Performance Profiling

---
name: performance-profiling-e3877952548d
description: Performance profiling principles. Measurement, analysis, and optimization techniques.
allowed-tools: Read, Glob, Grep, Bash
---

# Performance Profiling

> Measure, analyze, optimize - in that order.

## 🔧 Runtime Scripts

**Execute these for automated profiling:**

| Script | Purpose | Usage |
|--------|---------|-------|
| `scripts/lighthouse_audit.py` | Lighthouse performance audit | `python scripts/lighthouse_audit.py https://example.com` |

---

## 1. Core Web Vitals

### Targets

| Metric | Good | Poor | Measures |
|--------|------|------|----------|
| **LCP** | < 2.5s | > 4.0s | Loading |
| **INP** | < 200ms | > 500ms | Interactivity |
| **CLS** | < 0.1 | > 0.25 | Stability |

### When to Measure

| Stage | Tool |
|-------|------|
| Development | Local Lighthouse |
| CI/CD | Lighthouse CI |
| Production | RUM (Real User Monitoring) |

---

## 2. Profiling Workflow

### The 4-Step Process

```
1. BASELINE → Measure current state
2. IDENTIFY → Find the bottleneck
3. FIX → Make targeted change
4. VALIDATE → Confirm improvement
```

### Profiling Tool Selection

| Problem | Tool |
|---------|------|
| Page load | Lighthouse |
| Bundle size | Bundle analyzer |
| Runtime | DevTools Performance |
| Memory | DevTools Memory |
| Network | DevTools Network |

---

## 3. Bundle Analysis

### What to Look For

| Issue | Indicator |
|-------|-----------|
| Large dependencies | Top of bundle |
| Duplicate code | Multiple chunks |
| Unused code | Low coverage |
| Missing splits | Single large chunk |

### Optimization Actions

| Finding | Action |
|---------|--------|
| Big library | Import specific modules |
| Duplicate deps | Dedupe, update versions |
| Route in main | Code split |
| Unused exports | Tree shake |

---

## 4. Runtime Profiling

### Performance Tab Analysis

| Pattern | Meaning |
|---------|---------|
| Long tasks (>50ms) | UI blocking |
| Many small tasks | Possible batching opportunity |
| Layout/paint | Rendering bottleneck |
| Script | JavaScript execution |

### Memory Tab Analysis

| Pattern | Meaning |
|---------|---------|
| Growing heap | Possible leak |
| Large retained | Check references |
| Detached DOM | Not cleaned up |

---

## 5. Common Bottlenecks

### By Symptom

| Symptom | Likely Cause |
|---------|--------------|
| Slow initial load | Large JS, render blocking |
| Slow interactions | Heavy event handlers |
| Jank during scroll | Layout thrashing |
| Growing memory | Leaks, retained refs |

---

## 6. Quick Win Priorities

| Priority | Action | Impact |
|----------|--------|--------|
| 1 | Enable compression | High |
| 2 | Lazy load images | High |
| 3 | Code split routes | High |
| 4 | Cache static assets | Medium |
| 5 | Optimize images | Medium |

---

## 7. Anti-Patterns

| ❌ Don't | ✅ Do |
|----------|-------|
| Guess at problems | Profile first |
| Micro-optimize | Fix biggest issue |
| Optimize early | Optimize when needed |
| Ignore real users | Use RUM data |

---

> **Remember:** The fastest code is code that doesn't run. Remove before optimizing.

## AGI Framework Integration

### Qdrant Memory Integration

Before executing complex tasks with this skill:
```bash
python3 execution/memory_manager.py auto --query "<task summary>"
```

**Decision Tree:**
- **Cache hit?** Use cached response directly — no need to re-process.
- **Memory match?** Inject `context_chunks` into your reasoning.
- **No match?** Proceed normally, then store results:

```bash
python3 execution/memory_manager.py store \
  --content "Description of what was decided/solved" \
  --type decision \
  --tags performance-profiling <relevant-tags>
```

> **Note:** Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.

### Agent Team Collaboration

- **Strategy**: This skill communicates via the shared memory system.
- **Orchestration**: Invoked by `orchestrator` via intelligent routing.
- **Context Sharing**: Always read previous agent outputs from memory before starting.

### Local LLM Support

When available, use local Ollama models for embedding and lightweight inference:
- Embeddings: `nomic-embed-text` via Qdrant memory system
- Lightweight analysis: Local models reduce API costs for repetitive patterns