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.
npx skills add MEKXH/golem --skill spatial-analysisgit clone --depth 1 https://github.com/MEKXH/golemWrote 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.
[](https://agentmods.dev/skills/mekxh/golem/spatial-analysis)<a href="https://agentmods.dev/skills/mekxh/golem/spatial-analysis"><img src="https://agentmods.dev/badge/skills/mekxh/golem/spatial-analysis/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.
<a href="https://agentmods.dev/skills/mekxh/golem/spatial-analysis"><img src="https://agentmods.dev/badge/skills/mekxh/golem/spatial-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00022 | $0.00576 |
| Opus 5.5 | $0.00009 | $0.00230 |
| Sonnet 5.5 | $0.00004 | $0.00115 |
| Haiku 4.5 | $0.00002 | $0.00058 |
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 1mo 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.
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.
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.
- 1mo ago First seen · 65 lines · 22 tokens per session scan A 846207248235
spatial-analysis is a skill published in the GitHub repository MEKXH/golem (200 stars, last pushed 3mo ago), licensed MIT. It adds 22 tokens to every session and 576 once invoked, about $0.0001 per session on Opus 5.5. 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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