# Performance Review

> Performance optimization analysis: CPU/memory profiling, caching strategies, database query optimization, connection pooling, concurrency patterns, memory leak detection, and throughput improvement. Use when code is slow, memory usage is high, latency needs reduction, or conducting performance reviews before release.

- Skill: `kcenon/performance-review` (Agent Skill, multi-file: 5 files)
- Install (CLI): `npx skillmds@latest add kcenon/performance-review`
- Raw SKILL.md: https://api.skillmd.com/api/skills/kcenon/performance-review/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: kcenon (https://skillmd.com/u/kcenon)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/kcenon/performance-review

---


# Performance Review Skill

## When to Use

- Code performance optimization
- Memory leak fixes
- Throughput improvements
- Performance review requests
- Bottleneck analysis

## Performance Analysis Workflow

```
Profiling → Identify bottlenecks → Optimize → Verify
```

## Checklist

### Algorithm & Data Structures

- [ ] Time complexity analysis (Big-O)
- [ ] Appropriate data structure selection
- [ ] Remove unnecessary operations

### Memory

- [ ] Minimize memory allocation
- [ ] Object reuse (pooling)
- [ ] Cache-friendly access patterns

### Concurrency

- [ ] Minimize lock contention
- [ ] Leverage async I/O
- [ ] Thread pool optimization

### Caching

- [ ] Appropriate cache strategy
- [ ] Cache invalidation policy
- [ ] Cache hit rate monitoring

## Reference Documents (Import Syntax)
@./reference/performance.md
@./reference/memory.md
@./reference/concurrency.md
@./reference/monitoring.md

## Caution

> "Premature optimization is the root of all evil" - Donald Knuth
>
> Always confirm bottlenecks through profiling before optimizing.

## Output

This skill runs in a forked context (`context: fork`) using the read-only `Explore` agent. It does not have access to the calling conversation's history — operate entirely from the supplied `<file-or-directory>` argument.

Return a structured report at the end of analysis:

```markdown
## Performance Review Report

| Severity | Findings |
|----------|----------|
| S1 (block-merge: clear regression) | N items |
| S2 (review-required: measured/suspected hotspot) | N items |
| S3 (advisory: style/maintainability) | N items |

### S1 Findings
1. `file.ext:line` — severity: S1 — finding + recommended optimization + expected gain
2. ...

### Hotspot Map
- Algorithm/data-structure issues: N
- Memory issues: N
- Concurrency issues: N
- Caching opportunities: N

### Coverage
- Files inspected: N
- Profiling data referenced: yes/no
- Categories not evaluated (need runtime data): ...
```

Each finding MUST include a `severity:` field (`S1`, `S2`, or `S3`). When a finding's severity is ambiguous, default to `S3` (advisory) per the false-positive playbook.