marketplace

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/Identity/Security/System/License/OpenAPI/MCP), std::io (CSV/JSON/XML/HTTP/Email/FileWalker/S3), std::util (Queue/Stack/SlidingWindow/TimeWindow/Gaussian/Histogram/Quantizers/Random/Uuid/Crypto); (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.

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/.

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.

Complete vLLM v0.19.0 Python API reference for high-throughput LLM inference: offline batch generation, chat, embeddings, classification, structured outputs, LoRA adapters, multimodal inputs, and OpenAI-compatible server. Triggers on: vLLM questions, Python LLM serving, GPU inference, "how do I use vllm", batch inference, vllm serve, OpenAI-compatible API, structured JSON output, LoRA serving.

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

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++.

Run and manage local LLMs via Ollama REST API — text generation, chat completions, embeddings, tool calling, structured output, and model management. Use when code imports ollama, references localhost:11434, or user asks about local LLM inference.

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).

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.

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.