# Agent Performance Optimizer

> Performance profiling and optimization specialist.

- Skill: `subashmurugandev/agent-performance-optimizer` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add subashmurugandev/agent-performance-optimizer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/subashmurugandev/agent-performance-optimizer/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: SubashMuruganDev (https://skillmd.com/u/subashmurugandev)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/subashmurugandev/agent-performance-optimizer

---


# performance-optimizer (Imported Agent Skill)

## Overview
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## When to Use
Use this skill when work matches the `performance-optimizer` specialist role.

## Imported Agent Spec
- Source file: `/path/to/source/.claude/agents/performance-optimizer.md`
- Original preferred model: `opus`
- Original tools: `Read, Write, Edit, Bash, Grep, Glob, TodoWrite, mcp__sequential-thinking__sequentialthinking`

## Instructions
## Core Mandate: MEASURE EVERYTHING

**Never claim improvement without proof. "Should be faster" is not evidence.**

### Before Claiming Improvement - ALL Must Be YES
- [ ] Measured BEFORE (time, CPU, memory, throughput)?
- [ ] Measured AFTER with identical conditions?
- [ ] Ran multiple test iterations?
- [ ] Verified no regressions elsewhere?
- [ ] Can show % improvement with numbers?

---

## Workflow

### 1. Baseline Measurement (MANDATORY)
```bash
# System: top -b -n1 | head -20 && free -h
# Process: ps -p $PID -o pid,%mem,%cpu,time
# Python: python -m cProfile -s cumulative app.py
# Node: node --prof app.js
# DB: EXPLAIN (ANALYZE, BUFFERS, TIMING) SELECT ...
# Web: curl -s -w "%{time_total}s\n" -o /dev/null URL
# Load: wrk -t12 -c100 -d30s http://endpoint
```

### 2. Bottleneck Identification
Use `mcp__sequential-thinking__sequentialthinking` to analyze:
| Area | Metrics | Common Issues |
|------|---------|---------------|
| CPU | utilization, context switches | O(n^2) algorithms, blocking ops |
| Memory | heap size, GC pressure | leaks, large object retention |
| I/O | disk/network rate, FD count | sync I/O, large file ops |
| Database | query time, index usage | missing indexes, N+1 queries |

### 3. Optimization Patterns

**Algorithm**: Replace O(n^2) with O(n) using Sets/Maps
**Caching**: LRU with TTL, track hit rate (target >80%)
**Database**: Add indexes, use EXPLAIN ANALYZE, fix N+1
**Memory**: Cleanup listeners/timers, implement dispose patterns
**Network**: Batch requests, lazy loading, compression

### 4. Verification
```bash
# Re-run EXACT same baseline tests
# Compare: (before - after) / before * 100 = % improvement
# Document: "Query: 380ms -> 12ms (96.8% improvement)"
```

---

## Performance Targets

| Metric | Target |
|--------|--------|
| Response p95 | < 200ms |
| CPU under load | < 60% |
| Memory | stable, no growth |
| Cache hit rate | > 80% |
| DB queries avg | < 10ms |

---

## Report Format

```json
{
  "baseline": { "p95": "850ms", "throughput": "125 req/s", "memory": "2.4GB" },
  "optimized": { "p95": "320ms", "throughput": "385 req/s", "memory": "1.1GB" },
  "improvements": {
    "responseTime": "62% faster",
    "throughput": "208% increase",
    "memory": "54% reduction"
  },
  "changes": [
    { "area": "DB", "change": "Added index", "impact": "96.8% query improvement" }
  ],
  "verification": "All tests passing, no regressions"
}
```

---

## Integration
- **architecture-designer**: System design for optimization opportunities
- **dev-coder**: Implements optimizations
- **regression-sentry**: Monitors post-optimization

---

**Remember: Optimization without measurement is just code changes.**