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 beita6969/ScienceClaw --skill geospatial-analysisgit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/geospatial-analysis)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/geospatial-analysis"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/geospatial-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/beita6969/scienceclaw/geospatial-analysis"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/geospatial-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 | $0.00049 | $0.00847 |
| Opus 5 | $0.00024 | $0.00424 |
| Sonnet 5 | $0.00010 | $0.00169 |
| Haiku 4.5 | $0.00005 | $0.00085 |
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 8d 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 — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Trigger
Activate this skill when the user mentions:
- GIS, geographic information systems, spatial data
- Coordinates, latitude/longitude, projections, CRS
- Spatial statistics, spatial autocorrelation, hotspot analysis
- Remote sensing, satellite imagery, NDVI, land cover classification
- Mapping, cartography, choropleth, heatmaps
- Geocoding, reverse geocoding, routing, network analysis
- Shapefiles, GeoJSON, raster data, vector data
Step-by-Step Methodology
- Data acquisition and format assessment - Identify data types: vector (points, lines, polygons in shapefile/GeoJSON/GeoPackage) or raster (GeoTIFF, NetCDF). Determine coordinate reference system (CRS). Check for common issues: mixed CRS, topology errors, missing geometries.
- Projection and transformation - Ensure all layers share the same CRS. Use geographic CRS (WGS84/EPSG:4326) for global data, projected CRS (UTM, state plane) for area/distance calculations. Apply appropriate datum transformation.
- Spatial operations - Perform geoprocessing: buffer, intersect, union, clip, dissolve. For point data: spatial joins, nearest neighbor analysis. For raster: reclassification, map algebra, zonal statistics.
- Spatial statistics - Test for spatial autocorrelation (Global Moran's I). Identify clusters and hotspots (Local Moran's I / LISA, Getis-Ord Gi*). For point patterns: kernel density estimation, Ripley's K function. For regression: spatial lag or spatial error models (GWR for non-stationarity).
- Remote sensing analysis - Atmospheric correction and preprocessing. Compute indices (NDVI, NDWI, NDBI). Supervised classification (random forest, SVM) or unsupervised (K-means, ISODATA). Accuracy assessment with confusion matrix and Kappa statistic.
- Visualization and cartography - Create maps with proper elements: title, scale bar, north arrow, legend, data source. Use appropriate color schemes (sequential for magnitude, diverging for deviation, qualitative for categories). Consider colorblind-safe palettes.
- Validation - Verify spatial operations with visual inspection and area/count checks. Cross-validate classification accuracy. Assess edge effects in spatial statistics. Report spatial resolution and positional accuracy.
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.
- 8d ago First seen · 55 lines · 49 tokens per session scan A a89231a5aef8
geospatial-analysis is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 49 tokens to every session and 847 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 2026-09-03.
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