Mapbox for Openclaw

A comprehensive toolset for integrating, debugging, and optimizing Mapbox GL JS and Mapbox APIs within modern web architectures.

jvy
v1.0.0
Mar 21, 2026
1
847
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install mapbox

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install mapbox using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).

Prefer to download?

Get the raw skill files in a ZIP archive.

What is Mapbox?

The Mapbox skill for Openclaw Skills provides developers with a robust framework for implementing high-performance map visualizations and spatial services. It covers everything from basic map rendering using Mapbox GL JS to advanced API integrations for geocoding, directions, and static imagery. By prioritizing token-safe patterns and framework-specific best practices, it ensures that map implementations are secure, performant, and maintainable.

Whether you are working with React, Next.js, or vanilla JavaScript, this skill streamlines the process of managing vector tiles, GeoJSON data, and custom map styles. It addresses common pitfalls like container sizing issues, coordinate order confusion, and memory leaks in Single Page Applications, making it an essential resource for spatial data engineering within the Openclaw Skills ecosystem.

Mapbox Use Cases

  • Developing interactive web maps using Mapbox GL JS across various frameworks like React or Vite.
  • Debugging rendering issues such as blank containers, invisible layers, or incorrect coordinate projections.
  • Implementing spatial features like geocoding, route planning, and dynamic data clustering.
  • Migrating legacy map implementations to modern Mapbox vector tile architectures.
  • Managing complex map styles and source/layer hierarchies for data-heavy visualizations.

How Mapbox Works

  1. Identify the project environment and runtime constraints such as the specific framework or build tools in use.
  2. Define the Mapbox surface required, such as GL JS map rendering, tileset management, or external API calls for geocoding.
  3. Initialize a minimal map instance to verify container height, CSS loading, and access token validity.
  4. Programmatically add data sources (GeoJSON or Vector) before defining visual layers and styling rules to ensure logical dependency.
  5. Apply advanced configurations like data-driven styling, interactive popups, and custom UI controls.
  6. Implement cleanup logic to destroy map instances during component unmounting to prevent memory leaks in the browser.

Mapbox Setup

To get started with this skill in your Openclaw Skills environment, ensure you have your Mapbox access token ready and the necessary packages installed.

npm install mapbox-gl
# Set your access token in your environment configuration
export MAPBOX_ACCESS_TOKEN='your_token_here'

Ensure your map container has a defined height in your CSS to prevent rendering failures:

#map {
  height: 500px;
  width: 100%;
}

Mapbox Data Schema & Taxonomy

The Mapbox skill follows a structured approach to spatial data and configuration management to ensure compatibility across Openclaw Skills integrations:

Component Description Format
Coordinates Geographic location data [longitude, latitude] (WGS84)
Sources Data providers for the map GeoJSON, Vector Tiles, Raster, Image
Layers Visual representation of sources Circle, Line, Fill, Symbol, Heatmap
Tokens Authentication and authorization Scoped public or secret strings
Styles Map design and appearance Mapbox Style Specification (JSON)

Mapbox Advanced Features

  • Automated teardown and memory management protocols for SPA frameworks to ensure high-performance Openclaw Skills deployments.
  • Data-driven styling capabilities for high-performance visualization of massive datasets without performance degradation.
  • Optimized clustering logic for point-based data to improve browser rendering speeds and user experience.
  • Token-safe implementation patterns that prevent the exposure of sensitive credentials in client-side source code.
  • Cross-skill compatibility for switching to specialized GIS tools or 3D globe rendering when project requirements evolve.

SKILL.md


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