By datathings
Perform high-performance steady-state analysis on distribution power systems—including power flow, state estimation, and IEC 60909 short-circuit calculations—using Python's power-grid-model library with numpy structured arrays for batch and parallel simulations across 22 component types.
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npx claudepluginhub datathings/marketplace --plugin powergridmodelComplete llama.cpp C/C++ API reference (v b7885) covering 198 functions: model loading, inference, text generation, embeddings, chat, advanced sampling (XTC, DRY, infill), per-sequence state management, model type detection, and more. For GGUF models, local LLM inference, and C/C++ AI development.
Comprehensive GreyCat development skill for graph-based language with built-in persistence. Covers data modeling, API development, parallel processing, frontend integration, and all standard libraries.
pandapower v3.4.0 Python skill - power systems analysis with 80+ functions for AC/DC power flow, OPF, short circuit (IEC 60909), and state estimation
ggml v0.9.7 C tensor library skill — 560+ functions for graph computation, GGUF I/O, multi-backend inference, and ML training
Comprehensive reference for GreyCat C API and GCL Standard Library. Covers native function implementation, tensor operations, scheduling, I/O, statistics, and all std modules.
pandapower v3.4.0 Python skill - power systems analysis with 80+ functions for AC/DC power flow, OPF, short circuit (IEC 60909), and state estimation
Progressive-disclosure workflows for power-system software exposed by PowerMCP: ANDES, Egret, LTSpice, OpenDSS, PSLF, PSS/E, PowerWorld, PyPSA, pandapower, and surge. Each skill exposes load, inspect, solve, modify, then advanced studies, so an agent starts with the lowest-risk action and only escalates once the base case is credible.
LTspice/ngspice circuit simulation with structured results — including per-device operating points (gm/gds/vth) read back by name, no rawfile parsing — plus LTspice .asc schematic editing, exposed as MCP tools. Tracks the latest ltspice-mcp release on PyPI.
Pandas MCP - Advanced Data Analysis for LLMs with comprehensive pandas operations
Data engineering and time series analysis mastery. Expert in jq, SQL, pandas, time series forecasting, ETL pipelines, streaming, and analytics visualization.
Skills for NVIDIAs ecosystem spans GPU acceleration, CUDA, AI agents, inference, robotics, Physical AI, Omniverse, and simulation. This plugin helps you understand the pieces, choose a path, validate your setup, and build practical NVIDIA-powered workflows.