Best use case
Performance Reviewer is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
## Overview
Teams using Performance Reviewer should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/performance-reviewer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Performance Reviewer Compares
| Feature / Agent | Performance Reviewer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
## Overview
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Performance Reviewer
## Overview
This skill analyzes code changes for performance regressions and optimization opportunities. It catches common issues like N+1 database queries, unnecessary re-renders in React components, missing database indexes, unoptimized loops, and bundle size increases before they reach production.
## Instructions
### Analyzing a Diff or PR
1. Get the diff: `git diff main...HEAD` or `git diff <base>...<head>`
2. For each changed file, evaluate against these performance categories:
**Database & Queries:**
- Look for queries inside loops (N+1 pattern)
- Check for missing `WHERE` clauses or full table scans
- Identify missing indexes on columns used in `WHERE`, `JOIN`, or `ORDER BY`
- Flag `SELECT *` when only specific columns are needed
- Watch for unbounded queries without `LIMIT`
**Frontend & Rendering:**
- React: Check for missing `useMemo`/`useCallback` on expensive computations passed as props
- Look for state updates that trigger unnecessary re-renders of large component trees
- Flag inline object/array creation in render (creates new reference every render)
- Check for large bundle imports (`import moment` → suggest `dayjs`)
**Algorithm & Data Structures:**
- Flag O(n²) or worse algorithms when O(n log n) alternatives exist
- Look for repeated array searches that should use a Set or Map
- Identify string concatenation in loops (suggest StringBuilder/join)
**Memory & Resources:**
- Check for missing cleanup in `useEffect` (event listeners, intervals, subscriptions)
- Look for growing arrays/objects that are never trimmed
- Flag missing connection pool limits or unclosed file handles
**Network & I/O:**
- Identify sequential API calls that could be parallelized (`Promise.all`)
- Check for missing pagination on list endpoints
- Flag missing caching for expensive or repeated operations
### Output Format
For each issue found, report:
- **File and line number**
- **Category** (Database, Frontend, Algorithm, Memory, Network)
- **Severity** (Critical, Warning, Info)
- **What's wrong** (specific description)
- **Suggested fix** (concrete code suggestion)
### Severity Guidelines
- **Critical**: Will cause visible degradation in production (N+1 in a loop, O(n²) on large datasets, memory leak)
- **Warning**: May cause issues at scale (missing index, unnecessary re-renders, sequential API calls)
- **Info**: Optimization opportunity, not urgent (bundle size suggestion, minor refactor)
## Examples
### Example 1: N+1 Query Detection
**Input diff:**
```python
# api/views.py
def list_orders(request):
orders = Order.objects.filter(user=request.user)
result = []
for order in orders:
items = OrderItem.objects.filter(order=order) # ← query per order
result.append({
"id": order.id,
"total": order.total,
"items": [{"name": i.product.name, "qty": i.quantity} for i in items]
})
return JsonResponse(result, safe=False)
```
**Output:**
```
🔴 Critical — Database N+1 Query
File: api/views.py:5-8
Issue: OrderItem query executes once per order, plus each i.product.name
triggers another query. For 50 orders with 3 items each = 201 queries.
Fix: Use select_related and prefetch_related:
orders = Order.objects.filter(user=request.user).prefetch_related(
Prefetch('items', queryset=OrderItem.objects.select_related('product'))
)
Result: Reduces to 3 queries regardless of order count.
```
### Example 2: React Re-render Issue
**Input diff:**
```jsx
function UserList({ users, onSelect }) {
return (
<div>
{users.map(user => (
<UserCard
key={user.id}
user={user}
style={{ padding: 16, margin: 8 }}
onClick={() => onSelect(user.id)}
/>
))}
</div>
);
}
```
**Output:**
```
🟡 Warning — Unnecessary Re-renders
File: components/UserList.jsx:6-7
Issue: Inline object `style={{ padding: 16, margin: 8 }}` and arrow function
`() => onSelect(user.id)` create new references every render, causing
all UserCard components to re-render even when users haven't changed.
Fix:
const cardStyle = useMemo(() => ({ padding: 16, margin: 8 }), []);
const handleClick = useCallback((id) => onSelect(id), [onSelect]);
// Then: style={cardStyle} onClick={() => handleClick(user.id)}
Impact: With 100+ users, prevents ~100 unnecessary DOM diffing operations per parent render.
```
## Guidelines
- Focus on issues introduced by the diff, not pre-existing problems
- Prioritize Critical issues — don't bury them in a list of Info suggestions
- Always provide concrete fix suggestions, not just "optimize this"
- Consider the scale: an O(n²) loop on a 5-element array is fine; on user-generated data it's not
- When suggesting caching, specify what to cache and invalidation strategy
- Don't flag micro-optimizations that harm readability for negligible gainRelated Skills
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