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Geospatial Data Pipeline

Checked
erichowens·vlatest·data·MIT

Helps with data, pipeline automation as part of automating DevOps pipelines and CI/CD workflows workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

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

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

erichowens

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned erichowens/some_claude_skills at 6713fc7a64.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
6713fc7a64
Manifest
.claude/skills/geospatial-data-pipeline/SKILL.md
Scanned
7 files, 2,726 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

The geospatial-data-pipeline skill enables AI agents to ingest, process, and visualize location-based data from various sources. It supports operations like coordinate transformation, spatial indexing, and generating map visualizations for complex datasets.

When to use it

  • Integrating GPS tracking logs into a central analytics dashboard.
  • Performing spatial joins between customer addresses and regional sales figures.
  • Converting raw latitude/longitude coordinates into standardized formats for mapping libraries.
  • Generating heatmaps to identify high-density activity zones from sensor data.

Key capabilities

  • Ingests diverse geospatial formats including GeoJSON, KML, and Shapefiles.
  • Performs coordinate system transformations between different projections.
  • Creates interactive map visualizations directly within the agent environment.
  • Executes spatial queries to filter data based on geographic boundaries.

Example prompts

  • "Load this CSV file containing delivery coordinates and plot them on a map showing coverage gaps."
  • "Transform these latitude and longitude points from WGS84 to Web Mercator for use in our legacy mapping system."
  • "Analyze the spatial distribution of server outages and generate a heatmap highlighting affected regions."

Tips & gotchas

Ensure your input data includes valid coordinate pairs; missing or malformed location data will cause pipeline failures. For large datasets, consider pre-aggregating points to improve visualization performance before processing.