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Agent skill

Geospatial Data Pipeline

by curiositech in curiositech/some_claude_skills

Process, analyze, and visualize geospatial data at scale. An agent skill from curiositech/some_claude_skills.

MITAuto-check passedData & Analytics

Install Geospatial Data Pipeline

skills CLI
$ npx skills add curiositech/some_claude_skills --skill geospatial-data-pipeline -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install curiositech/some_claude_skills geospatial-data-pipeline --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/curiositech/some_claude_skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/geospatial-data-pipeline .claude/skills/geospatial-data-pipeline && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
geospatial-data-pipeline
GitHub stars
243
Token cost
~3k tokens
SKILL.md length
566 words
Files
7 (incl. scripts, references)
Skills in repo
109
Repo updated
First seen
Licence
MIT

At a glance

Process, analyze, and visualize geospatial data at scale. An agent skill from curiositech/some_claude_skills.

  • Drone data processing
  • SKILL.md covers When to Use, Technology Selection, Common Anti-Patterns and Production Checklist, plus 3 more sections
  • Runs TypeScript scripts from its folder; calls npm
  • Location-based services

What it does

Geospatial Data Pipeline is an agent skill from curiositech/some_claude_skills. Process, analyze, and visualize geospatial data at scale. Handles drone imagery, GPS tracks, GeoJSON optimization, coordinate transformations, and tile generation. Use for mapping apps, drone data processing, location-based services. Activate on "geospatial", "GIS", "PostGIS", "GeoJSON", "map tiles", "coordinate systems". NOT for simple address validation, basic distance calculations, or static map embeds.

Its SKILL.md is about 3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including scripts and reference files (for example `.claude-plugin/plugin.json`, `references/coordinate-systems.md` and `references/geojson-optimization.md`).

It sits in Data & Analytics, covering Geospatial analysis and Data pipelines and ETL. It works with MongoDB. The repository describes itself as: Claude skills that make my life easier. The licence is MIT.

When your agent uses it

  • Drone data processing
  • Location-based services

Example prompts

  • “geospatial”
  • “PostGIS”
  • “GeoJSON”
  • “/geospatial-data-pipeline”

Requirements

  • Node.js
  • Pre-approved tools (allowed-tools): Read, Write, Edit, Bash(npm:*,gdal*,postgres*)

What it can do on your machine

Read from SKILL.md and the folder at commit 6713fc7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Write
    • Edit
    • Bash(npm:*
    • gdal*
    • postgres*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (TypeScript), which the agent can run.

    Shell commands in SKILL.md call:

    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Geospatial Data Pipeline loads about 3k tokens when it runs, and up to ~11k if it reads all its reference files. Until then it costs about 109 tokens; SKILL.md has 566 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~109
When it runs · the whole SKILL.md, loaded when a task matches
~3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~11k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from curiositech/some_claude_skills at commit 6713fc7, republished under its MIT licence (© curiositech). 566 words, ~2,964 tokens.

Download SKILL.mdSave it as .claude/skills/geospatial-data-pipeline/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
geospatial-data-pipeline
description
Process, analyze, and visualize geospatial data at scale. Handles drone imagery, GPS tracks, GeoJSON optimization, coordinate transformations, and tile generation. Use for mapping apps, drone data processing, location-based services. Activate on "geospatial", "GIS", "PostGIS", "GeoJSON", "map tiles", "coordinate systems". NOT for simple address validation, basic distance calculations, or static map embeds.
allowed-tools
Read, Write, Edit, Bash(npm:*,gdal*,postgres*)
metadata.category
Data & Analytics
metadata.tags
geospatial, data, pipeline, gis, postgis

Geospatial Data Pipeline

Expert in processing, optimizing, and visualizing geospatial data at scale.

When to Use

✅ Use for:

  • Drone imagery processing and annotation
  • GPS track analysis and visualization
  • Location-based search (find nearby X)
  • Map tile generation for web/mobile
  • Coordinate system transformations
  • Geofencing and spatial queries
  • GeoJSON optimization for web

❌ NOT for:

  • Simple address validation (use address APIs)
  • Basic distance calculations (use Haversine formula)
  • Static map embeds (use Mapbox Static API)
  • Geocoding (use Nominatim or Google Geocoding API)

Technology Selection

Database: PostGIS vs MongoDB Geospatial
FeaturePostGISMongoDB
Spatial indexesGiST, SP-GiST2dsphere
Query languageSQL + spatial functionsAggregation pipeline
Geometry types20+ (full OGC support)Basic (Point, Line, Polygon)
Coordinate systems6000+ via EPSGWGS84 only
Performance (10M points)<100ms<200ms
Best forComplex spatial analysisDocument-centric apps

Timeline:

  • 2005: PostGIS 1.0 released
  • 2012: MongoDB adds geospatial indexes
  • 2020: PostGIS 3.0 with improved performance
  • 2024: PostGIS remains gold standard for GIS workloads

Common Anti-Patterns

Anti-Pattern 1: Storing Coordinates as Strings

Novice thinking: "I'll just store lat/lon as text, it's simple"

Problem: Can't use spatial indexes, queries are slow, no validation.

Wrong approach:

typescript
// ❌ String storage, no spatial features
interface Location {
  id: string;
  name: string;
  latitude: string;   // "37.7749"
  longitude: string;  // "-122.4194"
}

// Linear scan for "nearby" queries
async function findNearby(lat: string, lon: string): Promise<Location[]> {
  const all = await db.locations.findAll();

  return all.filter(loc => {
    const distance = calculateDistance(
      parseFloat(lat),
      parseFloat(lon),
      parseFloat(loc.latitude),
      parseFloat(loc.longitude)
    );
    return distance < 5000; // 5km
  });
}

Why wrong: O(N) linear scan, no spatial index, string parsing overhead.

Correct approach:

typescript
// ✅ PostGIS GEOGRAPHY type with spatial index
CREATE TABLE locations (
  id SERIAL PRIMARY KEY,
  name VARCHAR(255),
  location GEOGRAPHY(POINT, 4326)  -- WGS84 coordinates
);

-- Spatial index (GiST)
CREATE INDEX idx_locations_geography ON locations USING GIST(location);

-- TypeScript query
async function findNearby(lat: number, lon: number, radiusMeters: number): Promise<Location[]> {
  const query = `
    SELECT id, name, ST_AsGeoJSON(location) as geojson
    FROM locations
    WHERE ST_DWithin(
      location,
      ST_SetSRID(ST_MakePoint($1, $2), 4326)::geography,
      $3
    )
    ORDER BY location <-> ST_SetSRID(ST_MakePoint($1, $2), 4326)::geography
    LIMIT 100
  `;

  return db.query(query, [lon, lat, radiusMeters]);  // &lt;10ms with index
}

Timeline context:

  • 2000s: Stored lat/lon as FLOAT columns, did math in app code
  • 2010s: PostGIS adoption, spatial indexes
  • 2024: GEOGRAPHY type handles Earth curvature automatically
Anti-Pattern 2: Not Using Spatial Indexes

Problem: Proximity queries do full table scans.

Wrong approach:

sql
-- ❌ No index, sequential scan
CREATE TABLE drone_images (
  id SERIAL PRIMARY KEY,
  image_url VARCHAR(255),
  location GEOGRAPHY(POINT, 4326)
);

-- This query scans ALL rows
SELECT * FROM drone_images
WHERE ST_DWithin(
  location,
  ST_SetSRID(ST_MakePoint(-122.4194, 37.7749), 4326)::geography,
  1000  -- 1km
);

EXPLAIN output: Seq Scan on drone_images (cost=0.00..1234.56 rows=1 width=123)

Correct approach:

sql
-- ✅ GiST index for spatial queries
CREATE INDEX idx_drone_images_location ON drone_images USING GIST(location);

-- Same query, now uses index
SELECT * FROM drone_images
WHERE ST_DWithin(
  location,
  ST_SetSRID(ST_MakePoint(-122.4194, 37.7749), 4326)::geography,
  1000
);

EXPLAIN output: Bitmap Index Scan on idx_drone_images_location (cost=4.30..78.30 rows=50 width=123)

Performance impact: 10M points, 5km radius query

  • Without index: 3.2 seconds (full scan)
  • With GiST index: 12ms (99.6% faster)
Anti-Pattern 3: Mixing Coordinate Systems

Novice thinking: "Coordinates are just numbers, I can mix them"

Problem: Incorrect distances, misaligned map features.

Wrong approach:

typescript
// ❌ Mixing EPSG:4326 (WGS84) and EPSG:3857 (Web Mercator)
const userLocation = {
  lat: 37.7749,   // WGS84
  lon: -122.4194
};

const droneImage = {
  x: -13634876,  // Web Mercator (EPSG:3857)
  y: 4545684
};

// Comparing apples to oranges!
const distance = Math.sqrt(
  Math.pow(userLocation.lon - droneImage.x, 2) +
  Math.pow(userLocation.lat - droneImage.y, 2)
);

Result: Wildly incorrect distance (millions of "units").

Correct approach:

sql
-- ✅ Transform to common coordinate system
SELECT ST_Distance(
  ST_Transform(
    ST_SetSRID(ST_MakePoint(-122.4194, 37.7749), 4326),  -- WGS84
    3857  -- Transform to Web Mercator
  ),
  ST_SetSRID(ST_MakePoint(-13634876, 4545684), 3857)  -- Already Web Mercator
) AS distance_meters;

Or better: Always store in one system (WGS84), transform on display only.

Timeline:

  • 2005: Web Mercator (EPSG:3857) introduced by Google Maps
  • 2010: Confusion peaks as apps mix WGS84 data with Web Mercator tiles
  • 2024: Best practice: Store WGS84, transform to 3857 only for tile rendering
Show full SKILL.md (214 more words)Show less
Anti-Pattern 4: Loading Huge GeoJSON Files

Problem: 50MB GeoJSON file crashes browser.

Wrong approach:

typescript
// ❌ Load entire file into memory
const geoJson = await fetch('/drone-survey-data.geojson').then(r => r.json());

// 50MB of GeoJSON = browser freeze
map.addSource('drone-data', {
  type: 'geojson',
  data: geoJson  // All 10,000 polygons loaded at once
});

Correct approach 1: Vector tiles (pre-chunked)

typescript
// ✅ Serve as vector tiles (MBTiles or PMTiles)
map.addSource('drone-data', {
  type: 'vector',
  tiles: ['https://api.example.com/tiles/{z}/{x}/{y}.pbf'],
  minzoom: 10,
  maxzoom: 18
});

// Browser only loads visible tiles

Correct approach 2: GeoJSON simplification + chunking

bash
# Simplify geometry (reduce points)
npm install -g @mapbox/geojson-precision
geojson-precision -p 5 input.geojson output.geojson

# Split into tiles
npm install -g geojson-vt
# Generate tiles programmatically (see scripts/tile_generator.ts)

Correct approach 3: Server-side filtering

typescript
// ✅ Only fetch visible bounds
async function fetchVisibleFeatures(bounds: Bounds): Promise<GeoJSON> {
  const response = await fetch(
    `/api/features?bbox=${bounds.west},${bounds.south},${bounds.east},${bounds.north}`
  );
  return response.json();
}

map.on('moveend', async () => {
  const bounds = map.getBounds();
  const geojson = await fetchVisibleFeatures(bounds);
  map.getSource('dynamic-data').setData(geojson);
});
Anti-Pattern 5: Euclidean Distance on Spherical Earth

Novice thinking: "Distance is just Pythagorean theorem"

Problem: Incorrect at scale, worse near poles.

Wrong approach:

typescript
// ❌ Flat Earth distance (wrong!)
function distanceKm(lat1: number, lon1: number, lat2: number, lon2: number): number {
  const dx = lon2 - lon1;
  const dy = lat2 - lat1;

  return Math.sqrt(dx * dx + dy * dy) * 111.32;  // 111.32 km/degree (WRONG)
}

// Example: San Francisco to New York
const distance = distanceKm(37.7749, -122.4194, 40.7128, -74.0060);
// Returns: ~55 km (WRONG! Actual: ~4,130 km)

Why wrong: Earth is a sphere, not a flat plane.

Correct approach 1: Haversine formula (great circle distance)

typescript
// ✅ Haversine formula (spherical Earth)
function haversineKm(lat1: number, lon1: number, lat2: number, lon2: number): number {
  const R = 6371; // Earth radius in km

  const dLat = toRadians(lat2 - lat1);
  const dLon = toRadians(lon2 - lon1);

  const a =
    Math.sin(dLat / 2) * Math.sin(dLat / 2) +
    Math.cos(toRadians(lat1)) * Math.cos(toRadians(lat2)) *
    Math.sin(dLon / 2) * Math.sin(dLon / 2);

  const c = 2 * Math.atan2(Math.sqrt(a), Math.sqrt(1 - a));

  return R * c;
}

// San Francisco to New York
const distance = haversineKm(37.7749, -122.4194, 40.7128, -74.0060);
// Returns: ~4,130 km ✅

Correct approach 2: PostGIS (handles curvature automatically)

sql
-- ✅ PostGIS ST_Distance with GEOGRAPHY
SELECT ST_Distance(
  ST_SetSRID(ST_MakePoint(-122.4194, 37.7749), 4326)::geography,
  ST_SetSRID(ST_MakePoint(-74.0060, 40.7128), 4326)::geography
) / 1000 AS distance_km;
-- Returns: 4130.137 km ✅

Accuracy comparison:

MethodSF to NYCError
Euclidean (flat)55 km98.7% wrong
Haversine (sphere)4,130 km✅ Correct
PostGIS (ellipsoid)4,135 kmMost accurate

Production Checklist

□ PostGIS extension installed and spatial indexes created
□ All coordinates stored in consistent SRID (recommend: 4326)
□ GeoJSON files optimized (&lt;1MB) or served as vector tiles
□ Coordinate transformations use ST_Transform, not manual math
□ Distance calculations use ST_Distance with GEOGRAPHY type
□ Bounding box queries use ST_MakeEnvelope + ST_Intersects
□ Large geometries chunked (not &gt;100KB per feature)
□ Map tiles pre-generated for common zoom levels
□ CORS configured for tile servers
□ Rate limiting on geocoding/reverse geocoding endpoints

When to Use vs Avoid

ScenarioAppropriate?
Drone imagery annotation and search✅ Yes - process survey data
GPS track visualization✅ Yes - optimize paths
Find nearest coffee shops✅ Yes - spatial queries
Jurisdiction boundary lookups✅ Yes - point-in-polygon
Simple address autocomplete❌ No - use Mapbox/Google
Embed static map on page❌ No - use Static API
Geocode single address❌ No - use geocoding API

References

  • /references/coordinate-systems.md - EPSG codes, transformations, Web Mercator vs WGS84
  • /references/postgis-guide.md - PostGIS setup, spatial indexes, common queries
  • /references/geojson-optimization.md - Simplification, chunking, vector tiles

Scripts

  • scripts/geospatial_processor.ts - Process drone imagery, GPS tracks, GeoJSON validation
  • scripts/tile_generator.ts - Generate vector tiles (MBTiles/PMTiles) from GeoJSON

This skill guides: Geospatial data | PostGIS | GeoJSON | Map tiles | Coordinate systems | Drone data processing | Spatial queries

© curiositech, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files (scripts, references) in .claude/skills/geospatial-data-pipeline of curiositech/some_claude_skills.

  • SKILL.md
  • .claude-plugin/plugin.json
  • references/coordinate-systems.md
  • references/geojson-optimization.md
  • references/postgis-guide.md
  • scripts/geospatial_processor.ts
  • scripts/tile_generator.ts

Open the folder on GitHubat commit 6713fc7

Compare with similar skills

Geospatial Data Pipeline next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Geospatial Data Pipeline compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Geospatial Data Pipeline this skillcuriositech/some_claude_skills243—~3kAutomated safety check: PassMIT
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Crawl4AI Web Scrapingsmallnest/goclaw5981 repos~2.5kAutomated safety check: PassMIT
Geo SleuthOldcircle/geo-sleuth1.3k—~6.1kAutomated safety check: PassMIT
Glue 09 10 Migrationaws-samples/aws-glue-samples1.5k—~2.4kAutomated safety check: PassMIT-0
Migrate Glue Devendpoint To Interactive Sessionsaws-samples/aws-glue-samples1.5k—~3.6kAutomated safety check: PassMIT-0

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Works with

Categories

Questions about Geospatial Data Pipeline

What does Geospatial Data Pipeline do?

Process, analyze, and visualize geospatial data at scale. An agent skill from curiositech/some_claude_skills. Geospatial Data Pipeline is an agent skill from curiositech/some_claude_skills. Process, analyze, and visualize geospatial data at scale.

When should I use Geospatial Data Pipeline?

Geospatial Data Pipeline fits situations like: drone data processing; location-based services.

How do I install Geospatial Data Pipeline in Claude Code?

Run `npx skills add curiositech/some_claude_skills --skill geospatial-data-pipeline -a claude-code`. Or copy the skill folder (.claude/skills/geospatial-data-pipeline in curiositech/some_claude_skills) into .claude/skills/geospatial-data-pipeline in your project. Claude Code loads it when a task matches its description.

How do I install Geospatial Data Pipeline in Codex?

Run `npx skills add curiositech/some_claude_skills --skill geospatial-data-pipeline -a codex`. Or copy the skill folder (.claude/skills/geospatial-data-pipeline in curiositech/some_claude_skills) into .agents/skills/geospatial-data-pipeline in your project. Codex loads it when a task matches its description.

Can I use Geospatial Data Pipeline in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add curiositech/some_claude_skills --skill geospatial-data-pipeline -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/geospatial-data-pipeline, .gemini/skills/geospatial-data-pipeline, .github/skills/geospatial-data-pipeline and .opencode/skills/geospatial-data-pipeline in your project.

What does Geospatial Data Pipeline need to run?

Going by SKILL.md and its folder, Geospatial Data Pipeline needs TypeScript for the scripts in its folder and the command-line tools its instructions call (npm). Our summary lists: Node.js. Its frontmatter pre-approves these tools: Read, Write, Edit, Bash(npm:*,gdal*,postgres*).

Does Geospatial Data Pipeline access the network?

SKILL.md contains no URLs. Its commands use npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Geospatial Data Pipeline safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Geospatial Data Pipeline use?

Geospatial Data Pipeline is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Geospatial Data Pipeline use?

About 3k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7.8k tokens, read only when the agent opens those files.

What are the alternatives to Geospatial Data Pipeline?

Skills that share tags, products or a category with Geospatial Data Pipeline: Antv L7 (antvis/L7, 4.1k stars), Crawl4AI Web Scraping (smallnest/goclaw, 598 stars), Geo Sleuth (Oldcircle/geo-sleuth, 1.3k stars) and Glue 09 10 Migration (aws-samples/aws-glue-samples, 1.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Geospatial Data Pipeline?

curiositech (a GitHub organization) maintains it in curiositech/some_claude_skills, which has 243 GitHub stars. The repository holds 109 skills in this directory. The repository was last updated on September 6, 2026.

Source: curiositech/some_claude_skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.