How do I install the Benchmark skill?

Run npx skillmds add thedixitjain/benchmark-4 in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Benchmark safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Benchmark?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Benchmark free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Benchmark?

thedixitjain (@thedixitjain) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Benchmark by thedixitjain — Agent Skill

Name: Benchmark
Rating: 4.2 (5 reviews)
Author: thedixitjain

File contents

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 | WARNING: WARN |
| Bundle | 180KB | 175KB | -5KB | ✓ BETTER |
| Build | 12s | 14s | +2s | WARNING: 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

Source: affaan-m/ECC → skills/benchmark/SKILL.md

Also appears in: hashgraph-online/awesome-codex-plugins/plugins/Colin4k1024/tsp/skills/benchmark/SKILL.md

---name: benchmark-4description: Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.---# 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 | WARNING: WARN | | Bundle | 180KB | 175KB | -5KB | ✓ BETTER | | Build | 12s | 14s | +2s | WARNING: 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 --- **Source:** [`affaan-m/ECC`](https://github.com/affaan-m/ECC) → `skills/benchmark/SKILL.md` **Also appears in:** `hashgraph-online/awesome-codex-plugins/plugins/Colin4k1024/tsp/skills/benchmark/SKILL.md`

Frequently asked questions