Source profileQuality 76/100

affaan-m/ECC/skills/benchmark/SKILL.md

benchmark

Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.

Source repository stars
234,327
Declared platforms
0
Static risk flags
0
Last source update
2026-07-27
Source checked
2026-07-28

Decision brief

What it does—and where it fits

Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.

Best for

  • Before and after a PR to measure performance impact
  • Setting up performance baselines for a project
  • When users report "it feels slow"

Not for

  • Tasks that require unconfirmed production actions or broad system permissions.
  • Environments where the pinned source and install steps cannot be inspected.

Compatibility matrix

Platform support, with evidence labels

PlatformStatusEvidenceWhat to check
CodexNot declaredNo explicit evidencePortability before use
Claude CodeNot declaredNo explicit evidencePortability before use
CursorNot declaredNo explicit evidencePortability before use
Gemini CLINot declaredNo explicit evidencePortability before use
Open the compatibility checker

Installation

Inspect first. Install second.

The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.

Source-detected install commandSource
npx skills add https://github.com/affaan-m/ECC --skill "skills/benchmark"
Safe inspection promptEditorial

Inspect the Agent Skill "benchmark" from https://github.com/affaan-m/ECC/blob/4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38/skills/benchmark/SKILL.md at commit 4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.

Workflow

What the source asks the agent to do

  1. 01

    When to Use

    Before and after a PR to measure performance impact

    Before and after a PR to measure performance impactSetting up performance baselines for a projectWhen users report "it feels slow"
  2. 02

    How It Works

    Measures real browser metrics via browser MCP:

    Measures real browser metrics via browser MCP:Benchmarks API endpoints:Measures development feedback loop:
  3. 03

    Mode 1: Page Performance

    Measures real browser metrics via browser MCP:

    Measures real browser metrics via browser MCP:
  4. 04

    Mode 2: API Performance

    Benchmarks API endpoints:

    Benchmarks API endpoints:

Permission review

Static risk signals and limitations

No configured static risk pattern was detected

This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.

Evidence record

Why each signal appears

EvidenceSourceComputedTestedEditorial
SignalValueEvidence typeMeaning
Quality score76/100ComputedDocumentation, specificity, maintenance, and trust rules
Repository stars234,327SourceRepository attention, not individual Skill quality
Compatibility0 platformsSourceDeclared in the catalog source record
Usage guideautomated source guideEditorialGenerated or reviewed according to the visible evidence level

Pinned source

Provenance and original SKILL.md

Repository
affaan-m/ECC
Skill path
skills/benchmark/SKILL.md
Commit
4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38
License
MIT
Collected
2026-07-28
Default branch
main
View the original SKILL.md

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

Alternatives

Compare before choosing