Lighthouse Audits

Overview

This public intake copy packages

packages/skills-catalog/skills/(performance)/perf-lighthouse

https://github.com/tech-leads-club/agent-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

Lighthouse Audits

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: CLI Quick Start, Common Flags, Performance Budgets, Node API, GitHub Actions, Lighthouse CI (LHCI).

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when the request clearly matches the imported source intent: Run Lighthouse audits locally via CLI or Node API, parse and interpret reports, and set performance budgets. Use when measuring site performance, understanding Lighthouse scores, setting up budgets, or integrating....
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: CLI Quick Start

# Install
npm install -g lighthouse

# Basic audit
lighthouse https://example.com

# Mobile performance only (faster)
lighthouse https://example.com --preset=perf --form-factor=mobile

# Output JSON for parsing
lighthouse https://example.com --output=json --output-path=./report.json

# Output HTML report
lighthouse https://example.com --output=html --output-path=./report.html

Examples

Example 1: Ask for the upstream workflow directly

Use @perf-lighthouse to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @perf-lighthouse against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @perf-lighthouse for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @perf-lighthouse using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

packages/skills-catalog/skills/(performance)/perf-lighthouse

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Imported Troubleshooting Notes

Imported: Troubleshooting

--number-of-runs=3

CHROME_PATH

--max-wait-for-load=60000

--extra-headers

Related Skills

• @accessibility

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @ai-cold-outreach

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @ai-pricing

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @ai-sdr

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Common Flags

--preset=perf           # Performance only (skip accessibility, SEO, etc.)
--form-factor=mobile    # Mobile device emulation (default)
--form-factor=desktop   # Desktop
--throttling-method=devtools  # More accurate throttling
--only-categories=performance,accessibility  # Specific categories
--chrome-flags="--headless"   # Headless Chrome

Imported: Performance Budgets

Create

budget.json

[
  {
    "resourceSizes": [
      { "resourceType": "script", "budget": 200 },
      { "resourceType": "image", "budget": 300 },
      { "resourceType": "stylesheet", "budget": 50 },
      { "resourceType": "total", "budget": 500 }
    ],
    "resourceCounts": [{ "resourceType": "third-party", "budget": 5 }],
    "timings": [
      { "metric": "interactive", "budget": 3000 },
      { "metric": "first-contentful-paint", "budget": 1500 },
      { "metric": "largest-contentful-paint", "budget": 2500 }
    ]
  }
]

Run with budget:

lighthouse https://example.com --budget-path=./budget.json

Imported: Node API

import lighthouse from 'lighthouse'
import * as chromeLauncher from 'chrome-launcher'

async function runAudit(url) {
  const chrome = await chromeLauncher.launch({ chromeFlags: ['--headless'] })

  const result = await lighthouse(url, {
    port: chrome.port,
    onlyCategories: ['performance'],
    formFactor: 'mobile',
    throttling: {
      cpuSlowdownMultiplier: 4,
    },
  })

  await chrome.kill()

  const { performance } = result.lhr.categories
  const { 'largest-contentful-paint': lcp } = result.lhr.audits

  return {
    score: Math.round(performance.score * 100),
    lcp: lcp.numericValue,
  }
}

Imported: GitHub Actions

# .github/workflows/lighthouse.yml
name: Lighthouse

on:
  pull_request:
  push:
    branches: [main]

jobs:
  lighthouse:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Build site
        run: npm ci && npm run build

      - name: Run Lighthouse
        uses: treosh/lighthouse-ci-action@v11
        with:
          urls: |
            http://localhost:3000
            http://localhost:3000/about
          budgetPath: ./budget.json
          uploadArtifacts: true
          temporaryPublicStorage: true
        env:
          LHCI_GITHUB_APP_TOKEN: ${{ secrets.LHCI_GITHUB_APP_TOKEN }}

Imported: Lighthouse CI (LHCI)

For full CI integration with historical tracking:

# Install
npm install -g @lhci/cli

# Initialize config
lhci wizard

Creates

lighthouserc.js

module.exports = {
  ci: {
    collect: {
      url: ['http://localhost:3000/', 'http://localhost:3000/about'],
      startServerCommand: 'npm run start',
      numberOfRuns: 3,
    },
    assert: {
      assertions: {
        'categories:performance': ['error', { minScore: 0.9 }],
        'categories:accessibility': ['warn', { minScore: 0.9 }],
        'first-contentful-paint': ['error', { maxNumericValue: 1500 }],
        'largest-contentful-paint': ['error', { maxNumericValue: 2500 }],
        'cumulative-layout-shift': ['error', { maxNumericValue: 0.1 }],
      },
    },
    upload: {
      target: 'temporary-public-storage', // or 'lhci' for self-hosted
    },
  },
}

Run:

lhci autorun

Imported: Parse JSON Report

import fs from 'fs'

const report = JSON.parse(fs.readFileSync('./report.json'))

// Overall scores (0-1, multiply by 100 for percentage)
const scores = {
  performance: report.categories.performance.score,
  accessibility: report.categories.accessibility.score,
  seo: report.categories.seo.score,
}

// Core Web Vitals
const vitals = {
  lcp: report.audits['largest-contentful-paint'].numericValue,
  cls: report.audits['cumulative-layout-shift'].numericValue,
  fcp: report.audits['first-contentful-paint'].numericValue,
  tbt: report.audits['total-blocking-time'].numericValue,
}

// Failed audits
const failed = Object.values(report.audits)
  .filter((a) => a.score !== null && a.score < 0.9)
  .map((a) => ({ id: a.id, score: a.score, title: a.title }))

Imported: Compare Builds

# Save baseline
lighthouse https://prod.example.com --output=json --output-path=baseline.json

# Run on PR
lighthouse https://preview.example.com --output=json --output-path=pr.json

# Compare (custom script)
node compare-reports.js baseline.json pr.json

Simple comparison script:

const baseline = JSON.parse(fs.readFileSync(process.argv[2]))
const pr = JSON.parse(fs.readFileSync(process.argv[3]))

const metrics = ['largest-contentful-paint', 'cumulative-layout-shift', 'total-blocking-time']

metrics.forEach((metric) => {
  const base = baseline.audits[metric].numericValue
  const current = pr.audits[metric].numericValue
  const diff = (((current - base) / base) * 100).toFixed(1)
  const emoji = current <= base ? '✅' : '❌'
  console.log(`${emoji} ${metric}: ${diff}% (${base.toFixed(0)} → ${current.toFixed(0)})`)
})