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d-padmanabhand-padmanabhan/agent-engineering-handbook/skills/web-perf/SKILL.md

web-perf

Skill17 starsChanged 56 days agoMITView on GitHub

Analyzes web performance using Chrome DevTools MCP. Measures Core Web Vitals (LCP, INP, CLS) and supplementary metrics (FCP, TBT, Speed Index), identifies render-blocking resources, network dependency chains, layout shifts, caching issues, and accessibility gaps. Use when asked to audit, profile, debug, or optimize page load performance, Lighthouse scores, or site speed. Biases towards retrieval from current documentation over pre-trained knowledge.

43 other projects use a version of this, 40 of them edited.

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The file

Show its rules and text

We split this skill into the rules it gives an agent

20 rules. 12 are common, 0 are distinctive, and 8 are very specific to this project.

  • 12

    Common

    Most of the 42 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 8

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Common:

    Ask the user to add this to their MCP config:

    line 22
  2. Common:

    Be assertive: Verify claims by checking network requests, DOM, or codebase, then state findings definitively.

    line 33
  3. Common:

    Quantify impact: Use estimated savings from insights.

    line 35
  4. Common:

    Skip non-issues: If render-blocking resources have 0ms estimated impact, note but don't recommend action.

    line 36
  5. Common:

    Start a performance trace with reload to capture cold-load metrics:

    line 72
  6. Common:

    Wait for trace completion, then retrieve results.

    line 78
  7. Very specific:

    If trace returns empty or fails, verify the page loaded correctly with navigate_page first

    line 82
  8. Common:

    If insight names don't match, inspect the trace response to list available insights

    line 83
  9. Very specific:

    Use performance_analyze_insight to extract key metrics.

    line 87
  10. Common:

    Unused preconnects: If flagged, verify by checking if ANY requests went to that origin.

    line 132
  11. Common:

    Search for configuration files to identify the stack:

    line 163
  12. Very specific:

    Webpack: Check for mode: 'production', sideEffects in package.json, usedExports optimization

    line 181
  13. Common:

    Check for CSS-in-JS vs. static CSS extraction

    line 187
  14. Very specific:

    Look for PurgeCSS/UnCSS configuration (Tailwind's content config)

    line 188
  15. Very specific:

    Check for @babel/preset-env targets and useBuiltIns setting

    line 193
  16. Very specific:

    Look for core-js imports (often oversized)

    line 194
  17. Very specific:

    Check browserslist config for overly broad targeting

    line 195
  18. Very specific:

    Check for terser, esbuild, or swc minification

    line 199
  19. Common:

    Look for gzip/brotli compression in build output or server config

    line 200
  20. Common:

    Check for source maps in production builds (should be external or disabled)

    line 201

“Common” is measured: kept by at least 6 in 10 of the 42 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

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

Run this before starting. Try calling navigate_page or performance_start_trace. If unavailable, stop: the chrome-devtools MCP server isn't configured.

Ask the user to add this to their MCP config:

"chrome-devtools": {
  "type": "local",
  "command": ["npx", "-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)

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