webperf-interaction
Intelligent interaction performance analysis with automated workflows for INP debugging, scroll jank investigation, and main…
Intelligent Core Web Vitals analysis with automated workflows and decision trees. Measures LCP, CLS, INP with guided debugging that automatically determines follow-up analysis based on results. Includes workflows for LCP deep dive (5 phases), CLS investigation (loading vs
Claude Code
$ npx -y skills add nucliweb/webperf-snippets --skill webperf-core-web-vitals --agent claude-code
Codex
$ npx skills add nucliweb/webperf-snippets --skill webperf-core-web-vitals -a codex -y
Cursor
$ npx skills add nucliweb/webperf-snippets --skill webperf-core-web-vitals -a cursor -y
Gemini CLI
$ npx skills add nucliweb/webperf-snippets --skill webperf-core-web-vitals -a gemini-cli -y
GitHub Copilot
$ npx skills add nucliweb/webperf-snippets --skill webperf-core-web-vitals -a github-copilot -y
OpenCode
$ npx skills add nucliweb/webperf-snippets --skill webperf-core-web-vitals -a opencode -y
Windsurf
$ npx skills add nucliweb/webperf-snippets --skill webperf-core-web-vitals -a windsurf -y
name: webperf-core-web-vitals description: Intelligent Core Web Vitals analysis with automated workflows and decision trees. Measures LCP, CLS, INP with guided debugging that automatically determines follow-up analysis based on results. Includes workflows for LCP deep dive (5 phases), CLS investigation (loading vs interaction), INP debugging (latency breakdown + attribution), and cross-skill integration with loading, interaction, and media skills. Use when the user asks about Core Web Vitals, LCP optimization, layout shifts, or interaction responsiveness. Compatible with Chrome DevTools MCP. license: MIT metadata: author: Joan Leon | @nucliweb version: 2.6.3 mcp-server: chrome-devtools category: web-performance repository: https://github.com/nucliweb/webperf-snippets
JavaScript snippets for measuring web performance in Chrome DevTools.
1. Run the script with `mcp__chrome-devtools__evaluate_script`, passing the file instead of its code: `sourcePath` with the absolute path of `scripts/<Name>.js` in this skill, and `format: "script"`, since a script runs by itself and is not a function. Read the object it returns. The scripts have no console output, so the returned object is the result: `script`, `status` and the metric data (`value`, `rating`, `details`, `items`, `issues`), as described in `references/schema.md`. 2. When `status` is `"tracking"`, the script keeps observing the page. After the interactions, call the function named in `getDataFn` with another `mcp__chrome-devtools__evaluate_script` call, as an arrow function in `function`, for example `() => getINP()`, and read the object it returns. 3. When `status` is `"error"` or `"unsupported"`, read `error`: the page may not have loaded yet, or the browser lacks the API.
`sourcePath` and `format` need Chrome DevTools MCP 1.11.0 or later, and the server only reads files inside its workspace roots: the project, the temp folder and each `--workspace=<folder>` in its arguments. When it answers `Access denied`, as it does for a skill installed outside the project, such as in `~/.claude/skills/`, read the file and pass its code in `function`, still with `format: "script"`. A `--workspace` with the real folder of the skills makes the path work; the server follows symlinks, so a link to the folder is not enough.
Without Chrome DevTools MCP, run the same snippet with the [webperf-snippets CLI](https://webperf-snippets.net/CLI), which loads the page in headless Chromium and prints the same result objects: `npx webperf-snippets <url> --snippet <Name> --json`, where `<Name>` is the script name without `.js`. It needs Playwright and Chromium (`npm install --save-dev webperf-snippets playwright`, then `npx playwright install chromium`), and tracking snippets take `--interact-script <file>` to drive the interactions.
When the user asks for a comprehensive Core Web Vitals analysis or "audit CWV":
1. **LCP.js** - Measure Largest Contentful Paint 2. **CLS.js** - Measure Cumulative Layout Shift 3. **INP.js** - Measure Interaction to Next Paint 4. **LCP-Subparts.js** - Break down LCP timing phases 5. **LCP-Trail.js** - Track LCP candidate evolution
When LCP is slow or the user asks "debug LCP" or "why is LCP slow":
1. **LCP.js** - Establish baseline LCP value 2. **LCP-Subparts.js** - Break down into TTFB, resource load, render delay 3. **LCP-Trail.js** - Identify all LCP candidates and changes 4. **LCP-Image-Entropy.js** - Check if LCP image has visual complexity issues 5. **LCP-Video-Candidate.js** - Detect if LCP is a video (poster or video element)
When layout shifts are detected or the user asks "debug CLS" or "layout shift issues":
1. **CLS.js** - Measure overall CLS score 2. **Layout-Shift-Loading-and-Interaction.js** _(pending — available in webperf-interaction skill)_ 3. Cross-reference with **webperf-loading** skill:
When interactions feel slow or the user asks "debug INP" or "slow interactions":
1. **INP.js** - Measure overall INP value; call `getINP()` after interactions, `getINPDetails()` for the 15 slowest interactions 2. **Interactions.js** _(pending — available in webperf-interaction skill)_ 3. **Input-Latency-Breakdown.js** _(pending — available in webperf-interaction skill)_ 4. **Long-Animation-Frames.js** _(pending — available in webperf-interaction skill)_ 5. **Long-Animation-Frames-Script-Attribution.js** _(pending — available in webperf-interaction skill)_
When LCP is a video element (detected by LCP-Video-Candidate.js):
1. **LCP-Video-Candidate.js** - Identify video as LCP candidate 2. **Video-Element-Audit.js** (from Media skill) - Audit video loading strategy 3. **LCP-Subparts.js** - Analyze video loading phases 4. Cross-reference with **webperf-loading** skill:
When LCP is an image (most common case):
1. **LCP.js** - Measure LCP timing 2. **LCP-Subparts.js** - Break down timing phases 3. **LCP-Image-Entropy.js** - Analyze image complexity 4. Cross-reference with **webperf-media** skill:
5. Cross-reference with **webperf-loading** skill:
Tested JavaScript snippets that measure and debug web performance. Your AI agent runs them for you, or you run them yourself in DevTools: the same code every time.
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