ollama

AMD ROCm GPU computing stack for HIP kernel development and GPU-accelerated library usage. Use when: writing HIP kernels (.hip files), using rocBLAS/rocFFT/rocRAND/rocSOLVER/rocSPARSE/hipBLAS/hipBLASLt/hipTensor/hipSPARSELt/rocALUTION compute libraries, profiling with rocProfiler or rocprof, porting CUDA code to HIP, building CMake/Makefile projects targeting AMD GPUs, using HIP Graphs for low-overhead kernel replay, or debugging GPU code with rocGDB.

Python library for steady-state distribution power system analysis (power flow, state estimation, short-circuit calculations). Use when working with the power-grid-model library to: (1) perform load flow or Newton-Raphson/iterative calculations on electrical grids, (2) run state estimation with sensor data, (3) compute IEC 60909 short-circuit currents, (4) execute batch/time-series or N-1 contingency simulations, or (5) work with grid component types (node, line, transformer, source, sym_load, etc.) and numpy structured arrays.

Power systems analysis and optimization library (pandapower v3.4.0). Use when working with electric power networks: building network models (buses, lines, transformers, loads, generators), running AC/DC power flow, optimal power flow (OPF), short circuit calculations (IEC 60909), state estimation, time series simulations, network topology analysis, or visualizing power grids in Python.

OpenCL SDK (Khronos Group) for cross-platform GPU/CPU parallel computing in C and C++. Use when writing OpenCL kernels, managing devices/contexts/queues, allocating and transferring buffers or images, building and executing programs, or using the C++ wrapper (opencl.hpp / cl::CommandQueue, cl::Buffer, cl::KernelFunctor). Covers OpenCL C API, C++ bindings, and SDK utility libraries (OpenCLUtils, OpenCLSDK).

Complete llama.cpp C/C++ API reference covering model loading, inference, text generation, embeddings, chat, tokenization, sampling, batching, KV cache, LoRA adapters, and state management. Triggers on: llama.cpp questions, LLM inference code, GGUF models, local AI/ML inference, C/C++ LLM integration, "how do I use llama.cpp", API function lookups, implementation questions, troubleshooting llama.cpp issues, and any llama-cpp or ggerganov/llama.cpp mentions.

Build, run, and edit GreyCat projects. GreyCat is a statically-typed language plus runtime for graph-persistent, time-series-aware applications. Use when reading or writing `.gcl` source, when the user mentions GreyCat / project.gcl / nodeTime / nodeList / nodeIndex / nodeGeo / @expose / @library, or when the task involves running `greycat <command>`, deploying a project, or reasoning about gcdata/, lib/, files/, webroot/.

GreyCat C API and GCL Standard Library reference. Use for: (1) Native C development with gc_machine_t context, tensors, objects, memory management, crypto, I/O; (2) GCL Standard Library modules - std::core (Date/Time/Tuple/geospatial types), std::runtime (Scheduler/Task/Logger/User/Security/System/OpenAPI/MCP), std::io (CSV/JSON/XML/HTTP/Email/FileWalker), std::util (Queue/Stack/SlidingWindow/Gaussian/Histogram/Quantizers/Random/Plot); (3) Plugin development patterns - lifecycle hooks, type configuration, nativegen, module-level and type-level function linking, global state, thread safety, conditional logging. Keywords: GreyCat, GCL, native functions, tensors, task automation, scheduler, plugin development.

C tensor computation library for ML inference and training. Use when working with ggml graphs, GGUF model files, backend scheduling, quantization, or implementing low-level ML ops in C/C++.

NVIDIA CUDA parallel computing platform — use when writing .cu kernels, using cuBLAS/cuDNN/cuFFT/cuSPARSE/cuRAND/cuSolver, Thrust, or Cooperative Groups for GPU-accelerated computing

Complete CBLAS and LAPACKE C API reference (LAPACK v3.12.1) covering 1284 functions for numerical linear algebra: BLAS Level 1/2/3 vector and matrix operations, linear system solvers (LU, Cholesky, LDL), eigenvalue/eigenvector computation, singular value decomposition, least squares, QR/LQ factorizations, and auxiliary routines. Triggers on: BLAS/LAPACK questions, CBLAS/LAPACKE code, linear algebra in C/C++, matrix operations, numerical computing, scientific computing, HPC, linking BLAS/LAPACK.

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.

LangChain Ollama integration — run local LLMs with ChatOllama (chat completions, tool calling, structured output, reasoning/thinking mode), OllamaLLM (raw text completions), and OllamaEmbeddings. Connects to a local Ollama server at localhost:11434.