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Found 26 Skills
Foundation skill for the geoscience skills library. Routes user intent to the correct domain skills, slash commands, and workflow skills. Loaded automatically at session start via SessionStart hook.
6 geoscience & climate skills. Trigger: earth science data, GIS, remote sensing, climate modeling. Design: geospatial tools, satellite data processing, and environmental models.
Petrophysical analysis and formation evaluation from well logs. Calculate porosity, water saturation, permeability, and lithology. Use when Claude needs to: (1) Calculate shale volume from gamma ray, (2) Compute porosity from density/neutron/sonic logs, (3) Estimate water saturation using Archie or Simandoux, (4) Calculate permeability from porosity and saturation, (5) Perform pay zone identification, (6) Conduct multi-mineral analysis, (7) Generate petrophysical summation plots.
Linear operators for large-scale inverse problems with matrix-free representations. Use when Claude needs to: (1) Define linear operators for forward/adjoint operations, (2) Solve inverse problems (deconvolution, imaging, tomography), (3) Apply signal processing transforms (FFT, convolution, derivatives), (4) Compose operators for complex workflows, (5) Perform regularized inversion with smoothness or sparsity constraints, (6) Process seismic or image data at scale.
Stereonet plots for structural geology using matplotlib. Create lower-hemisphere stereographic projections for orientation data. Use when Claude needs to: (1) Create stereonet plots for structural data, (2) Plot planes as great circles or poles, (3) Plot lineations with trend/plunge, (4) Generate density contours for orientations, (5) Calculate mean orientations and statistics, (6) Analyze fold axes with pi-diagrams, (7) Convert between strike/dip and trend/plunge formats.
Magnetotelluric data processing and modelling. Read EDI files, analyze MT responses, perform inversions, and visualize resistivity models. Use when Claude needs to: (1) Read/write EDI files, (2) Process MT impedance tensors, (3) Analyze phase tensors and dimensionality, (4) Plot apparent resistivity and phase curves, (5) Create pseudosections, (6) Perform strike analysis, (7) Run 1D inversions, (8) Prepare data for 2D/3D modelling.
3D structural geological modeling using implicit methods. Create geological models with faults, folds, and unconformities from surface points and orientations. Use when Claude needs to: (1) Build 3D geological models from surface contacts and orientations, (2) Model faults, unconformities, or intrusions, (3) Compute and visualize subsurface geology, (4) Export models to VTK or numpy arrays, (5) Generate gravity forward models, (6) Create cross-sections or 3D visualizations.
Read and parse DLIS (Digital Log Interchange Standard) and LIS (Log Information Standard) well log files. Use when Claude needs to: (1) Read/parse DLIS or LIS files, (2) Extract well log curves as numpy arrays, (3) Access file metadata and origin information, (4) Handle multi-frame or multi-file DLIS, (5) Convert DLIS to LAS or DataFrame, (6) Work with RP66 format well logs, (7) Process array or image log data.
3D visualization and mesh analysis for geoscience data using PyVista/VTK. Use when Claude needs to: (1) Create 3D visualizations of geological models, (2) Render seismic volumes or voxel data, (3) Visualize point clouds or well paths, (4) Plot surfaces and meshes in 3D, (5) Read/write VTK, STL, OBJ files, (6) Create cross-sections through 3D models, (7) Export publication-quality figures or interactive HTML.
Read, write, and manipulate LAS (Log ASCII Standard) well log files for borehole geophysical and petrophysical data. Use when Claude needs to: (1) Read/parse LAS 1.2 or 2.0 files, (2) Extract well headers or curve data, (3) Convert LAS to DataFrame/CSV/Excel, (4) Create new LAS files from arrays, (5) Modify existing LAS files, (6) Handle problematic or malformed LAS files, (7) Batch process multiple well files.
Spatial and spatiotemporal regression with GNNWR (Geographically Neural Network Weighted Regression). Use when Claude needs to: (1) Build spatially varying coefficient regression models, (2) Analyze geographic non-stationarity in spatial data, (3) Generate spatial coefficient maps for publication, (4) Run spatiotemporal regression with GTNNWR, (5) Scale geographically weighted regression to large datasets (N > 10k) with KNN mode, (6) Diagnose spatial model performance with F-tests, AIC, and residual maps.
Spatial data gridding and interpolation with a machine-learning style API. Process geographic and Cartesian point data onto regular grids. Use when Claude needs to: (1) Grid scattered spatial data onto regular grids, (2) Interpolate point data using splines, linear, or cubic methods, (3) Process geographic coordinates with projections, (4) Reduce large datasets using block averaging, (5) Remove polynomial trends from spatial data, (6) Cross-validate gridding parameters, (7) Create processing pipelines with Chain, (8) Grid vector data like GPS velocities.