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