Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
日本語の概要は準備中です。原文の説明を表示しています。
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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Transform debugging from frustrating guesswork into systematic problem-solving with proven strategies, powerful tools, and methodical approaches.
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
Don't Assume:
Do:
Explain your code and problem out loud (to a rubber duck, colleague, or yourself). Often reveals the issue.
## 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
## 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?
## 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
## 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
// 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"
}
]
}
# 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
// 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
# 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
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.
// 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);
}
}
// 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`);
}
});
## 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
## 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
## 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
## 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)
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
日本語の概要は準備中です。原文の説明を表示しています。
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
日本語の概要は準備中です。原文の説明を表示しています。
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
日本語の概要は準備中です。原文の説明を表示しています。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
日本語の概要は準備中です。原文の説明を表示しています。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
日本語の概要は準備中です。原文の説明を表示しています。