scikit-gstat
Geostatistical analysis with scikit-learn style API. Compute variograms, kriging interpolation, and spatial correlation analysis. Use when Claude needs to: (1) Compute experimental variograms from spatial data, (2) Fit variogram models (spherical, exponential, gaussian, matern), (3) Perform Ordinary or Universal Kriging interpolation, (4) Assess spatial anisotropy with directional variograms, (5) Cross-validate spatial models, (6) Analyze spatio-temporal data, (7) Export variogram parameters for other geostatistical software.
npx skills add majiayu000/claude-skill-registry --skill scikit-gstat --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# SciKit-GStat - Geostatistics ## Quick Reference ```python import skgstat as skg import numpy as np # Create variogram V = skg.Variogram(coordinates=coords, values=values, n_lags=15) # Fit model V.model = 'spherical' print(f"Range: {V.parameters[0]:.2f}, Sill: {V.parameters[1]:.2f}") # Kriging interpolation ok = skg.OrdinaryKriging(V) predictions = ok.transform(grid_coords) ``` ## Key Classes | Class | Purpose | |-------|---------| | `Variogram` | Empirical and theoretical variograms | | `OrdinaryKriging` | Interpolation with spatial correlation | | `DirectionalVariogram` | Anisotropic variograms | | `SpaceTimeVariogram` | Spatio-temporal analysis | ## Essential Operations ### Create and Fit Variogram ```python import skgstat as skg V = skg.Variogram( coordinates=coords, # (n, 2) array of x, y values=values, # (n,) array of measurements n_lags=15, maxlag='median' # or specific distance ) # Fit model: 'spherical', 'exponential', 'gaussian', 'matern', 'stable' V.model = 'spherical' # Get parameters print(f"Range: {V.parameters[0]:.2f}") print(f"Sill: {V.parameters[1]:.2f}") print(f"Nugget: {V.parameters[2]:.2f}") print(f"RMSE: {V.rmse:.4f}") ``` ### Ordinary Kriging ```python import
- Quick Reference
- Key Classes
- Essential Operations
- Create and Fit Variogram
- Ordinary Kriging
- Directional Variogram
- Cross-Validation
- Robust Estimators
- Quick Model Reference
- When to Use vs Alternatives
- Common Workflows
- Variogram Fitting and Ordinary Kriging
- Common Issues
- Tips
What does the scikit-gstat skill do?
Geostatistical analysis with scikit-learn style API. Compute variograms, kriging interpolation, and spatial correlation analysis. Use when Claude needs to: (1) Compute experimental variograms from spatial data, (2) Fit variogram models (spherical, exponential, gaussian, matern), (3) Perform Ordinary or Universal Kriging interpolation, (4) Assess spatial anisotropy with directional variograms, (5) Cross-validate spatial models, (6) Analyze spatio-temporal data, (7) Export variogram parameters for other geostatistical software.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill scikit-gstat --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.