Benchmark — Performance Baseline & Regression Detection
## When to Use
Best use case
Benchmark — Performance Baseline & Regression Detection is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
## When to Use
Teams using Benchmark — Performance Baseline & Regression Detection 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/benchmark/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Benchmark — Performance Baseline & Regression Detection Compares
| Feature / Agent | Benchmark — Performance Baseline & Regression Detection | 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?
## When to Use
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
# Benchmark — Performance Baseline & Regression Detection ## When to Use - Before and after a PR to measure performance impact - Setting up performance baselines for a project - When users report "it feels slow" - Before a launch — ensure you meet performance targets - Comparing your stack against alternatives ## How It Works ### Mode 1: Page Performance Measures real browser metrics via browser MCP: ``` 1. Navigate to each target URL 2. Measure Core Web Vitals: - LCP (Largest Contentful Paint) — target < 2.5s - CLS (Cumulative Layout Shift) — target < 0.1 - INP (Interaction to Next Paint) — target < 200ms - FCP (First Contentful Paint) — target < 1.8s - TTFB (Time to First Byte) — target < 800ms 3. Measure resource sizes: - Total page weight (target < 1MB) - JS bundle size (target < 200KB gzipped) - CSS size - Image weight - Third-party script weight 4. Count network requests 5. Check for render-blocking resources ``` ### Mode 2: API Performance Benchmarks API endpoints: ``` 1. Hit each endpoint 100 times 2. Measure: p50, p95, p99 latency 3. Track: response size, status codes 4. Test under load: 10 concurrent requests 5. Compare against SLA targets ``` ### Mode 3: Build Performance Measures development feedback loop: ``` 1. Cold build time 2. Hot reload time (HMR) 3. Test suite duration 4. TypeScript check time 5. Lint time 6. Docker build time ``` ### Mode 4: Before/After Comparison Run before and after a change to measure impact: ``` /benchmark baseline # saves current metrics # ... make changes ... /benchmark compare # compares against baseline ``` Output: ``` | Metric | Before | After | Delta | Verdict | |--------|--------|-------|-------|---------| | LCP | 1.2s | 1.4s | +200ms | ⚠ WARN | | Bundle | 180KB | 175KB | -5KB | ✓ BETTER | | Build | 12s | 14s | +2s | ⚠ WARN | ``` ## Output Stores baselines in `.ecc/benchmarks/` as JSON. Git-tracked so the team shares baselines. ## Integration - CI: run `/benchmark compare` on every PR - Pair with `/canary-watch` for post-deploy monitoring - Pair with `/browser-qa` for full pre-ship checklist
Related Skills
cohere-performance-tuning
Optimize Cohere API performance with caching, batching, model selection, and streaming. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Cohere Chat, Embed, and Rerank. Trigger with phrases like "cohere performance", "optimize cohere", "cohere latency", "cohere caching", "cohere slow", "cohere batch".
coderabbit-performance-tuning
Optimize CodeRabbit review speed, relevance, and signal-to-noise ratio. Use when reviews take too long, contain too many irrelevant comments, or when teams are experiencing review fatigue. Trigger with phrases like "coderabbit performance", "optimize coderabbit", "coderabbit slow", "coderabbit noise", "coderabbit too many comments", "coderabbit relevance".
clickup-performance-tuning
Optimize ClickUp API v2 performance with caching, pagination, connection pooling, and request batching patterns. Trigger: "clickup performance", "optimize clickup", "clickup latency", "clickup caching", "clickup slow", "clickup batch requests", "clickup pagination".
clickhouse-performance-tuning
Optimize ClickHouse query performance with indexing, projections, settings tuning, and query analysis using system tables. Use when queries are slow, investigating performance bottlenecks, or tuning ClickHouse server settings. Trigger: "clickhouse performance", "optimize clickhouse query", "clickhouse slow query", "clickhouse indexing", "clickhouse tuning", "clickhouse projections".
clerk-performance-tuning
Optimize Clerk authentication performance. Use when improving auth response times, reducing latency, or optimizing Clerk SDK usage. Trigger with phrases like "clerk performance", "clerk optimization", "clerk slow", "clerk latency", "optimize clerk".
clay-performance-tuning
Optimize Clay table enrichment throughput, reduce processing time, and improve hit rates. Use when experiencing slow enrichment, poor email find rates, or needing to process large tables efficiently. Trigger with phrases like "clay performance", "optimize clay", "clay slow", "clay throughput", "clay fast enrichment", "clay batch optimization".
clari-performance-tuning
Optimize Clari API performance with caching, batch exports, and data pipeline efficiency. Use when exports take too long, optimizing data warehouse load times, or reducing API calls in multi-forecast environments. Trigger with phrases like "clari performance", "clari slow export", "optimize clari pipeline", "clari caching".
clade-performance-tuning
Optimize Anthropic API latency — streaming, prompt caching, model selection, Use when working with performance-tuning patterns. connection reuse, and parallel requests. Trigger with "anthropic slow", "claude latency", "speed up anthropic", "anthropic performance", "claude response time".
castai-performance-tuning
Optimize CAST AI autoscaler performance, node provisioning speed, and API efficiency. Use when nodes take too long to provision, autoscaler is not reacting fast enough, or optimizing API call patterns for multi-cluster dashboards. Trigger with phrases like "cast ai performance", "cast ai slow", "cast ai node provisioning", "cast ai autoscaler speed".
canva-performance-tuning
Optimize Canva Connect API performance with caching, pagination, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Canva integrations. Trigger with phrases like "canva performance", "optimize canva", "canva latency", "canva caching", "canva slow", "canva pagination".
optimizing-cache-performance
This skill enables Claude to analyze and improve application caching strategies. It optimizes cache hit rates, TTL configurations, cache key design, and invalidation strategies. Use this skill when the user requests to "optimize cache performance", "improve caching strategy", "analyze cache hit rate", or needs assistance with "cache key design", "TTL optimization", or "cache invalidation". The skill identifies potential bottlenecks and recommends adjustments for improved performance and efficiency of caching mechanisms like Redis.
brightdata-performance-tuning
Optimize Bright Data API performance with caching, batching, and connection pooling. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Bright Data integrations. Trigger with phrases like "brightdata performance", "optimize brightdata", "brightdata latency", "brightdata caching", "brightdata slow", "brightdata batch".