# Review Performance Fundamentals

Performance bugs often hide in normal code reviews until traffic or data volume grows. This skill gives Claude, Codex, and Claude Code a focused checklist for finding common scalability issues.

## Install

```bash
npx skillstore add danielpodolsky/performance-fundamentals
```

## Metadata

- Status: approved
- Slug: danielpodolsky-performance-fundamentals
- Skillstore revision: r1
- Version status: missing
- Tree hash: a679890688f7155aeaa11d26848066e0099ecb01e82aab6682b98da7a2521a1a
- Author: DanielPodolsky
- GitHub username: DanielPodolsky
- License: MIT
- Repository: https://github.com/DanielPodolsky/mentor-spec/tree/main/.claude/skills/fundamentals/performance
- Ref: 34f316ba14ef36c7a620fc09f2676d2429997a77
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands
- Quality score: 78
- Quality tier: bronze
- Public page: https://skillstore.pages.dev/skills/danielpodolsky-performance-fundamentals
- Manifest: https://skillstore.pages.dev/api/skills/danielpodolsky-performance-fundamentals/manifest

## Capabilities

- Reviews database access for N\+1 queries, missing indexes, pagination gaps, and excessive field selection.
- Checks frontend rendering patterns for avoidable re-renders, expensive render work, and missing memoization.
- Evaluates API responses for oversized payloads, missing compression, weak caching, and slow synchronous work.
- Highlights resource cleanup risks such as timers, subscriptions, listeners, and connection handling.
- Uses scale-focused questions to guide junior developers toward performance-aware decisions.
- Provides quick Big O references and target metrics for common web performance reviews.

## Use Cases

- Review Pull Requests for Scalability: Find common performance risks in changed code before the branch is merged.
- Coach Junior Developers: Use targeted questions to teach query count, rendering cost, memory cleanup, and payload size.
- Prepare Production Readiness Checks: Review endpoints, data flows, and UI paths for issues that appear under larger data sets.

## Prompt Templates

### Basic Performance Review

```
Review this change for basic performance issues. Focus on loops, queries, rendering, payload size, and cleanup.
```

### Database and API Review

```
Review the database and API performance of this change. Check query count, indexes, pagination, selected fields, caching headers, and response size.
```

### Frontend Render Review

```
Review this UI code for rendering performance. Look for expensive render work, unnecessary re-renders, missing memoization, large lists, and missing cleanup.
```

### Advanced Scalability Audit

```
Audit this feature for scalability risks. Estimate behavior at 10,000 and 1,000,000 records, identify bottlenecks, and suggest measurable validation steps.
```

## Limitations

- Does not run benchmarks, load tests, profilers, or database explain plans by itself.
- Cannot prove performance impact without runtime data from the target application.
- May need framework-specific context to judge memoization, caching, and query strategies correctly.
- Focuses on common review patterns, not deep systems tuning or infrastructure capacity planning.

## Best Practices

- Review performance against expected data volume, not only the current sample size.
- Pair each suggested optimization with a measurement plan or observable production metric.
- Prefer clear fixes for query count, pagination, cleanup, and payload size before complex tuning.

## Anti Patterns

- Treating every slow path as a memoization problem without checking data access first.
- Optimizing small local examples while ignoring production record counts and traffic patterns.
- Adding caches without invalidation rules, ownership, monitoring, or memory limits.

## Security Audit

- Audited at: 2026-07-06T09:35:26.241\+00:00
- Summary: All static external command findings are false positives caused by Markdown code fences or inline code formatting in SKILL.md. The skill contains performance review guidance only; no prompt injection, external command execution, network access, or data exfiltration intent was found.

## Stats

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- Downloads: 10
- Favorites: 0
- Popularity score: 0