---

name: performance-auditing description: Guide for analyzing and improving application performance including identifying bottlenecks, implementing caching, and optimizing queries. This skill should be used when reviewing performance issues or optimizing code.

Performance Audit Skill

This skill provides elite performance engineering expertise for making applications lightning-fast through systematic optimization.

When to Use This Skill

Invoke this skill when:

• Analyzing slow page loads or response times
• Identifying performance bottlenecks in code execution
• Designing and implementing caching strategies
• Optimizing database queries and preventing N+1 problems
• Reducing memory consumption or investigating memory leaks
• Improving asset delivery (compression, minification, bundling)
• Implementing lazy loading or code splitting
• Profiling and benchmarking code performance
• Reviewing new features for performance implications
• Establishing performance budgets for critical user journeys

Core Performance Expertise

1. Performance Analysis Methodology

To analyze performance issues effectively:

Measure First: Always establish baseline metrics before optimization. Use profiling tools, timing measurements, and performance monitoring to identify actual bottlenecks rather than assumed ones.

Prioritize Impact: Focus on optimizations that provide the greatest performance improvement relative to implementation effort. Target the critical path and high-traffic code paths first.

Consider Trade-offs: Evaluate each optimization for its impact on code maintainability, complexity, and resource usage. Sometimes a 10% performance gain isn't worth a 50% increase in code complexity.

Validate Improvements: After implementing optimizations, measure again to confirm actual performance gains. Be prepared to roll back changes that don't deliver meaningful improvements.

2. Caching Strategies

To implement effective caching:

• Choose the appropriate caching layer (browser cache, CDN, application cache, database query cache, computed result cache)
• Implement proper cache invalidation strategies to prevent stale data issues
• Use cache keys that are specific enough to avoid collisions but general enough to maximize hit rates
• Set appropriate TTLs based on data volatility and business requirements
• Implement cache warming for predictable high-traffic scenarios
• Use cache-aside, write-through, or write-behind patterns as appropriate
• Monitor cache hit rates and adjust strategies based on real usage patterns

Key Rules:

• Never cache without considering invalidation strategy
• Always measure cache hit rates to validate effectiveness
• Balance cache complexity against actual performance benefits

3. Frontend Performance Optimization

To optimize frontend performance, focus on:

Critical Rendering Path:

• Minimize render-blocking resources (CSS, JavaScript)
• Prioritize above-the-fold content loading
• Use resource hints (preload, prefetch, preconnect)

Asset Optimization:

• Compress and minify JavaScript and CSS
• Optimize images (format, compression, responsive sizes)
• Implement lazy loading for images and off-screen content
• Use code splitting to reduce initial bundle size

Runtime Performance:

• Debounce and throttle user interaction handlers
• Use virtual scrolling for large lists
• Offload CPU-intensive tasks to Web Workers
• Implement efficient React re-render patterns (memoization, useMemo, useCallback)

Key Rules:

• Always measure with real-world conditions (throttled network, low-end devices)
• Focus on First Contentful Paint (FCP) and Time to Interactive (TTI)
• Avoid premature optimization of rarely-executed code

4. Backend Performance Optimization

To optimize backend performance, address:

Database Performance:

• Add indexes on frequently queried columns
• Prevent N+1 query problems with eager loading
• Use query explain plans to identify slow operations
• Implement connection pooling for database connections
• Consider read replicas for high-traffic read operations

Request Processing:

• Implement pagination and filtering for large datasets
• Use asynchronous processing for long-running tasks
• Batch similar operations to reduce overhead
• Implement request/response compression

Resource Management:

• Use connection pooling for external services
• Implement circuit breakers for failing dependencies
• Set appropriate timeouts to prevent resource exhaustion

Key Rules:

• Database queries should use indexes, not full table scans
• Long-running operations belong in background jobs, not HTTP requests
• Always implement pagination for unbounded result sets

5. Infrastructure Performance

To optimize infrastructure performance:

• Configure CDN caching for static assets
• Implement load balancing for horizontal scaling
• Use appropriate database indexing and sharding strategies
• Enable compression (gzip, brotli) for text-based responses
• Optimize container resource allocation

Key Rules:

• CDN cache misses should be minimized through proper cache headers
• Horizontal scaling requires stateless application design
• Monitor resource utilization to right-size infrastructure

Report Output Format

IMPORTANT: The section below defines the COMPLETE report structure that MUST be used. Do NOT create your own format or simplified version.

Location and Naming

• Directory: /docs/performance/
• Filename: YYYY-MM-DD-HHMMSS-performance-audit.md
• Example: 2025-10-29-143022-performance-audit.md

Report Template

🚨 CRITICAL INSTRUCTION - READ CAREFULLY 🚨

You MUST use this exact template structure for ALL performance audit reports. This is MANDATORY and NON-NEGOTIABLE.

REQUIREMENTS:

1. ✅ Use the COMPLETE template structure below - ALL sections are REQUIRED
2. ✅ Follow the EXACT heading hierarchy (##, ###, ####)
3. ✅ Include ALL section headings as written in the template
4. ✅ Use the finding numbering format: P-001, P-002, etc.
5. ✅ Include the tables, code examples, and checklists as shown
6. ❌ DO NOT create your own format or structure
7. ❌ DO NOT skip or combine sections
8. ❌ DO NOT create abbreviated or simplified versions
9. ❌ DO NOT number issues as "1, 2, 3" - use P-001, P-002, P-003 format

If you do not follow this template exactly, the report will be rejected.

Audit Overview

• Target System: [Application Name/System]
• Analysis Date: [Date Range]
• Analysis Scope: [Web Application/API/Database/Full Stack]
• Technology Stack: [e.g., .NET 8, Umbraco CMS, SQL Server, Elasticsearch, Azure]

Performance Assessment Summary

Performance Level	Count	Percentage
Critical Issues	X	X%
High Impact	X	X%
Medium Impact	X	X%
Low Impact	X	X%
Total	X	100%

Key Analysis Results

• Performance Anti-Patterns: X critical patterns identified requiring immediate attention
• Code Optimization Opportunities: X high-impact optimizations discovered
• Architecture Assessment: X/10 performance best practices implemented
• Overall Code Performance Score: X/100 (based on static analysis and architectural patterns)

---

Analysis Methodology

Performance Analysis Approach

• Static Code Analysis: Comprehensive source code review for performance anti-patterns
• Database Query Analysis: Review of SQL queries, indexing strategies, and data access patterns
• Resource Utilization Assessment: Analysis of memory, CPU, and I/O usage patterns
• Architecture Performance Review: Examination of caching, scaling, and optimization strategies

Analysis Coverage

• Files Analyzed: X source files across Y projects
• Database Queries Reviewed: X queries and stored procedures
• API Endpoints Tested: X endpoints across Y controllers
• Performance Patterns Checked: N+1 queries, memory leaks, CPU bottlenecks, I/O blocking

Analysis Capabilities

• Pattern Detection: N+1 queries, inefficient loops, memory leaks, blocking operations
• Database Analysis: Missing indexes, expensive queries, deadlock potential
• Resource Analysis: Memory allocation patterns, CPU-intensive operations, I/O bottlenecks
• Architecture Assessment: Caching strategies, async/await patterns, connection pooling

---

Performance Findings

Critical Performance Issues

P-001: N+1 Query Problem

Location: src/Website.Core/Services/VendorService.cs:78 Performance Impact: 9.8 (Critical) Pattern Detected: Loading related entities in a loop causing multiple database queries Code Context:

foreach (var vendor in vendors)
{
    vendor.Products = context.Products.Where(p => p.VendorId == vendor.Id).ToList();
}

Impact: Database query count increases linearly with result set size (1 + N queries instead of 1) Performance Cost: 2000ms+ response time for 100 vendors Recommendation: Use Include() for eager loading or projection for specific fields Fix Priority: Immediate (within 24 hours)

P-002: Synchronous Database Operations

Location: src/Website.Web/Controllers/ApiController.cs:45 Performance Impact: 9.1 (Critical) Pattern Detected: Synchronous database calls blocking request threads Code Context: Missing async/await pattern in controller actions Impact: Thread pool exhaustion under high load, poor scalability Performance Cost: Thread starvation affecting overall application responsiveness Recommendation: Convert all database operations to async/await pattern Fix Priority: Immediate (within 48 hours)

High Performance Impact Findings

P-003: Large Object Heap Pressure

Location: Multiple locations in data processing services Performance Impact: 7.8 (High) Pattern Detected: Large objects (>85KB) causing frequent Gen 2 garbage collection Affected Components:

• src/Website.AirtableData/Services/ImportService.cs:156
• src/Website.ElasticSearch/Services/IndexingService.cs:89 Impact: GC pressure causing application pauses and increased memory usage Performance Cost: 200-500ms GC pauses, 40% higher memory usage Recommendation: Implement streaming for large data sets, use object pooling Fix Priority: Within 1 week

P-004: Inefficient Elasticsearch Queries

Location: src/Website.ElasticSearch/Services/SearchService.cs:123 Performance Impact: 7.5 (High) Pattern Detected: Full-text search without field targeting or filtering Code Context: Broad queries without proper field restrictions or caching Impact: High Elasticsearch cluster load, slow search response times Performance Cost: 800ms+ search response time, high CPU usage on ES cluster Recommendation: Implement targeted field searches, result caching, and query optimization Fix Priority: Within 2 weeks

Medium Performance Impact Findings

P-005: Missing Database Indexes

Location: Database schema analysis Performance Impact: 6.3 (Medium) Pattern Detected: Frequent WHERE clauses on non-indexed columns Affected Tables:

• Vendors table: missing index on OrganizationId, IsActive
• Products table: missing composite index on CategoryId, Status, CreatedDate Impact: Table scans causing slow query performance Performance Cost: 500-1200ms query response time for filtered data Recommendation: Add appropriate indexes based on query patterns Fix Priority: Within 1 month

P-006: Inefficient LINQ Queries

Location: Multiple service classes Performance Impact: 5.9 (Medium) Pattern Detected: Multiple enumeration of IEnumerable, inefficient projections Affected Areas: Vendor listing, product filtering, category navigation Impact: Unnecessary CPU cycles, increased memory allocation Performance Cost: 200-400ms additional processing time Recommendation: Use ToList() strategically, optimize LINQ expressions Fix Priority: Within 1 month

Low Performance Impact Findings

P-007: Missing Output Caching

Location: Web application controllers and views Performance Impact: 3.8 (Low) Pattern Detected: Repeated computation of static or semi-static content Missing Caching: Category lists, navigation menus, vendor counts Impact: Unnecessary CPU usage for frequently accessed data Performance Cost: 50-100ms additional processing per request Recommendation: Implement response caching and memory caching strategies Fix Priority: Within 2 months

---

Code Pattern Performance Analysis

Performance Anti-Pattern Detection

• N+1 Query Patterns: X instances detected across Y service classes
• Synchronous Blocking Operations: X async-convertible operations identified
• Large Object Allocations: X locations with >85KB object creation
• Inefficient LINQ Usage: X queries with multiple enumeration or suboptimal patterns

Database Access Pattern Analysis

• Entity Framework Usage: X queries analyzed for efficiency patterns
• Missing Async Patterns: X database operations identified for async conversion
• Query Complexity: X complex queries requiring optimization review
• Connection Management: Connection pooling configuration assessment

Resource Management Pattern Analysis

• Memory Allocation Patterns: Large object heap pressure points identified
• Garbage Collection Pressure: X locations with excessive object creation
• Thread Pool Usage: X blocking operations affecting scalability
• Caching Opportunities: X frequently computed operations without caching

---

Architecture Performance Assessment

Data Access Layer Analysis

• Entity Framework Performance: ⚠️ N+1 queries detected, lazy loading causing issues
• Connection Pooling: ✅ Properly configured with appropriate pool sizes
• Query Optimization: ❌ Missing indexes and inefficient LINQ expressions
• Caching Strategy: ❌ Insufficient caching at data layer

Application Layer Analysis

• Async/Await Usage: ❌ Many synchronous operations blocking threads
• Memory Management: ⚠️ Some memory leaks in background services
• CPU Utilization: ⚠️ CPU-intensive operations not optimized
• I/O Operations: ❌ File operations and API calls not properly optimized

Infrastructure Analysis

• Load Balancing: ✅ Properly configured with health checks
• CDN Usage: ⚠️ Static assets cached but optimization needed
• Database Scaling: ⚠️ Read replicas available but not fully utilized
• Monitoring Coverage: ❌ Limited application performance monitoring

---

Performance Bottleneck Analysis

Top 10 Performance Bottlenecks

Rank	Component	Issue	Impact Score	Response Time Impact
1	VendorService	N+1 Query Problem	9.8	+2000ms
2	ApiController	Sync DB Operations	9.1	Thread exhaustion
3	ImportService	Large Object Heap	7.8	+500ms GC pauses
4	SearchService	Inefficient ES Queries	7.5	+800ms
5	Database	Missing Indexes	6.3	+600ms
6	LINQ Queries	Multiple Enumeration	5.9	+300ms
7	File Operations	Synchronous I/O	5.2	+400ms
8	Caching	Missing Cache Strategy	4.8	+200ms
9	Background Jobs	Memory Leaks	4.5	Resource exhaustion
10	API Serialization	Large Payloads	3.9	+150ms

---

Technical Recommendations

Immediate Performance Fixes

1. Fix N+1 query problems using Include() or projection in Entity Framework
2. Convert synchronous operations to async/await pattern for scalability
3. Add missing database indexes for frequently queried columns
4. Implement connection pooling for external service calls

Performance Enhancements

1. Implement comprehensive caching strategy using Redis or in-memory caching
2. Optimize Elasticsearch queries with proper field targeting and filtering
3. Add response compression for API endpoints and static content
4. Implement lazy loading for heavy components and data

Architecture Improvements

1. Add application performance monitoring using Application Insights or similar
2. Implement database read replicas for read-heavy operations
3. Add background job optimization with proper queue management
4. Implement API rate limiting and request throttling

---

Code Optimization Examples

N+1 Query Fix

Before (Inefficient):

var vendors = context.Vendors.ToList();
foreach (var vendor in vendors)
{
    vendor.Products = context.Products.Where(p => p.VendorId == vendor.Id).ToList();
}

After (Optimized):

var vendors = context.Vendors
    .Include(v => v.Products)
    .ToList();
// OR for specific fields only
var vendorsWithProductCount = context.Vendors
    .Select(v => new VendorViewModel
    {
        Id = v.Id,
        Name = v.Name,
        ProductCount = v.Products.Count()
    })
    .ToList();

Async/Await Implementation

Before (Blocking):

[HttpGet]
public IActionResult GetVendors()
{
    var vendors = vendorService.GetAll(); // Synchronous call
    return Ok(vendors);
}

After (Non-blocking):

[HttpGet]
public async Task<IActionResult> GetVendors()
{
    var vendors = await vendorService.GetAllAsync(); // Asynchronous call
    return Ok(vendors);
}

Caching Implementation

Before (No Caching):

public List<Category> GetCategories()
{
    return context.Categories.OrderBy(c => c.Name).ToList();
}

After (With Caching):

public async Task<List<Category>> GetCategoriesAsync()
{
    const string cacheKey = "categories_all";
    var cached = await cache.GetAsync<List<Category>>(cacheKey);
    if (cached != null)
        return cached;

    var categories = await context.Categories
        .OrderBy(c => c.Name)
        .ToListAsync();

    await cache.SetAsync(cacheKey, categories, TimeSpan.FromMinutes(30));
    return categories;
}

---

Performance Optimization Priorities

Phase 1: Critical Performance Fixes

• Fix N+1 queries in VendorService.cs:78
• Convert synchronous database operations to async in ApiController.cs:45
• Add missing database indexes for Vendors and Products tables
• Implement connection pooling for Elasticsearch service

Phase 2: High Impact Optimizations

• Optimize large object allocations in import services
• Implement caching strategy for frequently accessed data
• Optimize Elasticsearch queries with proper filtering
• Fix memory leaks in background job processing

Phase 3: Medium Impact Improvements

• Optimize LINQ queries to avoid multiple enumeration
• Implement response compression and output caching
• Add database read replicas for read operations
• Optimize file I/O operations with async patterns

Phase 4: Performance Monitoring and Fine-tuning

• Implement comprehensive APM solution
• Add custom performance counters and metrics
• Set up automated performance testing
• Document performance best practices

---

Estimated Performance Improvement Impact

Performance Gains by Priority

Priority Level	Expected Improvement	Implementation Complexity
Critical Fixes	60-80% response time improvement	High - requires careful testing
High Impact	30-50% overall performance gain	Medium - architectural changes
Medium Impact	15-25% additional optimization	Medium - code and config changes
Low Impact	5-10% fine-tuning benefits	Low - mostly configuration

Resource Utilization Improvements

• Database Load: Expected 40-60% reduction in query execution time
• Memory Usage: Expected 25-35% reduction in memory pressure
• CPU Utilization: Expected 20-30% reduction in CPU usage
• Thread Pool: Expected elimination of thread starvation issues

---

Performance Monitoring Setup Recommendations

Monitoring Infrastructure Setup

• Application Performance Monitoring: Azure Application Insights or New Relic integration
• Database Monitoring: SQL Server Extended Events and Performance Dashboard configuration
• Infrastructure Monitoring: Azure Monitor or Prometheus + Grafana setup
• Code-Level Monitoring: Custom performance counters for identified bottlenecks

Recommended Performance Tracking

• Database Query Monitoring: Track queries identified in this analysis for execution time
• Memory Allocation Tracking: Monitor large object heap allocations in flagged components
• Thread Pool Monitoring: Track thread starvation in areas with synchronous operations
• Cache Hit Rate Monitoring: Measure effectiveness of recommended caching implementations

Performance Testing Recommendations

• Load Testing: Focus on endpoints with identified N+1 query problems
• Database Performance Testing: Test queries with missing indexes under load
• Memory Pressure Testing: Validate large object allocation fixes
• Concurrency Testing: Verify async/await implementations under concurrent load

---

Summary

This performance analysis identified X critical, Y high, Z medium, and W low performance issues across the application stack. The analysis focused on code patterns, database queries, resource utilization, and architectural performance without requiring extensive load testing infrastructure.

Key Strengths Identified:

• Good modular architecture with clear separation of concerns
• Proper use of modern .NET 8 features and Umbraco CMS
• Well-structured database schema with appropriate relationships

Critical Areas Requiring Immediate Attention:

• N+1 query problems causing database performance issues
• Synchronous operations blocking thread pool resources
• Missing database indexes for frequently queried data
• Inefficient memory allocation patterns in data processing

Expected Overall Performance Improvement: 70-90% reduction in response times and 40-60% improvement in resource utilization after implementing all recommendations.

Examples

Example 1: N+1 Query Problem

Bad approach:

const orders = await Order.findAll();
for (const order of orders) {
  order.customer = await Customer.findByPk(order.customerId);
  order.items = await OrderItem.findAll({ where: { orderId: order.id } });
}

Good approach:

const orders = await Order.findAll({
  include: [
    { model: Customer },
    { model: OrderItem }
  ]
});

Example 2: Inefficient Caching

Bad approach:

// Cache entire dataset, never invalidate
const cache = await getCachedData('all-products');
if (cache) return cache;
const products = await Product.findAll();
await setCachedData('all-products', products, 86400); // 24 hours

Good approach:

// Cache with granular keys and appropriate TTL
const cacheKey = `products:page:${page}:filter:${filter}`;
const cache = await getCachedData(cacheKey);
if (cache) return cache;

const products = await Product.findAll({ where: filter, limit: 20, offset: page * 20 });
await setCachedData(cacheKey, products, 300); // 5 minutes

// Invalidate on product updates
await invalidateCachePattern('products:*');

Example 3: Unoptimized Asset Loading

Bad approach:

<!-- Loading full-size images for all screen sizes -->
<img src="/images/hero-4k.jpg" alt="Hero image">

Good approach:

<!-- Responsive images with lazy loading -->
<img
  srcset="
    /images/hero-mobile.jpg 640w,
    /images/hero-tablet.jpg 1024w,
    /images/hero-desktop.jpg 1920w
  "
  sizes="(max-width: 640px) 640px, (max-width: 1024px) 1024px, 1920px"
  src="/images/hero-desktop.jpg"
  alt="Hero image"
  loading="lazy"
>

Best Practices

1. Measure Before and After: Never optimize without establishing baseline metrics. Use profiling tools to identify actual bottlenecks, then validate improvements with measurements.

2. Optimize the Critical Path: Focus on the most-used features and flows first. A 50% improvement on a feature used by 80% of users has more impact than a 90% improvement on a rarely-used feature.

3. Consider Total Cost: Evaluate optimizations holistically - faster code that uses 10x more memory or is 5x harder to maintain may not be a good trade-off.

4. Use Appropriate Tools: Leverage browser dev tools, database query analyzers, profilers, and APM tools to identify bottlenecks scientifically rather than guessing.

5. Implement Progressive Enhancement: Optimize for the common case while gracefully handling edge cases. Don't sacrifice reliability for speed.

6. Monitor in Production: Performance in development often differs from production. Implement real user monitoring (RUM) to track actual user experience.

7. Set Performance Budgets: Establish and enforce performance budgets for page weight, load time, and critical metrics. Prevent performance regression through automated checks.

8. Document Trade-offs: When implementing complex optimizations, document the reasoning, expected benefits, and any maintenance considerations for future developers.

Quality Assurance Checklist

Before recommending any optimization, verify:

• ✓ Have baseline metrics been established?
• ✓ Does the optimization address a real bottleneck, not premature optimization?
• ✓ Will the solution work under production load conditions?
• ✓ Have potential bugs or edge cases been considered?
• ✓ Is the impact on code readability and maintainability acceptable?
• ✓ Can the improvement be validated through testing?
• ✓ Are monitoring metrics defined to track ongoing effectiveness?

Common Performance Anti-Patterns

Proactively identify these common issues:

Database Anti-Patterns

• N+1 queries (missing eager loading)
• Missing indexes on filtered/joined columns
• Using SELECT * instead of specific columns
• Fetching all records without pagination
• Executing queries in loops

Frontend Anti-Patterns

• Loading all JavaScript upfront (no code splitting)
• Large, unoptimized images
• Synchronous, render-blocking scripts
• Excessive re-renders in React (missing memoization)
• Memory leaks from uncleared intervals/listeners

Caching Anti-Patterns

• Caching without invalidation strategy
• Cache keys too granular (low hit rate)
• Cache keys too broad (stale data)
• No cache monitoring
• Caching entire large datasets

API Anti-Patterns

• No rate limiting
• Returning excessive data (no field filtering)
• Missing pagination
• Synchronous processing of async operations
• No response compression

Performance Testing Strategies

To validate performance improvements:

1. Load Testing: Simulate concurrent users to identify breaking points
2. Profiling: Use CPU and memory profilers to identify hotspots
3. Benchmarking: Create reproducible performance tests for critical paths
4. Real User Monitoring: Track actual user experience in production
5. Synthetic Monitoring: Automated performance tests from various locations

Context-Aware Analysis

When project-specific context is available in CLAUDE.md files, incorporate:

• Technology Stack: Identify framework-specific optimization opportunities
• Usage Patterns: Optimize for actual traffic patterns and user behavior
• Infrastructure: Consider deployment architecture and resource constraints
• Performance Requirements: Align optimizations with business SLAs and budgets

Communication Guidelines

When reporting performance findings:

• Lead with measured impact (seconds, requests, bytes)
• Provide concrete code examples showing before/after
• Explain the "why" behind optimizations, not just the "what"
• Set realistic expectations for performance improvements
• Acknowledge when existing code is already well-optimized
• Recommend incremental improvements over risky rewrites

Remember: The goal is to make applications measurably faster while maintaining code quality and reliability. Combine deep technical knowledge with practical engineering judgment to deliver optimizations that matter.