spatial-analysis

spatial-analysis is a skill for Claude Code, Codex from MEKXH/golem. It costs 22 tokens per session (576 once invoked), scanned A, original, MIT.

A guided workflow for spatial analysis, meaning analysis of data tied to locations such as points, areas, and map coordinates.

In plain words
What is it for?
Use it to discover geographic datasets, check their metadata and coordinates, query spatial databases, and perform location-based analysis.
Why use it?
It helps the agent find suitable data, inspect its format and coordinate system, reuse known processing steps, and apply established PostGIS query patterns.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/mekxh/golem/spatial-analysis
Any agent
npx skills add MEKXH/golem --skill spatial-analysis
Clone the repo
git clone --depth 1 https://github.com/MEKXH/golem

Made for: Claude Code, Codex.

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.

agentmods badge for spatial-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/mekxh/golem/spatial-analysis.svg)](https://agentmods.dev/skills/mekxh/golem/spatial-analysis)
Your own site
<a href="https://agentmods.dev/skills/mekxh/golem/spatial-analysis"><img src="https://agentmods.dev/badge/skills/mekxh/golem/spatial-analysis.svg" alt="Measured on agentmods" height="20"></a>
Per session 22 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 576 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5 $0.00022 $0.00576
Opus 5 $0.00011 $0.00288
Sonnet 5 $0.00004 $0.00115
Haiku 4.5 $0.00002 $0.00058

Measured 3d ago against content hash 846207248235, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

spatial-analysis 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 3d 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/spatial-analysis/SKILL.md · 65 lines

How it starts

The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Spatial Analysis Workflow

When the user requests geospatial data analysis, follow this structured approach:

Step 1: Discover Candidate Data

If the required data is not already present, locate candidate datasets first:

geo_data_catalog(action="local_scan", path="<workspace path>")
geo_data_catalog(action="overpass_search", bbox=[minLon,minLat,maxLon,maxLat], tags={"amenity":"school"}, limit=10)
geo_data_catalog(action="stac_search", collections=["sentinel-2-l2a"], bbox=[minLon,minLat,maxLon,maxLat], limit=5)

Step 2: Inspect the Data

Use geo_info to understand the data before doing anything:

geo_info(path="<file_path>")

This tells you the format, CRS, extent, and size.

Step 3: Check the CRS

Use geo_crs_detect to verify the coordinate reference system:

geo_crs_detect(path="<file_path>")

Step 4: Reuse Learned Pipelines First

Before inventing a new multi-step flow, check whether the workspace already contains a similar learned geo pipeline in pipelines/geo/. Reuse the same tool sequence when the goal is materially similar.

Step 5: Check the Spatial SQL Codebook

When the task maps to a common PostGIS pattern, inspect the codebook first:

geo_sql_codebook(action="list", intent="<analysis goal>")
geo_sql_codebook(action="render", pattern="<pattern_name>", values={...})

Step 6: Inspect PostGIS Before Querying

When analysis involves a PostGIS database, inspect the available schema before composing SQL:

geo_spatial_query(action="schema")
geo_spatial_query(action="query", sql="SELECT ...")

Step 7: Process and Convert Data

Use geo_process for GDAL/OGR operations and geo_format_convert for direct format changes.

Step 8: Fabricate a Missing Persistent Tool

If the task is recurrent and no learned pipeline, built-in tool, or verified codebook pattern fits, fabricate a workspace geo tool:

  • Create the script under tools/geo/scripts/.
  • Create the manifest under tools/geo/<tool_name>.yaml.
  • Use a geo_ tool name.
  • The script will receive tool arguments as JSON on stdin.
  • The fabricated tool will auto-register on the next agent startup.

Read the full file on GitHub · 65 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.

  1. 3d ago First seen · 65 lines · 22 tokens per session scan A 846207248235

Subscribe to this mod's changes

spatial-analysis is a skill published in the GitHub repository MEKXH/golem (200 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 576 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 2026-08-30.

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