We are Datathings, specializing in high-performance software for large-scale data infrastructure. Our foundation is GreyCat — a temporal graph database and programming language built for efficiency at scale, with native agentic AI capabilities. On that foundation, we built Kopr: a digital twin managing Luxembourg's entire electricity distribution grid — 1 million grid assets, 330,000 delivery points, and 45 billion meter readings per year, with machine learning running continuously over live sensor data.
The plugins here bring that stack to your AI agent: GreyCat's runtime and language tools, the numerical and GPU computing libraries behind high-performance inference and optimization, and widely-used power systems analysis frameworks for anyone building in that domain.
Install GreyCat:
Linux, Mac or FreeBSD (x64, arm64):
curl -fsSL https://get.greycat.io/install.sh | bash -s devWindows (x64, arm64):
iwr https://get.greycat.io/install_dev.ps1 -useb | iexInstall Claude Code:
Follow the installation instructions at https://code.claude.com/docs/en/setup
Add the marketplace:
/plugin marketplace add datathings/marketplace
Install plugins:
/plugin install greycat@datathings
Try it:
Run Claude in a demo folder, then paste this prompt:
Use your greycat skill to create a GreyCat backend with Country, City, Street, House, and Person nodes linked as a geographic hierarchy with back references for bidirectional navigation (country contains cities, cities contain streets, etc., and children reference their parents). Add geo coordinates (latitude, longitude) to appropriate nodes. Houses should have temperature sensors storing time series data. Generate two sample CSV files: `./data/addresses.csv` (with house IDs) and `./data/temperatures.csv` (with house_id, date, value columns), and create an importer that loads both on startup (import the CSVs on main if the country index size is 0). Expose all important API endpoints. Create comprehensive API documentation and expose meaningful functions as MCP.
| Plugin | Category | Type | Version | Description |
|---|---|---|---|---|
| GreyCat Technology | ||||
| greycat | GreyCat Technology | Skill | 2.5.7 | Full-stack GreyCat development — GCL language, graph persistence, LSP, frontend integration |
| greycat-c | GreyCat Technology | Skill | 2.5.7 | GreyCat C API and Standard Library for native development |
| Agentic AI | ||||
| llamacpp | Agentic AI | Skill | 2.5.7 | llama.cpp C API reference (163 functions) for local LLM inference |
| ggml | Agentic AI | Skill | 2.5.7 | ggml C tensor library (560+ functions) for graph computation, GGUF I/O, multi-backend inference, and ML training |
| vllm | Agentic AI | Skill | 2.5.7 | vLLM v0.16.0 — high-throughput Python LLM inference with offline batch, OpenAI-compatible server, LoRA adapters, multimodal inputs, and structured outputs |
| ollama | Agentic AI | Skill | 2.5.7 | Ollama v0.16.3 — run and interact with local LLMs via REST API (chat, generate, embed, model management) |
| High Performance Math & GPU | ||||
| blas_lapack | High Performance Math & GPU | Skill | 2.5.7 | CBLAS & LAPACKE C API reference (1284 functions) for numerical linear algebra |
| cuda | High Performance Math & GPU | Skill | 2.5.7 | NVIDIA CUDA C/C++ — Runtime API, cuBLAS, cuFFT, cuSPARSE, cuRAND, cuSolver, Thrust, Cooperative Groups |
| opencl | High Performance Math & GPU | Skill | 2.5.7 | OpenCL SDK (Khronos) — cross-platform GPU/CPU parallel computing, C API (~60 functions), C++ wrapper (opencl.hpp), SDK utilities |
| rocm | High Performance Math & GPU | Skill | 2.5.7 | AMD ROCm 7.2.0 — HIP kernel development, rocBLAS/rocFFT/rocRAND/rocSOLVER libraries, profiling, and CUDA-to-HIP porting |
| Power Grid Management | ||||
| pandapower | Power Grid Management | Skill | 2.5.7 | pandapower v3.4.0 — Python power systems analysis with AC/DC power flow, OPF, short circuit (IEC 60909), state estimation, and visualization |
| powergridmodel | Power Grid Management | Skill | 2.5.7 | power-grid-model v1.13.10 — high-performance Python library for steady-state distribution power system analysis: power flow, state estimation, and IEC 60909 short-circuit calculations |
GreyCat is both a database and a programming language — stateful, graph-native, and designed to expose functions directly as HTTP APIs or MCP endpoints. Install these plugins when building with GreyCat or extending it at the native level.
/plugin install greycat@datathings
/plugin install greycat-c@datathings
The inference stack for running AI locally: ggml provides the tensor computation engine and GGUF model format, while llama.cpp builds a complete LLM inference API on top of it. Both plug directly into GreyCat-backed applications or any native pipeline.
/plugin install llamacpp@datathings
/plugin install ggml@datathings
/plugin install vllm@datathings
/plugin install ollama@datathings
The compute stack for high-performance numerical work: foundational linear algebra (BLAS/LAPACK) and full GPU acceleration across NVIDIA CUDA, OpenCL, and AMD ROCm.
/plugin install blas_lapack@datathings
/plugin install cuda@datathings
/plugin install opencl@datathings
/plugin install rocm@datathings
Kopr — our electricity distribution digital twin built on GreyCat — manages 1 million grid assets and 45 billion annual meter readings, with optimal power flow and grid analysis capabilities built in. We include these libraries because they were instrumental in our testing and validation work.
/plugin install pandapower@datathings
/plugin install powergridmodel@datathings
Activates on .gcl files and GreyCat projects. Provides:
- GCL syntax, types, decorators (@expose, @permission, @volatile)
- Indexed collections (nodeIndex, nodeList, nodeTime, nodeGeo)
- Concurrency patterns (Jobs, await)
- Standard library (core, io, runtime, util)
- Pro libraries (ai, algebra, finance, kafka, opcua, powerflow, s3, sql, useragent)
- Frontend integration (@greycat/web SDK)
- LSP support (completion, diagnostics, hover, go-to-def) — requires
greycat-langin PATH
Reference for native C development with GreyCat:
- C API functions, tensor operations
- Native function implementation
Complete llama.cpp C API (163 functions):
- Model loading, inference, tokenization
- Sampling strategies (XTC, DRY, infill)
- GGUF model support
C tensor computation library powering llama.cpp and many ML inference engines (v0.9.7, 560+ functions):
- Lazy computation graph with CPU/GPU/Metal/Vulkan backends and automatic multi-backend scheduling
- 40+ quantization formats (Q4_0 → Q5_K), GGUF v3 I/O, Flash Attention, RoPE, AdamW/SGD training
vLLM (v0.16.0) — high-throughput Python inference engine for large language models:
- Offline batch inference (
LLMclass) and OpenAI-compatible server (vllm serve) with streaming - LoRA adapters, multimodal inputs, structured outputs (JSON/regex/grammar), and paged attention
Ollama (v0.16.3) — local LLM runtime with a simple REST API on localhost:
- Text generation, chat, and embeddings via REST API with streaming support
- Model management (pull/push/delete) and custom model creation via Modelfile
Complete CBLAS & LAPACKE C API (1284 functions, LAPACK v3.12.1):
- BLAS Level 1/2/3 vector and matrix operations; linear solvers (LU, Cholesky, LDL)
- Eigenvalue/SVD/least squares decompositions; QR/LQ factorizations
NVIDIA CUDA parallel computing platform (cuda-samples v13.1, CUDALibrarySamples main). Complete reference for GPU-accelerated C/C++ development:
- Runtime API (device, memory, streams, kernel launch); math libraries: cuBLAS, cuFFT, cuSPARSE, cuRAND, cuSolver
- Thrust (STL-like GPU algorithms) and Cooperative Groups (thread synchronization)
Khronos Group OpenCL SDK (v2025.07.23) for cross-platform GPU/CPU parallel computing in C and C++:
- C API (~60 functions) and C++ wrapper (opencl.hpp with RAII types) for platform/device management, memory, and kernels
- Full NDRange execution (1D/2D/3D), events, profiling, and out-of-order queues
AMD ROCm GPU computing stack (rocm-7.2.0) for HIP-based GPU development:
- HIP C++ kernels with full compute libraries (rocBLAS, rocFFT, rocRAND, rocSOLVER, rocSPARSE, rocWMMA)
- Profiling (rocProfiler, rocm-smi) and CUDA portability via hipify-perl
pandapower (v3.4.0) — Python library for modeling and analyzing electric power networks:
- AC/DC power flow, optimal power flow, short-circuit (IEC 60909), and state estimation
- 15+ benchmark networks (IEEE, CIGRE, Kerber) and visualization with matplotlib/plotly
power-grid-model (v1.13.10) — high-performance Python/C++ library for steady-state distribution power system analysis:
- Symmetric and asymmetric three-phase power flow, state estimation, and IEC 60909 short-circuit analysis
- Batch/N-1 contingency analysis with multi-threaded parallel execution; 22 component types
The ./skills/ folder contains pre-packaged .skill files (zip archives) for use with other AI tools or manual installation:
skills/
├── greycat.skill # GreyCat full-stack development
├── greycat-c.skill # GreyCat C API reference
├── llamacpp.skill # llama.cpp C API reference
├── blas_lapack.skill # CBLAS & LAPACKE C API reference
├── ggml.skill # ggml C tensor library
├── cuda.skill # NVIDIA CUDA C/C++ GPU programming
├── opencl.skill # OpenCL cross-platform GPU/CPU parallel computing
├── rocm.skill # AMD ROCm GPU computing (HIP + libraries)
├── pandapower.skill # pandapower Python power systems analysis
├── powergridmodel.skill # power-grid-model Python distribution power system analysis
├── vllm.skill # vLLM high-throughput Python LLM inference
└── ollama.skill # Ollama local LLM runtime REST API
Each .skill file contains a SKILL.md with instructions and optional references/ documentation. To regenerate:
./package.sh # Interactive skill selection
./package.sh -a # Package all skills
./package.sh greycat # Package specific skillAdd to .claude/settings.json (project or ~/.claude/settings.json for global):
{
"extraKnownMarketplaces": {
"datathings": {
"source": { "source": "github", "repo": "datathings/marketplace" }
}
},
"enabledPlugins": {
"greycat@datathings": true
}
}/plugin list # List installed plugins
/plugin update greycat@datathings # Update a plugin
/plugin uninstall greycat@datathings # Remove a plugin
/plugin marketplace list # List marketplaces
/plugin marketplace remove datathings # Remove marketplaceFor developing or testing marketplace plugins locally:
/plugin marketplace add /path/to/marketplaceUpdate all plugin versions at once:
./bump-version.sh # Show current versions
./bump-version.sh 1.3.0 # Bump all plugins to 1.3.0Generate standalone .skill files for distribution:
./package.sh # Interactive skill selection
./package.sh -a # Package all skills
./package.sh greycat # Package specific skillTo develop native GreyCat C libraries (custom functions implemented in C), install the greycat-c plugin:
/plugin install greycat-c@datathingsThis provides the C API reference, tensor operations, and native function implementation patterns required for extending GreyCat with C code.
Skills not activating: Verify with /plugin list, ensure enabled in settings.
LSP not working: Check greycat-lang --version is installed and in PATH. The LSP is included in the greycat plugin.
- GreyCat: https://greycat.io | https://doc.greycat.io | https://get.greycat.io
- Datathings: https://datathings.com
- Kopr: https://kopr-twin.com
- llama.cpp: https://github.com/ggml-org/llama.cpp
- ggml: https://github.com/ggml-org/ggml
- LAPACK: https://github.com/Reference-LAPACK/lapack
- CUDA samples: https://github.com/NVIDIA/cuda-samples
- CUDA Library Samples: https://github.com/NVIDIA/CUDALibrarySamples
- OpenCL SDK: https://github.com/KhronosGroup/OpenCL-SDK
- ROCm: https://github.com/ROCm/ROCm
- ROCm Examples: https://github.com/ROCm/rocm-examples
- vLLM: https://github.com/vllm-project/vllm
- Ollama: https://github.com/ollama/ollama
- pandapower: https://github.com/pandapower/pandapower
- power-grid-model: https://github.com/PowerGridModel/power-grid-model
- Support: contact@datathings.com
- Issues: https://github.com/datathings/marketplace/issues
Apache-2.0
We're Datathings — the team behind GreyCat and Kopr, Luxembourg's electricity distribution digital twin.
If you're exploring GreyCat for your infrastructure, building agentic AI into production systems, or working on large-scale grid operations and want to talk to people who've done it — reach out at contact@datathings.com.