Quality perf review

Performance Review Skill

Analyze a full-stack web application for real performance bottlenecks. Focus on evidence-based issues, not generic suggestions. Interactive workflow that discusses each finding with the user.

When to Use This Skill

Use this skill when the user requests:

• "Perf review" / "Performance review"
• "Find bottlenecks"
• "Optimize performance"
• "Why is my app slow?"
• "Performance audit"
• "Slow code analysis"

Core Principles

1. Evidence over intuition: Only flag issues you can prove are bottlenecks
2. Impact over quantity: Fewer high-impact findings beat many micro-optimizations
3. Safety first: Every suggestion must not change functionality
4. Interactive review: Discuss each finding before moving to the next

---

Workflow

Phase 1: Project Discovery

Before analyzing, understand the project:

# Detect project type
ls package.json bun.lockb yarn.lock pnpm-lock.yaml 2>/dev/null

# Check for existing benchmarks
find . -name "*.bench.ts" -o -name "*.benchmark.ts" -o -name "bench.ts" 2>/dev/null

# Check for profiling config
ls .clinic clinic.json 0x.json 2>/dev/null

# Check frontend framework
grep -l "react\|vue\|svelte\|next\|nuxt" package.json 2>/dev/null

Inform the user:

• "I'll analyze [project type] for performance issues."
• "Found existing benchmarks: [list] / No existing benchmarks found."
• "I'll focus on [backend/frontend/both] based on your project structure."

Phase 2: Static Analysis

Analyze code for known performance anti-patterns. Check each category based on what's relevant to the project.

Backend Performance Checks

1. Database Queries (N+1, Missing Indexes)

Look for:

• Queries inside loops (N+1 pattern)
• Missing include/join in ORM queries
• SELECT * when only specific columns needed
• Missing pagination on large result sets

Grep patterns:
- "\.find\(" inside forEach/map/for loops
- "await.*query" inside loops
- "SELECT \*" in raw queries

2. API Endpoints

Look for:

• Synchronous operations that should be async
• Missing response streaming for large payloads
• Unnecessary serialization/deserialization
• Missing caching headers

3. Memory Patterns

Look for:

• Growing arrays/objects without bounds
• Event listeners not being removed
• Large objects held in closures
• Missing cleanup in long-running processes

4. I/O Operations

Look for:

• readFileSync / writeFileSync (sync file ops)
• Unbuffered streams
• Missing compression for responses
• Sequential I/O that could be parallel

5. Event Loop Blocking

Look for:

• CPU-intensive operations in request handlers
• Large JSON.parse/JSON.stringify on main thread
• Synchronous crypto operations
• Missing worker threads for heavy computation

Frontend Performance Checks

1. Bundle Size

Look for:

• Large dependencies (moment.js, lodash full import)
• Missing tree-shaking (import lodash vs import { map } from 'lodash')
• Duplicate dependencies
• Dev dependencies in production bundle

# Check bundle size if available
bun build --analyze 2>/dev/null || npx webpack-bundle-analyzer 2>/dev/null

2. Rendering Performance

Look for:

• Components re-rendering on every parent render
• Missing useMemo/useCallback for expensive computations
• Inline object/array creation in JSX props
• Missing key props or using index as key

3. Asset Loading

Look for:

• Unoptimized images (missing width/height, no lazy loading)
• Render-blocking CSS/JS
• Missing preload/prefetch for critical resources
• Large fonts loading synchronously

4. Network Efficiency

Look for:

• Waterfall requests (sequential when parallel possible)
• Missing data prefetching
• Over-fetching (requesting more data than needed)
• Missing response caching

Phase 3: Dynamic Analysis (Ask Permission)

If static analysis is inconclusive, offer to run benchmarks:

Question: "I'd like to run some performance measurements to identify bottlenecks more precisely. May I?"
Options:
- "Yes, run benchmarks" - Execute performance tests
- "No, continue with static analysis" - Skip dynamic analysis
- "Tell me what you'd run first" - Explain before executing

If approved, depending on project type:

For Bun/Node backend:

# Profile with clinic (if available)
npx clinic doctor -- node server.js

# Or use built-in profiler
node --prof server.js

For frontend:

# Measure bundle size
bun build --minify --outdir=dist && du -sh dist/

# Or with webpack
npx webpack-bundle-analyzer stats.json

Phase 4: Interactive Review

For each finding, present it interactively:

Finding Format

## Finding [N]: [Title]

**Location:** `file/path.ts:123`
**Category:** [Database/Memory/Bundle/etc.]
**Impact:** [High/Medium/Low] - [explanation of impact]

### The Issue
[Code snippet showing the problem]

### Why This Matters
[Explanation with numbers if possible - e.g., "This query runs N times per request"]

### Proposed Fix
[Code snippet showing the fix]

### Safety Assessment
- **Regression risk:** [Low/Medium] - [explanation]
- **Behavior change:** None / [describe any edge case differences]
- **Test coverage:** [covered by X test / not covered]
- **Reversibility:** [easy to revert / requires migration]

Ask User

After presenting each finding, ask:

Question: "How would you like to proceed with this finding?"
Options:
- "Apply this fix" - I'll make the change
- "Skip for now" - Move to next finding
- "Tell me more" - Explain in more detail
- "I'll fix it myself" - Note it and move on

Phase 5: Summary

After reviewing all findings, provide a summary:

## Performance Review Summary

### Applied Fixes
1. [Finding 1] - [file:line]
2. [Finding 2] - [file:line]

### Skipped/Deferred
1. [Finding 3] - Reason: [user chose to skip / needs more investigation]

### Estimated Impact
- Database queries: X fewer per request
- Bundle size: -Y KB
- Response time: [estimated improvement]

### Recommended Next Steps
1. [Run the test suite to verify no regressions]
2. [Set up performance monitoring for X]
3. [Consider profiling Y in production]

---

Focus Areas

DO Focus On

• Evidence-based issues: Query counts, bundle sizes, profiler output
• High-impact patterns: O(n²) → O(n), N+1 queries, bundle bloat
• Measurable improvements: "This will reduce queries from N to 1"
• Safe optimizations: Pure performance gains with no behavior change

DO NOT Focus On

• Micro-optimizations: for vs forEach without evidence
• Premature optimization: Caching before proving it's needed
• Speculative improvements: "This might be slow" without proof
• Behavior-changing refactors: Even if faster, flag don't fix

---

Safety Guarantees

Before suggesting any fix, verify:

1. No behavior change: The output/result must be identical
2. Test coverage: Note if the area has tests or needs them
3. Reversibility: Prefer easily-revertible changes
4. Edge cases: Consider null, empty, concurrent access

If uncertain about safety, present as "potential optimization" requiring user verification rather than a fix to apply.

---

Examples

Example: N+1 Query Detection

Bad (N+1):

const users = await db.user.findMany();
for (const user of users) {
  const posts = await db.post.findMany({ where: { userId: user.id } });
  // ... 100 users = 101 queries
}

Good (single query):

const users = await db.user.findMany({
  include: { posts: true }
});
// 1 query with JOIN

Example: Bundle Size Issue

Bad (imports entire library):

import _ from 'lodash';
const result = _.map(items, transform);

Good (tree-shakeable):

import { map } from 'lodash-es';
const result = map(items, transform);

Example: Sync File Operation

Bad (blocks event loop):

app.get('/config', (req, res) => {
  const config = fs.readFileSync('./config.json');
  res.json(JSON.parse(config));
});

Good (non-blocking):

app.get('/config', async (req, res) => {
  const config = await Bun.file('./config.json').json();
  res.json(config);
});