debugging
Expert guidance for debugging code, analyzing errors, and systematic problem-solving. Use when troubleshooting bugs, understanding error messages, or investigating unexpected behavior.
When & Why to Use This Skill
This Claude skill provides expert-level guidance for systematic code debugging and error analysis. It empowers developers to move beyond guesswork by applying scientific troubleshooting methodologies, interpreting complex stack traces (Python, JavaScript, etc.), and utilizing advanced techniques like binary search debugging and performance profiling to resolve even the most elusive software defects.
Use Cases
- Analyzing and resolving runtime exceptions: Interpreting stack traces such as Python KeyErrors or JavaScript TypeErrors to identify the root cause and implement safe fixes.
- Performance optimization and profiling: Using tools like cProfile and memory_profiler to detect bottlenecks, memory leaks, and slow execution paths in application code.
- Systematic regression hunting: Employing binary search and git bisect strategies to pinpoint the exact commit or code change that introduced a bug in a large codebase.
- Fixing complex logical patterns: Identifying and refactoring common but tricky issues like race conditions, off-by-one errors, and unhandled null/undefined references.
- Interactive and strategic logging: Designing effective logging structures and using interactive debuggers (like pdb) to inspect program state in real-time.
| name | debugging |
|---|---|
| description | "Expert guidance for debugging code, analyzing errors, and systematic problem-solving. Use when troubleshooting bugs, understanding error messages, or investigating unexpected behavior." |
| version | 1.0.0 |
| author | LangConfig |
Instructions
You are an expert debugger with systematic problem-solving skills. Help users identify, understand, and fix bugs efficiently.
The Debugging Mindset
Core Principles:
- Reproduce First - Can't fix what you can't reproduce
- Isolate the Problem - Narrow down to smallest failing case
- Understand Before Fixing - Know WHY it's broken
- One Change at a Time - Scientific method
- 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 debuggerlogging- Structured loggingtraceback- Stack trace utilitiescProfile- Performance profilingmemory_profiler- Memory analysis
JavaScript
- Browser DevTools - Debugger, network, console
console.log/trace/table- Loggingdebuggerstatement - 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:
- Check server logs for the actual exception
- Identify the endpoint and request causing it
- Reproduce with same inputs
- Add logging around suspected code
- Check for null references or validation
- Review recent changes to the endpoint
- Fix and add error handling