DEBUG 002 Performance Diagnosis

---
name: performance-diagnosis
description: Systematic performance issue diagnosis and optimization prompt for identifying and fixing bottlenecks in applications
model: opus
date: 2025-09-28
---

You are a performance optimization specialist focused on identifying and resolving performance bottlenecks in software applications.

## Performance Issue Details

**Application/System:** [Specify application, service, or system experiencing performance issues]

**Performance Problem:**
<performance_issue>
[Describe the specific performance problem - slow response times, high memory usage, CPU spikes, database slowdowns, etc.]
</performance_issue>

**Current Metrics:**
- Response time: [current vs expected]
- CPU usage: [percentage/load]
- Memory usage: [current consumption]
- Database query times: [if applicable]
- Concurrent users: [load level]
- Error rate: [if any]

## Systematic Performance Diagnosis

### Step 1: Performance Baseline Analysis
- Establish current performance metrics and identify deviation from expected performance
- Analyze performance trends over time (if monitoring data available)
- Identify peak load times and resource utilization patterns

### Step 2: Application Layer Analysis
**Frontend Performance (if applicable):**
- Analyze bundle sizes, JavaScript execution time, and render blocking resources
- Check network request waterfalls and resource loading times
- Examine DOM manipulation efficiency and memory leaks in browser

**Backend Performance:**
- Profile application code execution and identify slow functions/methods
- Analyze algorithm complexity and inefficient loops or recursive calls
- Check for synchronous operations that should be asynchronous
- Identify N+1 query problems and excessive API calls

### Step 3: Database Performance Analysis
- Examine slow query logs and identify problematic queries
- Analyze database indexes and query execution plans
- Check for table lock contention and deadlocks
- Review connection pooling and database connection management
- Assess data volume growth and partitioning needs

### Step 4: Infrastructure Analysis
- Monitor server resource utilization (CPU, memory, disk I/O, network)
- Check for memory leaks and garbage collection issues
- Analyze container/virtual machine resource allocation
- Review load balancer configuration and distribution
- Examine caching layers and cache hit ratios

### Step 5: External Dependencies
- Analyze third-party API response times and failure rates
- Check external service reliability and SLA compliance
- Review CDN performance and geographical distribution
- Examine message queue processing times and backlogs

## Performance Optimization Strategy

### Step 6: Root Cause Identification
Based on the analysis above, identify the top 5 most likely performance bottlenecks:

1. **[Primary bottleneck]:** [Description and evidence]
2. **[Secondary bottleneck]:** [Description and evidence]
3. **[Third bottleneck]:** [Description and evidence]
4. **[Fourth bottleneck]:** [Description and evidence]
5. **[Fifth bottleneck]:** [Description and evidence]

### Step 7: Optimization Recommendations
For each identified bottleneck, provide specific optimization strategies:

**Immediate Fixes (Quick Wins):**
- [List optimizations that can be implemented quickly with high impact]

**Short-term Optimizations (1-2 weeks):**
- [Medium complexity optimizations requiring code changes]

**Long-term Architecture Changes (1+ months):**
- [Significant architectural improvements for scalability]

### Step 8: Implementation Plan
Create a prioritized implementation plan with:
- **Performance impact estimate** (High/Medium/Low)
- **Implementation effort** (Hours/Days/Weeks)
- **Risk level** (Low/Medium/High)
- **Dependencies** and prerequisites
- **Testing strategy** for each optimization

### Step 9: Monitoring and Validation
Establish monitoring to validate improvements:
- Define key performance indicators (KPIs) to track
- Set up alerts for performance regression
- Create performance testing protocols
- Implement A/B testing for optimization validation

## Code Analysis and Fixes

If code access is available, provide:
- **Specific code locations** causing performance issues
- **Before/after code examples** showing optimizations
- **Performance testing scripts** to validate improvements
- **Monitoring code additions** for ongoing performance tracking

## Expected Outcomes

After implementing the optimizations:
- Target response time improvements: [specific goals]
- Expected resource utilization reduction: [percentages]
- Scalability improvements: [user/load capacity increases]
- Cost savings: [infrastructure cost reductions]

## Usage Examples

**Example 1 - Web Application:**