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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondebug-optimize-lcpExecute the skills CLI command in your project's root directory to begin installation:
Fetches debug-optimize-lcp from chromedevtools/chrome-devtools-mcp and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate debug-optimize-lcp. Access via /debug-optimize-lcp in your agent's command palette.
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.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
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.
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.
Follow these steps in order. Each step builds on the previous one.
Navigate to the page, then record a trace with reload to capture the full page load including LCP:
navigate_page to the target URL.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.
Use performance_analyze_insight to drill into LCP-specific insights. Look for these insight names in the trace results:
Call performance_analyze_insight with the insight set ID and the insight name from the trace results.
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).
Use list_network_requests to see when the LCP resource loaded relative to other resources:
list_network_requests filtered by resourceTypes: ["Image", "Font"] (adjust based on Step 3).get_network_request with the LCP resource's request ID for full details.Key Checks:
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.
After identifying the bottleneck subpart, apply these prioritized fixes.
The most common bottleneck. The LCP resource should start loading immediately.
data-src usage, or loading="lazy".<img> with src. Never lazy-load the LCP image.<link rel="preload" fetchpriority="high"> if the image isn't discoverable in HTML.fetchpriority="high" to the LCP <img> tag.The element should render immediately after loading.
<head>, or main thread blocking.Make the resource smaller or faster to deliver.
srcset).Cache-Control headers.font-display: swap if LCP is text blocked by a web font.The HTML document itself takes too long to arrive.
performance_start_trace with reload: true) and compare the new subpart breakdown. The bottleneck should shrink.emulate to test under constraints:
emulate with networkConditions: "Fast 3G" and cpuThrottlingRate: 4.Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
debug-optimize-lcp has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: debug-optimize-lcp is focused, and the summary matches what you get after install.
Keeps context tight: debug-optimize-lcp is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend debug-optimize-lcp for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in debug-optimize-lcp — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
debug-optimize-lcp reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added debug-optimize-lcp from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: debug-optimize-lcp is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for debug-optimize-lcp matched our evaluation — installs cleanly and behaves as described in the markdown.
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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