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 agentmods add skills/itallstartedwithaidea/agent-skills/geospatial-analysisnpx skills add itallstartedwithaidea/agent-skills --skill geospatial-analysisgit clone --depth 1 https://github.com/itallstartedwithaidea/agent-skillsWrote 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/itallstartedwithaidea/agent-skills/geospatial-analysis)<a href="https://agentmods.dev/skills/itallstartedwithaidea/agent-skills/geospatial-analysis"><img src="https://agentmods.dev/badge/skills/itallstartedwithaidea/agent-skills/geospatial-analysis.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00030 | $0.01415 |
| Opus 5 | $0.00015 | $0.00707 |
| Sonnet 5 | $0.00006 | $0.00283 |
| Haiku 4.5 | $0.00003 | $0.00142 |
Grade A, and why
geospatial-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 5d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Geospatial Analysis
Part of Agent Skills™ by googleadsagent.ai™
Description
Geospatial Analysis provides workflows for satellite imagery processing, GIS operations with GeoPandas, spatial statistics, and Earth observation data analysis. The agent builds reproducible geospatial pipelines that transform raw spatial data into actionable geographic intelligence, from raster processing through vector operations to publication-quality cartographic output.
Geospatial data is fundamentally different from tabular data: it has coordinate reference systems that must be respected, spatial relationships that affect statistical independence, and scale-dependent patterns that change with resolution. This skill encodes the domain knowledge needed to handle these challenges correctly: CRS transformations, spatial joins, buffer operations, raster-vector interoperability, and spatial autocorrelation tests.
The skill integrates satellite imagery analysis (Sentinel, Landsat) with vector data processing (shapefiles, GeoJSON), enabling workflows like land use classification from multispectral imagery, urban heat island analysis from thermal bands, and environmental change detection from temporal image stacks.
Use When
- Processing satellite imagery (Sentinel-2, Landsat, MODIS)
- Performing spatial joins, buffers, or overlay operations
- Computing spatial statistics (Moran's I, hot spot analysis)
- Creating publication-quality maps and cartographic outputs
- Analyzing land use, land cover, or environmental change
- Working with coordinate reference systems and projections
How It Works
graph TD
A[Spatial Data Input] --> B{Data Type}
B -->|Raster| C[Satellite Imagery Processing]
B -->|Vector| D[GeoPandas Operations]
C --> E[Band Math + Indices: NDVI, NDWI]
E --> F[Classification / Change Detection]
D --> G[Spatial Joins + Overlay]
G --> H[Spatial Statistics]
F --> I[Raster-Vector Integration]
H --> I
I --> J[Cartographic Output]
J --> K[Publication Map]
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.
- 5d ago First seen · 136 lines · 30 tokens per session scan A 356a36baa1b1
geospatial-analysis is a skill published in the GitHub repository itallstartedwithaidea/agent-skills (36 stars, last pushed 4mo ago), licensed MIT. It adds 30 tokens to every session and 1,415 once invoked, about $0.0002 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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