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Geospatial Frontend

Community
isaaccorley
geospatial-frontend

Build visually stunning geospatial demo webapps with MapLibre globe, DuckDB-WASM, and a warm dark HUD aesthetic. Use when building satellite imagery retrieval demos, embedding visualization tools, geospatial search UIs, or any map-centric data exploration frontend. Triggers on requests to scaffold a new geo demo, build a map-based UI, or create a retrieval/search visualization over satellite data.

Overview

Publisherisaaccorley
Repositorygeospatial-skills
Skill namegeospatial-frontend
Stars
80
Forks
12
Bundled files
2
LicenseApache-2.0
Links
  • 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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Geospatial Frontend 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/isaaccorley/geospatial-skills/tree/main/plugins/geospatial-frontend/skills/geospatial-frontend
  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/isaaccorley/geospatial-skills.git /tmp/geospatial-skills
mkdir -p .claude/skills
cp -r /tmp/geospatial-skills/plugins/geospatial-frontend/skills/geospatial-frontend .claude/skills/geospatial-frontend
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Geospatial Frontend 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 Geospatial Frontend 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 Geospatial Frontend 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.

Geospatial Frontend Demo Builder

Build premium map-centric demo webapps. Derived from a production geospatial retrieval demo with battle-tested design patterns.

Architecture

  • Stack: Vite + vanilla TypeScript + MapLibre GL JS (globe) + DuckDB-WASM + pure CSS
  • No frameworks: No React, Vue, Tailwind, CSS-in-JS. Pure semantic HTML + CSS custom properties + vanilla TS.
  • Map-first: The globe IS the app. Everything else floats on top as a transparent HUD.
  • Single source of truth: Global state object; updateView() re-renders; fingerprint diffing prevents redundant DOM rebuilds.

Design System

See references/design-system.md for full token spec (colors, fonts, shadows, spacing, radii, animations, components).

Core identity: Warm espresso + ivory + rust. Dark mode only. Glassmorphed floating panels. Inspired by Anthropic Salon aesthetic.

UI Rules

See references/user-preferences.md for battle-tested patterns, confirmed features, and design rules.

Layout

viewport (100vw x 100vh, fixed)
  ::before  starfield
  ::after   vignette
  #map      MapLibre globe canvas
  brand     top-left (spinning rings + logotype)
  search    top-center (glassmorphed geocoder)
  status    top-center-below (pulsing LED + message)
  panel-L   left (query: draw controls, exemplars, results)
  panel-R   right (explore/discoveries, optional)
  tutorial  overlay + spotlight + card (z:1000+)

Panels are position: fixed, glassmorphed, with generous padding. Map fills entire viewport underneath.

Map Integration

  • MapLibre GL JS with projection: "globe" (3D globe, not flat)
  • Sentinel-2 cloudless tiles (or similar satellite basemap)
  • Layer stack: AOI fill/stroke -> draft layers -> result fill/stroke -> point circles with halos -> hover preview
  • Drawing: rectangle (shift+drag with DOM overlay) + polygon (click-to-add, double-click-to-close)
  • Fly-to animations: globe.flyTo() with curve 1.4, speed 1.2
  • GeoJSON sources: all data as GeoJSON, expression-based paint properties for dynamic styling

Data Pipeline Pattern

  • DuckDB-WASM loads Parquet manifest + shards
  • Spatial filtering via ST_Intersects or bbox containment
  • Web Worker for heavy compute (embedding distances, scoring)
  • Concurrency-limited parallel shard fetches
  • Two-phase data loading: metadata first, embeddings on demand

Tutorial System

Interactive 5-7 step spotlight walkthrough that SIMULATES real actions:

  • Box-shadow spotlight (0 0 0 9999px rgba(...)) cuts a hole over target element
  • Card auto-positioned via getBoundingClientRect(), glass-panel styled
  • Simulated clicks with animated ripple effect
  • Enter key advances; auto-place exemplars if user skips ahead
  • Store completion in localStorage
  • Must be a LIVE demo -- simulate zoom, draw, exemplar placement, view modes

Responsive

Two breakpoints only:

  • 1100px: shrink panels, hide secondary nav
  • 900px: panels collapse to bottom-center stack, max-height 40-45vh

File Organization

Keep files under ~500 LOC. Split by concern:

  • main.ts -- app state, DOM, event handlers, tutorial
  • map.ts -- MapLibre setup, layers, drawing, camera
  • types.ts -- interfaces
  • util.ts -- geometry, color interpolation, formatting
  • scoring-worker.ts -- Web Worker for compute
  • styles.css -- all CSS (pure, no preprocessor)
  • index.html -- entry with font preloads + meta tags

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 Geospatial Frontend AI skill do?

Build visually stunning geospatial demo webapps with MapLibre globe, DuckDB-WASM, and a warm dark HUD aesthetic. Use when building satellite imagery retrieval demos, embedding visualization tools, geospatial search UIs, or any map-centric data exploration frontend. Triggers on requests to scaffold a new geo demo, build a map-based UI, or create a retrieval/search visualization over satellite data.

Why use Geospatial Frontend on TypingMind?

Because you install it once and use it with any model. Geospatial Frontend 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 Geospatial Frontend in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/isaaccorley/geospatial-skills/tree/main/plugins/geospatial-frontend/skills/geospatial-frontend. 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 Geospatial Frontend?

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 Geospatial Frontend?

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

Is the Geospatial Frontend AI skill free?

Yes. It is published on GitHub by isaaccorley under the Apache-2.0 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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