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debug-optimize-lcp▌

chromedevtools/chrome-devtools-mcp · updated May 19, 2026

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$npx skills add https://github.com/chromedevtools/chrome-devtools-mcp --skill debug-optimize-lcp
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summary

Largest Contentful Paint (LCP) measures how quickly a page's main content becomes visible. It's the time from navigation start until the largest image or text block renders in the viewport.

skill.md

What is LCP and why it matters

Largest Contentful Paint (LCP) measures how quickly a page's main content becomes visible. It's the time from navigation start until the largest image or text block renders in the viewport.

  • Good: 2.5 seconds or less
  • Needs improvement: 2.5–4.0 seconds
  • Poor: greater than 4.0 seconds

LCP is a Core Web Vital that directly affects user experience and search ranking. On 73% of mobile pages, the LCP element is an image.

LCP Subparts Breakdown

Every page's LCP breaks down into four sequential subparts with no gaps or overlaps. Understanding which subpart is the bottleneck is the key to effective optimization.

Subpart Ideal % of LCP What it measures
Time to First Byte (TTFB) ~40% Navigation start → first byte of HTML received
Resource load delay <10% TTFB → browser starts loading the LCP resource
Resource load duration ~40% Time to download the LCP resource
Element render delay <10% LCP resource downloaded → LCP element rendered

The "delay" subparts should be as close to zero as possible. If either delay subpart is large relative to the total LCP, that's the first place to optimize.

Common Pitfall: Optimizing one subpart (like compressing an image to reduce load duration) without checking others. If render delay is the real bottleneck, a smaller image won't help — the saved time just shifts to render delay.

Debugging Workflow

Follow these steps in order. Each step builds on the previous one.

Step 1: Record a Performance Trace

Navigate to the page, then record a trace with reload to capture the full page load including LCP:

  1. navigate_page to the target URL.
  2. performance_start_trace with reload: true and autoStop: true.

The trace results will include LCP timing and available insight sets. Note the insight set IDs from the output — you'll need them in the next step.

Step 2: Analyze LCP Insights

Use performance_analyze_insight to drill into LCP-specific insights. Look for these insight names in the trace results:

  • LCPBreakdown — Shows the four LCP subparts with timing for each.
  • DocumentLatency — Server response time issues affecting TTFB.
  • RenderBlocking — Resources blocking the LCP element from rendering.
  • LCPDiscovery — Whether the LCP resource was discoverable early.

Call performance_analyze_insight with the insight set ID and the insight name from the trace results.

Step 3: Identify the LCP Element

Use evaluate_script with the "Identify LCP Element" snippet found in references/lcp-snippets.md to reveal the LCP element's tag, resource URL, and raw timing data.

The url field tells you what resource to look for in the network waterfall. If url is empty, the LCP element is text-based (no resource to load).

Step 4: Check the Network Waterfall

Use list_network_requests to see when the LCP resource loaded relative to other resources:

  • Call list_network_requests filtered by resourceTypes: ["Image", "Font"] (adjust based on Step 3).
  • Then use get_network_request with the LCP resource's request ID for full details.

Key Checks:

  • Start Time: Compare against the HTML document and the first resource. If the LCP resource starts much later than the first resource, there's resource load delay to eliminate.
  • Duration: A large resource load duration suggests the file is too big or the server is slow.

Step 5: Inspect HTML for Common Issues

Use evaluate_script with the "Audit Common Issues" snippet found in references/lcp-snippets.md to check for lazy-loaded images in the viewport, missing fetchpriority, and render-blocking scripts.

Optimization Strategies

After identifying the bottleneck subpart, apply these prioritized fixes.

1. Eliminate Resource Load Delay (target: <10%)

The most common bottleneck. The LCP resource should start loading immediately.

  • Root Cause: LCP image loaded via JS/CSS, data-src usage, or loading="lazy".
  • Fix: Use standard <img> with src. Never lazy-load the LCP image.
  • Fix: Add <link rel="preload" fetchpriority="high"> if the image isn't discoverable in HTML.
  • Fix: Add fetchpriority="high" to the LCP <img> tag.

2. Eliminate Element Render Delay (target: <10%)

The element should render immediately after loading.

  • Root Cause: Large stylesheets, synchronous scripts in <head>, or main thread blocking.
  • Fix: Inline critical CSS, defer non-critical CSS/JS.
  • Fix: Break up long tasks blocking the main thread.
  • Fix: Use Server-Side Rendering (SSR) so the element exists in initial HTML.

3. Reduce Resource Load Duration (target: ~40%)

Make the resource smaller or faster to deliver.

  • Fix: Use modern formats (WebP, AVIF) and responsive images (srcset).
  • Fix: Serve from a CDN.
  • Fix: Set Cache-Control headers.
  • Fix: Use font-display: swap if LCP is text blocked by a web font.

4. Reduce TTFB (target: ~40%)

The HTML document itself takes too long to arrive.

  • Fix: Minimize redirects and optimize server response time.
  • Fix: Cache HTML at the edge (CDN).
  • Fix: Ensure pages are eligible for back/forward cache (bfcache).

Verifying Fixes & Emulation

  • Verification: Re-run the trace (performance_start_trace with reload: true) and compare the new subpart breakdown. The bottleneck should shrink.
  • Emulation: Lab measurements differ from real-world experience. Use emulate to test under constraints:
    • emulate with networkConditions: "Fast 3G" and cpuThrottlingRate: 4.
    • This surfaces issues visible only on slower connections/devices.
how to use debug-optimize-lcp

How to use debug-optimize-lcp on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • ›Cursor installed and configured on your development machine
  • ›Node.js version 16.0+ with npm package manager (verify with node --version)
  • ›Active project directory or workspace where you want to add debug-optimize-lcp
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/chromedevtools/chrome-devtools-mcp --skill debug-optimize-lcp

The skills CLI fetches debug-optimize-lcp from GitHub repository chromedevtools/chrome-devtools-mcp and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/debug-optimize-lcp

Reload or restart Cursor to activate debug-optimize-lcp. Access the skill through slash commands (e.g., /debug-optimize-lcp) or your agent's skill management interface.

⚠

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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Use Cases▌

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

✓

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

✓

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

✓

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

✓

Save 3-5 hours/week on communication overhead

Implementation Guide▌

Prerequisites

  • ›Claude Desktop or compatible AI client
  • ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • ›Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • ⚠Not validating competitive research—verify facts before sharing
  • ⚠Accepting user stories without involving engineering team
  • ⚠Over-relying on frameworks without qualitative judgment
  • ⚠Not customizing outputs to company culture and communication style
  • ⚠Skipping stakeholder validation of generated requirements

Best Practices▌

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • −Don't publish competitive analysis without fact-checking
  • −Don't finalize user stories without engineering review
  • −Don't make prioritization decisions solely on AI scoring
  • −Don't skip customer validation of generated requirements
  • −Don't ignore company-specific context and culture

💡 Pro Tips

  • ★Provide context: company goals, constraints, customer feedback
  • ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • ★Use skill for 70% generation + 30% customization to company needs

When to Use This▌

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path▌

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.5★★★★★66 reviews
  • ★★★★★Hassan Haddad· Dec 12, 2024

    debug-optimize-lcp has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Advait Gill· Dec 8, 2024

    Solid pick for teams standardizing on skills: debug-optimize-lcp is focused, and the summary matches what you get after install.

  • ★★★★★Jin Perez· Dec 4, 2024

    Keeps context tight: debug-optimize-lcp is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★Meera Ghosh· Nov 27, 2024

    I recommend debug-optimize-lcp for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Rahul Santra· Nov 23, 2024

    Useful defaults in debug-optimize-lcp — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Zaid Martin· Nov 3, 2024

    debug-optimize-lcp reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Meera Perez· Oct 22, 2024

    We added debug-optimize-lcp from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Maya Reddy· Oct 18, 2024

    Keeps context tight: debug-optimize-lcp is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • ★★★★★Pratham Ware· Oct 14, 2024

    Registry listing for debug-optimize-lcp matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Mei Anderson· Sep 25, 2024

    Solid pick for teams standardizing on skills: debug-optimize-lcp is focused, and the summary matches what you get after install.

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