Perf Lighthouse logo

Perf Lighthouse

OrganizationPopular
tech-leads-club
perf-lighthouse

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 audits into CI. Triggers on: lighthouse, run lighthouse, lighthouse score, performance audit, performance budget. Do NOT use for fixing specific performance issues (use perf-web-optimization or core-web-vitals) or Astro-specific optimization (use perf-astro).

Overview

Publishertech-leads-club
Repositoryagent-skills
Skill nameperf-lighthouse
Stars
7K
Forks
560
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by tech-leads-club on GitHub. Read the source before you install it.

Installation

Install the Perf Lighthouse AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/tech-leads-club/agent-skills/tree/main/packages/skills-catalog/skills/(performance)/perf-lighthouse
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Perf Lighthouse in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Perf Lighthouse on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Perf Lighthouse is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Lighthouse Audits

CLI Quick Start

bash
# 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

Common Flags

bash
--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

Performance Budgets

Create budget.json:

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:

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

Node API

javascript
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,
  }
}

GitHub Actions

yaml
# .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 }}

Lighthouse CI (LHCI)

For full CI integration with historical tracking:

bash
# Install
npm install -g @lhci/cli

# Initialize config
lhci wizard

Creates lighthouserc.js:

javascript
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:

bash
lhci autorun

Parse JSON Report

javascript
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 }))

Compare Builds

bash
# 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:

javascript
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)})`)
})

Troubleshooting

IssueSolution
Inconsistent scoresRun multiple times (--number-of-runs=3), use median
Chrome not foundSet CHROME_PATH env var
TimeoutsIncrease with --max-wait-for-load=60000
Auth requiredUse --extra-headers or puppeteer script

Frequently asked questions

What does the Perf Lighthouse AI skill do?

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 audits into CI. Triggers on: lighthouse, run lighthouse, lighthouse score, performance audit, performance budget. Do NOT use for fixing specific performance issues (use perf-web-optimization or core-web-vitals) or Astro-specific optimization (use perf-astro).

Why use Perf Lighthouse on TypingMind?

Because you install it once and use it with any model. Perf Lighthouse is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Perf Lighthouse in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tech-leads-club/agent-skills/tree/main/packages/skills-catalog/skills/(performance)/perf-lighthouse. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Perf Lighthouse?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Perf Lighthouse?

As many as you like. As long as a model supports skills, you can use Perf Lighthouse with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Perf Lighthouse AI skill free?

It is published on GitHub by tech-leads-club. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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