debugging-strategies
Install this skill
npx skills add wshobson/agentsWorks across Claude Code, Cursor, Codex, Copilot & Antigravity
The debugging-strategies skill provides a structured framework for diagnosing software defects by moving away from intuition-based guessing toward an evidence-based approach. It emphasizes the scientific method, where developers establish reproducible test cases before forming and verifying hypotheses about code behavior. The skill offers specific workflows for environment isolation, binary search techniques for narrowing down problematic code segments, and the implementation of diagnostic logging or breakpoints. By applying these methodologies, engineers can efficiently reduce the time spent on troubleshooting complex performance regressions, intermittent state issues, or production failures. It covers standard practices for both JavaScript and Python, providing configurations for VS Code and native tooling to inspect execution flow. This approach ensures that fixes are based on observable facts rather than assumptions about code integrity or environmental configuration.
When to Use This Skill
- •Tracing a memory leak across distributed service calls
- •Isolating a performance bottleneck within a rendering loop
- •Standardizing the analysis of complex production stack traces
- •Comparing configuration states between stable and failing environments
How to Invoke This Skill
Example prompts that trigger this skill in Claude Code, Cursor, or Antigravity:
- “Help me debug this intermittent failure
- “How can I set up a reproduction for this stack trace?
- “Show me the best way to binary search this logic
- “Configure my VS Code environment for deep debugging
- “Guide me through a systematic investigation of this crash
Pro Tips
- 💡Always start by trying to consistently reproduce the bug in the simplest possible scenario before diving into code.
- 💡When stuck, revert to the smallest working state, then reintroduce changes one by one to pinpoint the exact line or commit that introduced the issue.
- 💡Document your observations, hypotheses, and experiments rigorously. This not only helps you but also aids future team members in understanding complex issues.
What this skill does
- •Systematizes the reproduction of intermittent software bugs
- •Configures VS Code launch profiles for Node.js and Jest debugging
- •Implements conditional breakpoints and performance profiling in TypeScript
- •Provides standardized templates for identifying environment discrepancies
- •Executes binary search patterns to isolate failing logic blocks
When not to use it
- ✕When simple syntax errors are evident from immediate linting feedback
- ✕When troubleshooting network-level infrastructure outages unrelated to the application code
Example workflow
- Create a minimal test case that reproduces the current error
- Define the expected output versus the observed failure
- Set up a conditional breakpoint in the IDE to pause on the fault condition
- Evaluate variable states and call stacks at the point of failure
- Apply a binary search to comment out code sections to locate the specific line of impact
- Validate the fix by confirming the test case no longer triggers the error
Prerequisites
- –IDE with integrated debugger support
- –Knowledge of the language runtime environment
- –Access to recent version control history
Pitfalls & limitations
- !Over-relying on print-style logging which can pollute console output
- !Failing to reset environmental variables between different test iterations
- !Ignoring race conditions that disappear when stepping through code manually
FAQ
How it compares
Unlike generic prompting, this skill enforces a strict, multi-phase scientific method that tracks state changes and environmental variables to prevent repeated circular debugging.
📄 Full skill instructions — original source: wshobson/agents
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
1. **Reproduce First**: Can't fix what you can't reproduce
2. **Isolate the Problem**: Remove complexity until minimal case
3. **Read Error Messages**: They're usually helpful
4. **Check Recent Changes**: Most bugs are recent
5. **Use Version Control**: Git bisect, blame, history
6. **Take Breaks**: Fresh eyes see better
7. **Document Findings**: Help future you
8. **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)## Resources
- **references/debugging-tools-guide.md**: Comprehensive tool documentation
- **references/performance-profiling.md**: Performance debugging guide
- **references/production-debugging.md**: Debugging live systems
- **assets/debugging-checklist.md**: Quick reference checklist
- **assets/common-bugs.md**: Common bug patterns
- **scripts/debug-helper.ts**: Debugging utility functions
How to Use This Skill Unit
Option A: Project-Specific (Recommended)
- Click "Download" above
- In your project, create the directory:
.agent/skills/debugging-strategies/ - Save the file as
SKILL.md - The agent will automatically discover the skill based on its description.
Option B: Global Installation (All Agents)
Save the file to these locations to make it available across all projects:
- Claude Code:
~/.claude/skills/wshobson/agents/debugging-strategies/SKILL.md - Cursor:
~/.cursor/skills/wshobson/agents/debugging-strategies/SKILL.md - Antigravity:
~/.gemini/antigravity/skills/wshobson/agents/debugging-strategies/SKILL.md
🚀 Install with CLI:npx skills add wshobson/agents