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affaan-m/ECC

benchmark

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

76Collecting
See how to use itView GitHub source
npx skills add https://github.com/affaan-m/ECC --skill "skills/benchmark"
Automated source guide

Source checked Jul 28, 2026·Refresh due Oct 26, 2026

Reorganized from the pinned upstream SKILL.md

Turn benchmark's source instructions into a guide you can follow

According to the pinned SKILL.md from affaan-m/ECC: Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.

npx skills add https://github.com/affaan-m/ECC --skill "skills/benchmark"
Check the pinned source

Best fit

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

Bring this context

  • A concrete task that matches the documented purpose of benchmark.
  • The files, examples, or context the task depends on.
  • Your constraints, target environment, and definition of done.

Expected outputs

  • Stores baselines in .ecc/benchmarks/ as JSON. Git-tracked so the team shares baselines.

Key source sections

Read benchmark through these 5 source sections

Sections are extracted automatically from the pinned SKILL.md and link back to the source.

01

When to Use

Before and after a PR to measure performance impact

SKILL.md · When to Use
Before and after a PR to measure performance impactSetting up performance baselines for a projectWhen users report "it feels slow"
02

How It Works

Measures real browser metrics via browser MCP:

SKILL.md · How It Works
Measures real browser metrics via browser MCP:Benchmarks API endpoints:Measures development feedback loop:

SkillSignal prompt templates

Provide the task, context, and acceptance criteria

These prompts were written by SkillSignal from the source structure; they are not upstream text.

Task-start prompt

Confirm source fit, inputs, and outputs before acting.

Use benchmark to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.

Source-guided execution

Make the Agent explicitly follow the key extracted sections.

Apply the pinned benchmark source to [task]. Pay particular attention to these source sections: “When to Use”, “How It Works”, “Mode 1: Page Performance”, “Mode 2: API Performance”, “Mode 3: Build Performance”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].

Result-review prompt

Check omissions, permissions, and source drift before delivery.

Review the current benchmark result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.

Output checklist

Verify each item before delivery

The task matches the purpose documented in the SKILL.md.

The source section “When to Use” has been checked.

The source section “How It Works” has been checked.

The source section “Mode 1: Page Performance” has been checked.

The source section “Mode 2: API Performance” has been checked.

Inputs, constraints, and acceptance criteria are explicit.

Unverified facts, compatibility, and outcome claims are clearly marked.

Any file, command, network, or data action has been reviewed.

Choose a different workflow

When another Skill is the better fit

benchmark

このスキルを使用して、パフォーマンスベースラインを測定し、PR前後の回帰を検出し、スタック代替案を比較します。

A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.

Open source detail

benchmark

使用此技能测量性能基线，检测PR前后的回归，并比较堆栈替代方案。

A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.

Open source detail

ab-testing

When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program

A separate implementation from coreyhaines31/marketingskills; compare its source, maintenance signals, and permission requirements.

Open source detail

FAQ

What does benchmark do?

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

How do I start using benchmark?

The catalog detected this source-specific install command: npx skills add https://github.com/affaan-m/ECC --skill "skills/benchmark". Inspect the command and pinned source before running it.

Which Agent platforms does it declare?

No dedicated Agent platform is declared in the pinned source record.

Repository stars
234,327
Repository forks
35,711
Quality
76/100
Source repository last pushed

Quality breakdown

Based on traceable docs and repository signals; stars are not treated as quality.

76/100
Documentation25/30
Specificity19/25
Maintenance20/20
Trust signals12/25

Compare before choosing

Related Agent Skills and source variants

These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.

benchmark by affaan-m

このスキルを使用して、パフォーマンスベースラインを測定し、PR前後の回帰を検出し、スタック代替案を比較します。

benchmark by affaan-m

使用此技能测量性能基线，检测PR前后的回归，并比较堆栈替代方案。

ab-testing by coreyhaines31

When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program

design-intelligence by event4u-app

Grounded design brief from the adopted corpus — style, WCAG-checked color tokens, typography, layout pattern, anti-patterns. Use on ui-design-brief or any which-style/palette/font/chart decision.

existing-ui-audit by event4u-app

Use BEFORE writing or editing any non-trivial UI — inventories components, design tokens, shadcn primitives, and reusable patterns into state.ui_audit. Hard gate for the ui directive set.

View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 1 min

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 repo
affaan-m/ECC
Skill path
skills/benchmark/SKILL.md
Commit SHA
4e973d3eaf92
Repository license
MIT
Data collected