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.
git clone --depth 1 https://github.com/SteadfastAsArt/geoscience-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/commands/steadfastasart/geoscience-skills/spatial-gridding)<a href="https://agentmods.dev/commands/steadfastasart/geoscience-skills/spatial-gridding"><img src="https://agentmods.dev/badge/commands/steadfastasart/geoscience-skills/spatial-gridding.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 5.1 | $0.00014 | $0.00602 |
| Opus 5 | $0.00007 | $0.00301 |
| Sonnet 5 | $0.00003 | $0.00120 |
| Haiku 4.5 | $0.00001 | $0.00060 |
Grade A, and why
spatial-gridding 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spatial Data Gridding Workflow
Guide the user through spatial data gridding and interpolation. Determine the appropriate skill chain based on data type and gridding method.
Decision Tree
-
What gridding method fits your data?
- Deterministic (splines, linear, cubic) → Use
verdeskill - Geostatistical kriging with GSLIB backend → Use
geostatspyskill - Geostatistical with sklearn-compatible API → Use
scikit-gstatskill - Gravity or magnetic potential field data → Use
harmonicaskill
- Deterministic (splines, linear, cubic) → Use
-
Which tool best fits your workflow?
- Verde: Green's functions gridding, cross-validation, sklearn-style API
- GeostatsPy: GSLIB-based kriging, simulation, traditional geostatistics
- scikit-gstat: variogram modelling, sklearn integration, modern Python API
- Harmonica: equivalent sources for gravity/magnetics, terrain corrections
-
Visualization needs?
- 2D gridded maps → matplotlib (built-in)
- 3D surfaces and point clouds → Use
pyvistaskill
Skill Chain
verde (deterministic gridding) → pyvista (3D viz)
geostatspy (GSLIB kriging/simulation) → pyvista (3D viz)
scikit-gstat (variograms + kriging) → pyvista (3D viz)
harmonica (potential field gridding) → pyvista (3D viz)
Step Prompts
For each step, invoke the relevant domain skill and follow its guidance.
Step 1: Data Exploration
- Load scattered point data with coordinates and values
- Check for spatial clustering, outliers, and trends
- Compute basic statistics and visualize point distribution
Step 2: Variography (Geostatistical Methods)
- Compute experimental variogram (omnidirectional and directional)
- Fit theoretical variogram model (spherical, exponential, Gaussian)
- Identify nugget, sill, and range parameters
Step 3: Gridding / Interpolation
- Define output grid extent and resolution
- Verde: fit spline or linear gridder with cross-validation
- GeostatsPy/scikit-gstat: run kriging with fitted variogram
- Harmonica: fit equivalent sources for potential field data
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 · 67 lines · 14 tokens per session scan A b0de197e86b3
spatial-gridding is a command published in the GitHub repository SteadfastAsArt/geoscience-skills (57 stars, last pushed 5mo ago), licensed MIT. It adds 14 tokens to every session and 602 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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