debugging-strategies
wshobson/agents
Master systematic debugging techniques and root cause analysis to efficiently track down bugs across any codebase.
What is debugging-strategies?
A comprehensive guide to transforming debugging from guesswork into systematic problem-solving. Covers the scientific method for debugging, profiling tools for JavaScript/TypeScript, Python, and Go, and advanced techniques like binary search and differential debugging. Use when investigating bugs, performance issues, or unexpected behavior in any codebase.
- Apply the scientific method (observe, hypothesize, experiment, analyze) to systematically find root causes
- Reproduce issues consistently and create minimal reproductions to isolate problems
- Use debuggers and profiling tools for JavaScript/TypeScript (Chrome DevTools, VS Code), Python (pdb, ipdb), and Go (Delve)
- Implement strategic logging and console techniques to trace execution flow and variable values
- Apply advanced techniques like binary search debugging (git bisect) and differential debugging to narrow down issues
- Gather and organize information systematically: error messages, environment details, recent changes, and scope
How to install debugging-strategies
npx skills add https://github.com/wshobson/agents --skill debugging-strategiesHow to use debugging-strategies
- 1.Start with the reproduction phase: verify you can consistently reproduce the issue and document exact steps
- 2.Gather information: collect error messages, stack traces, environment details, and recent changes
- 3.Form a hypothesis: identify what changed and what's different between working and broken states
- 4.Test your hypothesis using one of four strategies: binary search (narrow code sections), add logging, isolate components, or compare working vs broken
- 5.Use the appropriate debugger for your language: Chrome DevTools/VS Code for JavaScript, pdb/ipdb for Python, Delve for Go
- 6.Apply advanced techniques like git bisect for regressions or differential debugging to compare environments
Use cases
- Tracking down elusive bugs in production or development environments
- Investigating performance issues and memory leaks using profiling tools
- Debugging distributed systems and understanding unfamiliar codebases
- Analyzing crash dumps, stack traces, and exception handling
- Finding regressions by comparing working vs broken code states
- Backend developers debugging Node.js, Python, or Go applications
- Frontend developers using Chrome DevTools and VS Code debuggers
- DevOps engineers investigating production issues
- QA engineers reproducing and documenting bugs
- Any developer working with unfamiliar codebases
debugging-strategies FAQ
Don't assume anything. Question everything, reproduce consistently, and isolate problems systematically. Avoid assumptions like 'it can't be X' or 'I didn't change Y'—verify instead.
Gather detailed information about the production environment (OS, runtime versions, dependencies), compare it to your working environment, and use logging and profiling tools. Consider using post-mortem debugging to analyze crash dumps.
Explain your code and problem out loud to a rubber duck, colleague, or yourself. The act of explaining often reveals the issue without needing external input.
Use git bisect to perform binary search across commits. Start with a known good commit and a known bad one, then git checks out middle commits for you to test until the problematic commit is found.
Use Chrome DevTools or VS Code debugger for JavaScript, cProfile for Python, or pprof for Go. All support CPU and memory profiling to identify bottlenecks.
Full instructions (SKILL.md)
Source of truth, from wshobson/agents.
name: debugging-strategies description: Master systematic debugging techniques, profiling tools, and root cause analysis to efficiently track down bugs across any codebase or technology stack. Use when investigating bugs, performance issues, or unexpected behavior.
Debugging Strategies
Transform debugging from frustrating guesswork into systematic problem-solving with proven strategies, powerful tools, and methodical approaches.
When to Use This Skill
- Tracking down elusive bugs
- Investigating performance issues
- Understanding unfamiliar codebases
- Debugging production issues
- Analyzing crash dumps and stack traces
- Profiling application performance
- Investigating memory leaks
- Debugging distributed systems
Core Principles
1. The Scientific Method
1. Observe: What's the actual behavior? 2. Hypothesize: What could be causing it? 3. Experiment: Test your hypothesis 4. Analyze: Did it prove/disprove your theory? 5. Repeat: Until you find the root cause
2. Debugging Mindset
Don't Assume:
- "It can't be X" - Yes it can
- "I didn't change Y" - Check anyway
- "It works on my machine" - Find out why
Do:
- Reproduce consistently
- Isolate the problem
- Keep detailed notes
- Question everything
- Take breaks when stuck
3. Rubber Duck Debugging
Explain your code and problem out loud (to a rubber duck, colleague, or yourself). Often reveals the issue.
Systematic Debugging Process
Phase 1: Reproduce
## Reproduction Checklist
1. **Can you reproduce it?**
- Always? Sometimes? Randomly?
- Specific conditions needed?
- Can others reproduce it?
2. **Create minimal reproduction**
- Simplify to smallest example
- Remove unrelated code
- Isolate the problem
3. **Document steps**
- Write down exact steps
- Note environment details
- Capture error messages
Phase 2: Gather Information
## Information Collection
1. **Error Messages**
- Full stack trace
- Error codes
- Console/log output
2. **Environment**
- OS version
- Language/runtime version
- Dependencies versions
- Environment variables
3. **Recent Changes**
- Git history
- Deployment timeline
- Configuration changes
4. **Scope**
- Affects all users or specific ones?
- All browsers or specific ones?
- Production only or also dev?
Phase 3: Form Hypothesis
## Hypothesis Formation
Based on gathered info, ask:
1. **What changed?**
- Recent code changes
- Dependency updates
- Infrastructure changes
2. **What's different?**
- Working vs broken environment
- Working vs broken user
- Before vs after
3. **Where could this fail?**
- Input validation
- Business logic
- Data layer
- External services
Phase 4: Test & Verify
## Testing Strategies
1. **Binary Search**
- Comment out half the code
- Narrow down problematic section
- Repeat until found
2. **Add Logging**
- Strategic console.log/print
- Track variable values
- Trace execution flow
3. **Isolate Components**
- Test each piece separately
- Mock dependencies
- Remove complexity
4. **Compare Working vs Broken**
- Diff configurations
- Diff environments
- Diff data
Debugging Tools
JavaScript/TypeScript Debugging
// Chrome DevTools Debugger
function processOrder(order: Order) {
debugger; // Execution pauses here
const total = calculateTotal(order);
console.log("Total:", total);
// Conditional breakpoint
if (order.items.length > 10) {
debugger; // Only breaks if condition true
}
return total;
}
// Console debugging techniques
console.log("Value:", value); // Basic
console.table(arrayOfObjects); // Table format
console.time("operation");
/* code */ console.timeEnd("operation"); // Timing
console.trace(); // Stack trace
console.assert(value > 0, "Value must be positive"); // Assertion
// Performance profiling
performance.mark("start-operation");
// ... operation code
performance.mark("end-operation");
performance.measure("operation", "start-operation", "end-operation");
console.log(performance.getEntriesByType("measure"));
VS Code Debugger Configuration:
// .vscode/launch.json
{
"version": "0.2.0",
"configurations": [
{
"type": "node",
"request": "launch",
"name": "Debug Program",
"program": "${workspaceFolder}/src/index.ts",
"preLaunchTask": "tsc: build - tsconfig.json",
"outFiles": ["${workspaceFolder}/dist/**/*.js"],
"skipFiles": ["<node_internals>/**"]
},
{
"type": "node",
"request": "launch",
"name": "Debug Tests",
"program": "${workspaceFolder}/node_modules/jest/bin/jest",
"args": ["--runInBand", "--no-cache"],
"console": "integratedTerminal"
}
]
}
Python Debugging
# Built-in debugger (pdb)
import pdb
def calculate_total(items):
total = 0
pdb.set_trace() # Debugger starts here
for item in items:
total += item.price * item.quantity
return total
# Breakpoint (Python 3.7+)
def process_order(order):
breakpoint() # More convenient than pdb.set_trace()
# ... code
# Post-mortem debugging
try:
risky_operation()
except Exception:
import pdb
pdb.post_mortem() # Debug at exception point
# IPython debugging (ipdb)
from ipdb import set_trace
set_trace() # Better interface than pdb
# Logging for debugging
import logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
def fetch_user(user_id):
logger.debug(f'Fetching user: {user_id}')
user = db.query(User).get(user_id)
logger.debug(f'Found user: {user}')
return user
# Profile performance
import cProfile
import pstats
cProfile.run('slow_function()', 'profile_stats')
stats = pstats.Stats('profile_stats')
stats.sort_stats('cumulative')
stats.print_stats(10) # Top 10 slowest
Go Debugging
// Delve debugger
// Install: go install github.com/go-delve/delve/cmd/dlv@latest
// Run: dlv debug main.go
import (
"fmt"
"runtime"
"runtime/debug"
)
// Print stack trace
func debugStack() {
debug.PrintStack()
}
// Panic recovery with debugging
func processRequest() {
defer func() {
if r := recover(); r != nil {
fmt.Println("Panic:", r)
debug.PrintStack()
}
}()
// ... code that might panic
}
// Memory profiling
import _ "net/http/pprof"
// Visit http://localhost:6060/debug/pprof/
// CPU profiling
import (
"os"
"runtime/pprof"
)
f, _ := os.Create("cpu.prof")
pprof.StartCPUProfile(f)
defer pprof.StopCPUProfile()
// ... code to profile
Advanced Debugging Techniques
Technique 1: Binary Search Debugging
# Git bisect for finding regression
git bisect start
git bisect bad # Current commit is bad
git bisect good v1.0.0 # v1.0.0 was good
# Git checks out middle commit
# Test it, then:
git bisect good # if it works
git bisect bad # if it's broken
# Continue until bug found
git bisect reset # when done
Technique 2: Differential Debugging
Compare working vs broken:
## What's Different?
| Aspect | Working | Broken |
| ------------ | ----------- | -------------- |
| Environment | Development | Production |
| Node version | 18.16.0 | 18.15.0 |
| Data | Empty DB | 1M records |
| User | Admin | Regular user |
| Browser | Chrome | Safari |
| Time | During day | After midnight |
Hypothesis: Time-based issue? Check timezone handling.
Technique 3: Trace Debugging
// Function call tracing
function trace(
target: any,
propertyKey: string,
descriptor: PropertyDescriptor,
) {
const originalMethod = descriptor.value;
descriptor.value = function (...args: any[]) {
console.log(`Calling ${propertyKey} with args:`, args);
const result = originalMethod.apply(this, args);
console.log(`${propertyKey} returned:`, result);
return result;
};
return descriptor;
}
class OrderService {
@trace
calculateTotal(items: Item[]): number {
return items.reduce((sum, item) => sum + item.price, 0);
}
}
Technique 4: Memory Leak Detection
// Chrome DevTools Memory Profiler
// 1. Take heap snapshot
// 2. Perform action
// 3. Take another snapshot
// 4. Compare snapshots
// Node.js memory debugging
if (process.memoryUsage().heapUsed > 500 * 1024 * 1024) {
console.warn("High memory usage:", process.memoryUsage());
// Generate heap dump
require("v8").writeHeapSnapshot();
}
// Find memory leaks in tests
let beforeMemory: number;
beforeEach(() => {
beforeMemory = process.memoryUsage().heapUsed;
});
afterEach(() => {
const afterMemory = process.memoryUsage().heapUsed;
const diff = afterMemory - beforeMemory;
if (diff > 10 * 1024 * 1024) {
// 10MB threshold
console.warn(`Possible memory leak: ${diff / 1024 / 1024}MB`);
}
});
Debugging Patterns by Issue Type
Pattern 1: Intermittent Bugs
## Strategies for Flaky Bugs
1. **Add extensive logging**
- Log timing information
- Log all state transitions
- Log external interactions
2. **Look for race conditions**
- Concurrent access to shared state
- Async operations completing out of order
- Missing synchronization
3. **Check timing dependencies**
- setTimeout/setInterval
- Promise resolution order
- Animation frame timing
4. **Stress test**
- Run many times
- Vary timing
- Simulate load
Pattern 2: Performance Issues
## Performance Debugging
1. **Profile first**
- Don't optimize blindly
- Measure before and after
- Find bottlenecks
2. **Common culprits**
- N+1 queries
- Unnecessary re-renders
- Large data processing
- Synchronous I/O
3. **Tools**
- Browser DevTools Performance tab
- Lighthouse
- Python: cProfile, line_profiler
- Node: clinic.js, 0x
Pattern 3: Production Bugs
## Production Debugging
1. **Gather evidence**
- Error tracking (Sentry, Bugsnag)
- Application logs
- User reports
- Metrics/monitoring
2. **Reproduce locally**
- Use production data (anonymized)
- Match environment
- Follow exact steps
3. **Safe investigation**
- Don't change production
- Use feature flags
- Add monitoring/logging
- Test fixes in staging
Best Practices
- Reproduce First: Can't fix what you can't reproduce
- Isolate the Problem: Remove complexity until minimal case
- Read Error Messages: They're usually helpful
- Check Recent Changes: Most bugs are recent
- Use Version Control: Git bisect, blame, history
- Take Breaks: Fresh eyes see better
- Document Findings: Help future you
- Fix Root Cause: Not just symptoms
Common Debugging Mistakes
- Making Multiple Changes: Change one thing at a time
- Not Reading Error Messages: Read the full stack trace
- Assuming It's Complex: Often it's simple
- Debug Logging in Prod: Remove before shipping
- Not Using Debugger: console.log isn't always best
- Giving Up Too Soon: Persistence pays off
- Not Testing the Fix: Verify it actually works
Quick Debugging Checklist
## When Stuck, Check:
- [ ] Spelling errors (typos in variable names)
- [ ] Case sensitivity (fileName vs filename)
- [ ] Null/undefined values
- [ ] Array index off-by-one
- [ ] Async timing (race conditions)
- [ ] Scope issues (closure, hoisting)
- [ ] Type mismatches
- [ ] Missing dependencies
- [ ] Environment variables
- [ ] File paths (absolute vs relative)
- [ ] Cache issues (clear cache)
- [ ] Stale data (refresh database)
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