A workflow for analyzing location-based data, including coordinates, addresses, ZIP codes, and geographic shapes. It checks locations, measures distances, finds spatial groupings, and assesses accessibility.

Use it to validate latitude and longitude, map geographic coverage, calculate nearest points or spacing, find geographic clusters, and study access to places.

It helps detect invalid or missing locations and understand whether records are concentrated, spread out, or related by distance. It turns geographic fields into measurable patterns for analysis.

geospatial — Skill for Claude Code by ChrisGVE

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add ChrisGVE/localdata-mcp --skill geospatial

Clone the repo

git clone --depth 1 https://github.com/ChrisGVE/localdata-mcp

Made for: Claude Code.

Or install localdata-mcp, the plugin that ships this one along with the rest of its 18 skills, 11 agents, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/chrisgve/localdata-mcp/geospatial/github.svg)](https://agentmods.dev/skills/chrisgve/localdata-mcp/geospatial)

Your own site

<a href="https://agentmods.dev/skills/chrisgve/localdata-mcp/geospatial"><img src="https://agentmods.dev/badge/skills/chrisgve/localdata-mcp/geospatial/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/chrisgve/localdata-mcp/geospatial"><img src="https://agentmods.dev/badge/skills/chrisgve/localdata-mcp/geospatial.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 563 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00027	$0.00563
Opus 5	$0.00014	$0.00282
Sonnet 5	$0.00005	$0.00113
Haiku 4.5	$0.00003	$0.00056

Measured 10d ago against content hash 36faa3e5684d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

geospatial scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/modeling/geospatial/SKILL.md · 37 lines

How it starts

The opening of the file, as written. The whole thing — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Geospatial Analysis

Analyze spatial patterns, distances, and geographic relationships in location-based data.

Steps

Identify spatial columns. Call describe_database with the database name from $ARGUMENTS. Look for columns containing coordinates (latitude/longitude, x/y), addresses, ZIP codes, or geometry fields. Call describe_table for detailed column inspection.
Validate coordinates. Call execute_query to check coordinate ranges: latitude should be -90 to 90, longitude -180 to 180. Flag nulls, zeros (often default values, not actual locations), and points in unexpected regions. Report the geographic extent of the data.
Assess spatial distribution. Call execute_query to compute basic spatial statistics: centroid (mean lat/lon), spread (standard deviation of coordinates), and bounding box. Determine whether points are concentrated in a small area or spread across a wide region.
Compute distances. Calculate distances between points of interest using the haversine formula (suitable for lat/lon data). Call execute_query with distance computations to find nearest neighbors, average spacing, and distance distributions.
Detect spatial clusters. Use analyze_clusters with DBSCAN and geographic distance to identify spatial hotspots. Alternatively, compute density by gridding the area and counting points per cell. Report cluster locations, sizes, and any relationship to attributes.
Cross-reference with attributes. Call execute_query to analyze how non-spatial attributes vary across geographic clusters or regions. Spatial patterns are most valuable when they correlate with other variables.
Present results. Provide:
- Spatial data summary: coordinate system, extent, point count, coverage
- Distribution pattern: clustered, dispersed, or random
- Key spatial clusters with locations and characterization
- Distance statistics relevant to the analysis question
- Geographic insights tied to the domain context

Read the full file on GitHub · 37 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

10d ago First seen · 37 lines · 27 tokens per session scan A 36faa3e5684d

Subscribe to this mod's changes

geospatial is a skill published in the GitHub repository ChrisGVE/localdata-mcp (4 stars, last pushed 26d ago), licensed Apache-2.0. It adds 27 tokens to every session and 563 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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