mapbox-web-performance-patterns

mapbox/mapbox-agent-skills · updated Apr 8, 2026

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$npx skills add https://github.com/mapbox/mapbox-agent-skills --skill mapbox-web-performance-patterns
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summary

Performance optimization patterns for Mapbox GL JS applications, prioritized by user impact.

  • Eliminate initialization waterfalls by loading map data in parallel with map initialization, and set precise viewport to avoid redundant tile fetches
  • Use symbol layers for 100+ markers (GPU-accelerated) and clustering for 10,000+ features; avoid HTML markers at scale
  • Choose GeoJSON for datasets under 5 MB, vector tiles for 20+ MB; implement viewport-based loading to reduce bandwidth
  • Always
skill.md

Mapbox Performance Patterns Skill

This skill provides performance optimization guidance for building fast, efficient Mapbox applications. Patterns are prioritized by impact on user experience, starting with the most critical improvements.

Performance philosophy: These aren't micro-optimizations. They show up as waiting time, jank, and repeat costs that hit every user session.

Priority Levels

Performance issues are prioritized by their impact on user experience:

  • 🔴 Critical (Fix First): Directly causes slow initial load or visible jank
  • 🟡 High Impact: Noticeable delays or increased resource usage
  • 🟢 Optimization: Incremental improvements for polish

🔴 Critical: Eliminate Initialization Waterfalls

Problem: Sequential loading creates cascading delays where each resource waits for the previous one.

Note: Modern bundlers (Vite, Webpack, etc.) and ESM dynamic imports automatically handle code splitting and library loading. The primary waterfall to eliminate is data loading - fetching map data sequentially instead of in parallel with map initialization.

Anti-Pattern: Sequential Data Loading

// ❌ BAD: Data loads AFTER map initializes
async function initMap() {
  const map = new mapboxgl.Map({
    container: 'map',
    accessToken: MAPBOX_TOKEN,
    style: 'mapbox://styles/mapbox/streets-v12'
  });

  // Wait for map to load, THEN fetch data
  map.on('load', async () => {
    const data = await fetch('/api/data'); // Waterfall!
    map.addSource('data', { type: 'geojson', data: await data.json() });
  });
}

Timeline: Map init (0.5s) → Data fetch (1s) = 1.5s total

Solution: Parallel Data Loading

// ✅ GOOD: Data fetch starts immediately
async function initMap() {
  // Start data fetch immediately (don't wait for map)
  const dataPromise = fetch('/api/data').then((r) => r.json());

  const map = new mapboxgl.Map({
    container: 'map',
    accessToken: MAPBOX_TOKEN,
    style: 'mapbox://styles/mapbox/streets-v12'
  });

  // Data is ready when map loads
  map.on('load', async () => {
    const data = await dataPromise;
    map.addSource('data', { type: 'geojson', data });
    map.addLayer({
      id: 'data-layer',
      type: 'circle',
      source: 'data'
    });
  });
}

Timeline: Max(map init, data fetch) = ~1s total

Set Precise Initial Viewport

// ✅ Set exact center/zoom so the map fetches the right tiles immediately
const map = new mapboxgl.Map({
  container: 'map',
  style: 'mapbox://styles/mapbox/streets-v12',
  center: [-122.4194, 37.7749],
  zoom: 13
});

// Use 'idle' to know when the initial viewport is fully rendered
// (all tiles, sprites, and other resources are loaded; no transitions in progress)
map.once('idle', () => {
  console.log('Initial viewport fully rendered');
});

If you know the exact area users will see first, setting center and zoom upfront avoids the map starting at a default view and then panning/zooming to the target, which wastes tile fetches.

Defer Non-Critical Features

// ✅ Load critical features first, defer others
const map = new mapboxgl.Map({
  /* config */
});

map.on('load', () => {
  // 1. Add critical layers immediately
  addCriticalLayers(map);

  // 2. Defer secondary features
  // Note: Standard style 3D buildings can be toggled via config:
  // map.setConfigProperty('basemap', 'show3dObjects', false);
  requestIdleCallback(
    () => {
      addTerrain(map);
      addCustom3DLayers(map); // For classic styles with custom fill-extrusion layers
    },
    { timeout: 2000 }
  );

  // 3. Defer analytics and non-visual features
  setTimeout(() => {
    initializeAnalytics(map);
  }, 3000);
});

Impact: Significant reduction in time-to-interactive, especially when deferring terrain and 3D layers


🔴 Critical: Optimize Initial Bundle Size

Problem: Large bundles delay time-to-interactive on slow networks.

Note: Modern bundlers (Vite, Webpack, etc.) automatically handle code splitting for framework-based applications. The guidance below is most relevant for optimizing what gets bundled and when.

Style JSON Bundle Impact

// ❌ BAD: Inline massive style JSON (can be 500+ KB)
const style = {
  version: 8,
  sources: {
    /* 100s of lines */
  },
  layers: [
    /* 100s of layers */
  ]
};

// ✅ GOOD: Reference Mapbox-hosted styles
const map = new mapboxgl.Map({
  style: 'mapbox://styles/mapbox/streets-v12' // Fetched on demand
});

// ✅ OR: Store large custom styles externally
const map = new mapboxgl.Map({
  style: '/styles/custom-style.json' // Loaded separately
});

Impact: Reduces initial bundle by 30-50% when moving from inlined to hosted styles


🟡 High Impact: Optimize Marker Count

Problem: Too many markers causes slow rendering and interaction lag.

Performance Thresholds

  • < 100 markers: HTML markers OK (Marker class)
  • 100-10,000 markers: Use symbol layers (GPU-accelerated)
  • 10,000+ markers: Clustering recommended
  • 100,000+ markers: Vector tiles with server-side clustering

Anti-Pattern: Thousands of HTML Markers

// ❌ BAD: 5,000 HTML markers = 5+ second render, janky pan/zoom
restaurants.forEach((restaurant) => {
  const marker = new mapboxgl.Marker()
    .setLngLat([restaurant.lng, restaurant.lat]
how to use mapbox-web-performance-patterns

How to use mapbox-web-performance-patterns 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 mapbox-web-performance-patterns
2

Execute installation command

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

$npx skills add https://github.com/mapbox/mapbox-agent-skills --skill mapbox-web-performance-patterns

The skills CLI fetches mapbox-web-performance-patterns from GitHub repository mapbox/mapbox-agent-skills 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/mapbox-web-performance-patterns

Reload or restart Cursor to activate mapbox-web-performance-patterns. Access the skill through slash commands (e.g., /mapbox-web-performance-patterns) 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.

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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)
  • No comments yet — start the thread.
general reviews

Ratings

4.540 reviews
  • Anaya Abbas· Dec 28, 2024

    mapbox-web-performance-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Ganesh Mohane· Dec 24, 2024

    mapbox-web-performance-patterns fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Anika Gill· Dec 20, 2024

    Useful defaults in mapbox-web-performance-patterns — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Shikha Mishra· Dec 16, 2024

    Useful defaults in mapbox-web-performance-patterns — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Mateo Torres· Dec 8, 2024

    We added mapbox-web-performance-patterns from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Noah Singh· Dec 8, 2024

    Registry listing for mapbox-web-performance-patterns matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Anaya Ramirez· Nov 27, 2024

    Keeps context tight: mapbox-web-performance-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Kwame Martinez· Nov 11, 2024

    mapbox-web-performance-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Yash Thakker· Nov 7, 2024

    mapbox-web-performance-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Dhruvi Jain· Oct 26, 2024

    Solid pick for teams standardizing on skills: mapbox-web-performance-patterns is focused, and the summary matches what you get after install.

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