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Webperf Resources

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nucliweb
webperf-resources

Intelligent network quality analysis with adaptive loading strategies. Detects connection type (2g/3g/4g), bandwidth, RTT, and save-data mode, then automatically triggers appropriate optimization workflows. Includes decision trees that recommend image compression for slow connections, critical CSS inlining for high RTT, and save-data optimizations (disable autoplay, reduce quality). Features connection-aware performance budgets (500KB for 2g, 1.5MB for 3g, 3MB for 4g+) and adaptive loading implementation guides. Cross-skill integration with Loading (TTFB impact), Media (responsive images), and Core Web Vitals (connection impact on LCP/INP). Use when the user asks about slow connections, mobile optimization, save-data support, or adaptive loading strategies. Compatible with Chrome DevTools MCP.

Overview

Publishernucliweb
Repositorywebperf-snippets
Skill namewebperf-resources
Stars
1.5K
Forks
90
Bundled files
3
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • 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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by nucliweb on GitHub. Read the source before you install it.

Installation

Install the Webperf Resources 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.
    https://github.com/nucliweb/webperf-snippets/tree/main/.claude/skills/webperf-resources
  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/nucliweb/webperf-snippets.git /tmp/webperf-snippets
mkdir -p .claude/skills
cp -r /tmp/webperf-snippets/.claude/skills/webperf-resources .claude/skills/webperf-resources
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Webperf Resources 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 Webperf Resources 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 Webperf Resources 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.

WebPerf: Resources & Network

JavaScript snippets for measuring web performance in Chrome DevTools.

Running a script

  1. Run the script with mcp__chrome-devtools__evaluate_script and 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, 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.

Without Chrome DevTools MCP, run the same snippet with the webperf-snippets 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.

Scripts

  • scripts/Network-Bandwidth-Connection-Quality.js — Network bandwidth & connection quality

Common Workflows

Network Quality Assessment

When the user asks about network performance, connection quality, or adaptive loading:

  1. Network-Bandwidth-Connection-Quality.js - Analyze network bandwidth, effective connection type, RTT, downlink, save-data mode

Adaptive Loading Strategy

When implementing adaptive loading or the user asks "how to optimize for slow connections":

  1. Network-Bandwidth-Connection-Quality.js - Detect connection quality
  2. Cross-reference with webperf-loading skill:
    • TTFB.js (measure server response on slow connections)
    • Find-render-blocking-resources.js (identify heavy resources to defer)
    • Resource-Hints-Validation.js (optimize preconnect for slow networks)
  3. Cross-reference with webperf-media skill:
    • Image-Element-Audit.js (implement responsive images based on connection)
    • Video-Element-Audit.js (adjust video quality based on connection)

Save-Data Mode Detection

When the user asks about save-data or data-saving features:

  1. Network-Bandwidth-Connection-Quality.js - Check if save-data is enabled
  2. If save-data is enabled, recommend:
    • Reducing image quality
    • Disabling autoplay videos
    • Deferring non-critical resources
    • Using low-res thumbnails

Mobile/Slow Connection Optimization

When the user asks "optimize for mobile" or "slow connection users":

  1. Network-Bandwidth-Connection-Quality.js - Assess connection type
  2. Cross-reference with webperf-loading skill:
    • TTFB.js (critical for slow connections)
    • Find-render-blocking-resources.js (minimize blocking on slow networks)
    • Critical-CSS-Detection.js (inline critical CSS to reduce RTT)
    • Prefetch-Resource-Validation.js (avoid excessive prefetch on slow connections)

Decision Tree

Use this decision tree to automatically run follow-up snippets based on results:

After Network-Bandwidth-Connection-Quality.js

  • If effectiveType is "slow-2g" or "2g" → Very slow connection, recommend:

    1. Run webperf-loading:Critical-CSS-Detection.js (inline critical CSS)
    2. Run webperf-media:Image-Element-Audit.js (implement aggressive lazy loading)
    3. Run webperf-loading:Prefetch-Resource-Validation.js (remove prefetch to save bandwidth)
    4. Run webperf-core-web-vitals:LCP.js (LCP is heavily impacted by slow connections)
    5. Recommend minimal resource strategy

  • If effectiveType is "3g" → Moderate connection, recommend:

    1. Run webperf-loading:Find-render-blocking-resources.js (minimize blocking)
    2. Run webperf-media:Image-Element-Audit.js (responsive images)
    3. Run webperf-loading:Resource-Hints-Validation.js (optimize preconnect)
    4. Run webperf-core-web-vitals:INP.js (high latency can impact interaction responsiveness)
    5. Implement adaptive image quality

  • If effectiveType is "4g" or better → Good connection, recommend:

    1. Standard optimization practices
    2. Consider strategic prefetch for navigation
    3. Higher quality media is acceptable

  • If save-data is enabled → User explicitly wants to save data, recommend:

    1. Reduce image quality aggressively
    2. Disable autoplay videos
    3. Defer non-critical resources
    4. Remove prefetch/preload hints
    5. Show "high quality" toggle option

  • If RTT > 300ms → High latency, recommend:

    1. Run webperf-loading:TTFB.js (latency impacts TTFB)
    2. Run webperf-loading:TTFB-Sub-Parts.js (break down latency components)
    3. Run webperf-loading:Resource-Hints-Validation.js (preconnect critical for high RTT)
    4. Run webperf-loading:Service-Worker-Analysis.js (caching is critical for high latency)
    5. Minimize number of origins
    6. Use HTTP/2 or HTTP/3 for multiplexing

  • If downlink < 1 Mbps → Very limited bandwidth, recommend:

    1. Run webperf-media:Image-Element-Audit.js (aggressive compression)
    2. Run webperf-media:Video-Element-Audit.js (disable autoplay)
    3. Run webperf-loading:Prefetch-Resource-Validation.js (remove prefetch)
    4. Implement bandwidth-aware loading

  • If downlink > 10 Mbps → Good bandwidth, consider:

    • Higher quality media
    • Strategic prefetch
    • Preloading next-page resources

Adaptive Loading Implementation Guide

Based on Network Information API results, implement these strategies:

For slow-2g / 2g (< 50 Kbps): (effectiveType: "slow-2g" or "2g")

  • Serve low-res images (quality: 30-40)
  • Disable autoplay videos
  • Remove all prefetch hints
  • Inline all critical CSS
  • Defer all non-critical JavaScript
  • Use system fonts (no webfonts)
  • Aggressive lazy loading (load on scroll + buffer)

For 3g (50-700 Kbps): (effectiveType: "3g")

  • Serve medium-res images (quality: 60-70)
  • Disable autoplay videos
  • Limited prefetch (critical only)
  • Inline critical CSS only
  • Defer non-critical JavaScript
  • Preload 1-2 critical fonts
  • Standard lazy loading

For 4g+ (> 700 Kbps): (effectiveType: "4g")

  • Serve high-res images (quality: 80-90)
  • Allow autoplay videos (muted)
  • Strategic prefetch for navigation
  • Standard CSS loading and JavaScript loading
  • Preload critical fonts
  • Standard lazy loading

For save-data mode: (navigator.connection.saveData === true)

  • Override connection type, treat as worse than actual
  • Show "Load high quality" toggle
  • Disable autoplay entirely
  • Minimal images, minimal quality
  • No prefetch, no preload (except critical)

Performance Budget by Connection Type

Adjust performance budgets based on connection quality:

slow-2g / 2g:

  • Total page weight: < 500KB
  • Images: < 200KB total
  • JavaScript: < 100KB total
  • No videos

3g:

  • Total page weight: < 1.5MB
  • Images: < 800KB total
  • JavaScript: < 300KB total
  • Videos: < 3MB (only if critical)

4g+:

  • Total page weight: < 3MB
  • Images: < 2MB total
  • JavaScript: < 1MB total
  • Videos: < 10MB

Network Information API Limitations

Be aware of API limitations and fallbacks:

API not available:

  • Safari does not support Network Information API
  • Fallback: Use TTFB as proxy for connection quality
  • Fallback: Device detection (mobile = assume slow)
  • Fallback: User preference toggle

API values are estimates:

  • effectiveType is based on recent observations
  • Values can change during session
  • Re-check periodically for long sessions

Privacy considerations:

  • Some browsers limit precision for privacy
  • Values may be rounded or capped
  • Consider user privacy when making decisions

References

  • references/snippets.md — Descriptions and thresholds for each script
  • references/schema.md — Return value schema for interpreting script output

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Webperf Resources AI skill do?

Intelligent network quality analysis with adaptive loading strategies. Detects connection type (2g/3g/4g), bandwidth, RTT, and save-data mode, then automatically triggers appropriate optimization workflows. Includes decision trees that recommend image compression for slow connections, critical CSS inlining for high RTT, and save-data optimizations (disable autoplay, reduce quality). Features connection-aware performance budgets (500KB for 2g, 1.5MB for 3g, 3MB for 4g+) and adaptive loading implementation guides. Cross-skill integration with Loading (TTFB impact), Media (responsive images),...

Why use Webperf Resources on TypingMind?

Because you install it once and use it with any model. Webperf Resources 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 Webperf Resources in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nucliweb/webperf-snippets/tree/main/.claude/skills/webperf-resources. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Webperf Resources?

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 Webperf Resources?

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

Is the Webperf Resources AI skill free?

Yes. It is published on GitHub by nucliweb under the MIT 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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