Total 47,066 skills
Showing 12 of 47066 skills
Centrifuge integration. Manage data, records, and automate workflows. Use when the user wants to interact with Centrifuge data.
Product Fruits integration. Manage ProductFruitsApps. Use when the user wants to interact with Product Fruits data.
Donately integration. Manage Organizations, Projects, Users. Use when the user wants to interact with Donately data.
Every Techmeme headline, searchable and cached locally — plus topic tracking, trending analysis, and catch-up workflows no other tool has. Trigger phrases: `what's happening in tech`, `tech news today`, `check techmeme`, `what did I miss in tech`, `trending tech stories`, `use techmeme`, `run techmeme`.
This skill should be used when debugging full-stack issues that span UI, backend, and database layers. It provides a systematic workflow to detect errors, analyze root causes, apply fixes iteratively, and verify solutions through automated server restarts and browser-based testing. Ideal for scenarios like failing schedulers, import errors, database issues, or API payload problems where issues originate in backend code but manifest in the UI.
Make integration. Manage data, records, and automate workflows. Use when the user wants to interact with Make data.
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.
Subsurface well data analysis toolkit for loading, processing, and analyzing well logs, projects, and formation tops. Built on lasio with enhanced curve processing. Use when Claude needs to: (1) Load wells from LAS files with metadata, (2) Work with multi-well Projects, (3) Process curves (despike, smooth, resample, normalize), (4) Manage formation tops, (5) Export well data to DataFrame/LAS/CSV, (6) Perform cross-well analysis and QC.
Read, write, and manipulate SEG-Y seismic data files. Fast C library with Python bindings for trace, header, inline, and crossline access. Use when Claude needs to: (1) Read/inspect SEG-Y files, (2) Extract trace data or headers, (3) Access 3D survey data by inline/crossline, (4) Create new SEG-Y files from arrays, (5) Modify existing SEG-Y files, (6) Extract subsets of seismic data, (7) Read/write Seismic Unix format.
N-dimensional labeled arrays for geoscience data. Read/write NetCDF, work with climate and oceanographic datasets, perform multi-dimensional analysis with labeled coordinates. Use when Claude needs to: (1) Read/write NetCDF or Zarr files, (2) Work with multidimensional arrays with labeled dimensions, (3) Analyze climate, ocean, or atmosphere data, (4) Compute temporal aggregations (daily/monthly/annual means), (5) Perform area-weighted statistics, (6) Process large datasets with Dask, (7) Apply CF conventions to scientific data.
Symbolic PDE solver with automatic code generation for finite-difference computations. Use when Claude needs to: (1) Perform seismic wave propagation modeling, (2) Implement acoustic or elastic wave equations, (3) Run forward modeling for shot gathers, (4) Set up Full Waveform Inversion (FWI) workflows, (5) Implement Reverse Time Migration (RTM), (6) Create absorbing boundary conditions, (7) Generate optimized stencil code for CPUs/GPUs, (8) Solve custom PDEs with finite differences.
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.