Web Perf logo

Web Perf

OrganizationPopular
cloudflare
web-perf

Audit, diagnose, or optimize website loading and interaction performance, Core Web Vitals, and Lighthouse performance scores.

Overview

Publishercloudflare
Repositoryskills
Skill nameweb-perf
Stars
2.8K
Forks
277
Bundled files
Instructions only
LicenseApache-2.0
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 cloudflare on GitHub. Read the source before you install it.

Installation

Install the Web Perf 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.
  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.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cloudflare/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/web-perf .claude/skills/web-perf
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Web Perf 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 Web Perf 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 Web Perf 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.

Web Performance Audit

Your knowledge of web performance metrics, thresholds, and tooling APIs may be outdated. Prefer retrieval over pre-training when citing specific numbers or recommendations.

Retrieval Sources

SourceHow to retrieveUse for
web.devhttps://web.dev/articles/vitalsCore Web Vitals thresholds, definitions
Chrome DevTools docshttps://developer.chrome.com/docs/devtools/performanceTooling APIs, trace analysis
Lighthouse scoringhttps://developer.chrome.com/docs/lighthouse/performance/performance-scoringScore weights, metric thresholds

FIRST: Verify MCP Tools Available

Discover available browser and performance tools before starting. Use the capabilities available for the requested audit. If trace tools are unavailable, continue any useful source or network analysis and state which measurements could not be collected.

If the user wants Chrome DevTools MCP setup, consult its installation guide and use the latest package version. Only change MCP configuration when setup is within the user's authorized scope; otherwise ask first. For clients using command and args, an example server entry is:

json
"chrome-devtools": {
  "command": "npx",
  "args": ["-y", "chrome-devtools-mcp@latest"]
}

Key Guidelines

  • Be assertive: Verify claims by checking network requests, DOM, or codebase—then state findings definitively.
  • Verify before recommending: Confirm something is unused before suggesting removal.
  • Quantify impact: Use estimated savings from insights. Don't prioritize changes with 0ms impact.
  • Skip non-issues: If render-blocking resources have 0ms estimated impact, note but don't recommend action.
  • Be specific: Say "compress hero.png (450KB) to WebP" not "optimize images".
  • Prioritize ruthlessly: A site with 200ms LCP and 0 CLS is already excellent—say so.

Quick Reference

TaskTool Call
Load pagenavigate_page(url: "...")
Start traceperformance_start_trace(autoStop: true, reload: true)
Analyze insightperformance_analyze_insight(insightSetId: "...", insightName: "...")
List requestslist_network_requests(resourceTypes: ["Script", "Stylesheet", ...])
Request detailsget_network_request(reqid: <id>)
A11y snapshottake_snapshot(verbose: true)

Workflow

Copy this checklist to track progress:

Audit Progress:
- [ ] Phase 1: Performance trace (navigate + record)
- [ ] Phase 2: Core Web Vitals analysis (includes CLS culprits)
- [ ] Phase 3: Network analysis
- [ ] Phase 4: Accessibility snapshot
- [ ] Phase 5: Codebase analysis (skip if third-party site)

Phase 1: Performance Trace

  1. Navigate to the target URL:

    navigate_page(url: "<target-url>")
  2. Start a performance trace with reload to capture cold-load metrics:

    performance_start_trace(autoStop: true, reload: true)
  3. Wait for trace completion, then retrieve results.

Troubleshooting:

  • If trace returns empty or fails, verify the page loaded correctly with navigate_page first
  • If insight names don't match, inspect the trace response to list available insights

Phase 2: Core Web Vitals Analysis

Use performance_analyze_insight to extract key metrics.

Note: Insight names may vary across Chrome DevTools versions. If an insight name doesn't work, check the insightSetId from the trace response to discover available insights.

Common insight names:

MetricInsight NameWhat to Look For
LCPLCPBreakdownTime to largest contentful paint; breakdown of TTFB, resource load, render delay
CLSCLSCulpritsElements causing layout shifts (images without dimensions, injected content, font swaps)
Render BlockingRenderBlockingCSS/JS blocking first paint
Document LatencyDocumentLatencyServer response time issues
Network DependenciesNetworkRequestsDepGraphRequest chains delaying critical resources

Example:

performance_analyze_insight(insightSetId: "<id-from-trace>", insightName: "LCPBreakdown")

Key thresholds (good/needs-improvement/poor):

  • TTFB: < 800ms / < 1.8s / > 1.8s
  • FCP: < 1.8s / < 3s / > 3s
  • LCP: < 2.5s / < 4s / > 4s
  • INP: < 200ms / < 500ms / > 500ms
  • TBT: < 200ms / < 600ms / > 600ms
  • CLS: < 0.1 / < 0.25 / > 0.25
  • Speed Index: < 3.4s / < 5.8s / > 5.8s

Phase 3: Network Analysis

List all network requests to identify optimization opportunities:

list_network_requests(resourceTypes: ["Script", "Stylesheet", "Document", "Font", "Image"])

Look for:

  1. Render-blocking resources: JS/CSS in <head> without async/defer/media attributes
  2. Network chains: Resources discovered late because they depend on other resources loading first (e.g., CSS imports, JS-loaded fonts)
  3. Missing preloads: Critical resources (fonts, hero images, key scripts) not preloaded
  4. Caching issues: Missing or weak Cache-Control, ETag, or Last-Modified headers
  5. Large payloads: Uncompressed or oversized JS/CSS bundles
  6. Unused preconnects: If flagged, verify by checking if ANY requests went to that origin. If zero requests, it's definitively unused—recommend removal. If requests exist but loaded late, the preconnect may still be valuable.

For detailed request info:

get_network_request(reqid: <id>)

Phase 4: Accessibility Snapshot

Take an accessibility tree snapshot:

take_snapshot(verbose: true)

Flag high-level gaps:

  • Missing or duplicate ARIA IDs
  • Elements with poor contrast ratios (check against WCAG AA: 4.5:1 for normal text, 3:1 for large text)
  • Focus traps or missing focus indicators
  • Interactive elements without accessible names

Phase 5: Codebase Analysis

Skip if auditing a third-party site without codebase access.

Analyze the codebase to understand where improvements can be made.

Detect Framework & Bundler

Search for configuration files to identify the stack:

ToolConfig Files
Webpackwebpack.config.js, webpack.*.js
Vitevite.config.js, vite.config.ts
Rolluprollup.config.js, rollup.config.mjs
esbuildesbuild.config.js, build scripts with esbuild
Parcel.parcelrc, package.json (parcel field)
Next.jsnext.config.js, next.config.mjs
Nuxtnuxt.config.js, nuxt.config.ts
SvelteKitsvelte.config.js
Astroastro.config.mjs

Also check package.json for framework dependencies and build scripts.

Tree-Shaking & Dead Code

  • Webpack: Check for mode: 'production', sideEffects in package.json, usedExports optimization
  • Vite/Rollup: Tree-shaking enabled by default; check for treeshake options
  • Look for: Barrel files (index.js re-exports), large utility libraries imported wholesale (lodash, moment)

Unused JS/CSS

  • Check for CSS-in-JS vs. static CSS extraction
  • Look for PurgeCSS/UnCSS configuration (Tailwind's content config)
  • Identify dynamic imports vs. eager loading

Polyfills

  • Check for @babel/preset-env targets and useBuiltIns setting
  • Look for core-js imports (often oversized)
  • Check browserslist config for overly broad targeting

Compression & Minification

  • Check for terser, esbuild, or swc minification
  • Look for gzip/brotli compression in build output or server config
  • Check for source maps in production builds (should be external or disabled)

Output Format

Present findings as:

  1. Core Web Vitals Summary - Table with metric, value, and rating (good/needs-improvement/poor)
  2. Top Issues - Prioritized list of problems with estimated impact (high/medium/low)
  3. Recommendations - Specific, actionable fixes with code snippets or config changes
  4. Codebase Findings - Framework/bundler detected, optimization opportunities (omit if no codebase access)

Frequently asked questions

What does the Web Perf AI skill do?

Audit, diagnose, or optimize website loading and interaction performance, Core Web Vitals, and Lighthouse performance scores.

Why use Web Perf on TypingMind?

Because you install it once and use it with any model. Web Perf 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 Web Perf in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cloudflare/skills/tree/main/skills/web-perf. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Web Perf?

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 Web Perf?

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

Is the Web Perf AI skill free?

Yes. It is published on GitHub by cloudflare under the Apache-2.0 license. 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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