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Found 5 Skills
A Just-In-Time (JIT) compiler for Python that translates a subset of Python and NumPy code into fast machine code. Developed by Anaconda, Inc. Highly effective for accelerating loops, custom mathematical functions, and complex numerical algorithms. Use for @njit, @vectorize, prange, cuda.jit, numba.typed, JIT compilation, parallel loops, GPU acceleration with CUDA, Monte Carlo simulations, numerical algorithms, and high-performance Python computing.
Create a custom technical indicator using Numba JIT + NumPy. Generates production-grade, O(n) optimized indicator functions with charting and benchmarking.
Compute surface wave dispersion curves for layered Earth models using the Thomson-Haskell matrix method with Numba acceleration. Use when Claude needs to: (1) Calculate Rayleigh or Love wave phase velocities, (2) Compute group velocity dispersion, (3) Generate sensitivity kernels for inversion, (4) Forward model dispersion curves from velocity profiles, (5) Compare dispersion between different Earth models, (6) Set up surface wave tomography workflows.
Set up the Python environment for OpenAlgo indicator analysis. Installs openalgo, plotly, dash, streamlit, numba, yfinance, matplotlib, seaborn, and creates the project folder structure.
GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants to speed up NumPy, pandas, scikit-learn, scikit-image, NetworkX, GeoPandas, or Faiss workloads. Covers physics simulation, differentiable rendering, mesh ray casting, particle systems (DEM/SPH/fluids), vector/similarity search, GPUDirect Storage file IO, interactive dashboards, geospatial analysis, medical imaging, and sparse eigensolvers. Also use when you see CPU-bound Python code (loops, large arrays, ML pipelines, graph analytics, image processing) that would benefit from GPU acceleration, even if not explicitly requested.