| skill | cuda |
| name | cuda |
| version | 1.0.0 |
| description | AI-powered CUDA development with 4 specialist agents (General, Optimizer, Debugger, Analyzer) plus an MCP toolset. Use when writing CUDA kernels (.cu/.cuh), optimising GPU code (coalescing, shared memory, occupancy), debugging nvcc compilation or race conditions, or profiling GPU performance with nsys/ncu. NOT for high-level PyTorch training without custom kernels (use pytorch-ml), Rust/C++ systems work without GPU (use rust-development), or CPU-only profiling (use performance-analysis). |
| tags | ["cuda","gpu","nvidia","optimisation","kernel","parallel-computing","nvcc","profiling","debugging"] |
| mcp_server | true |
| entry_point | mcp-server/server.py |
| protocol | stdio |
| compatibility | ["cuda >= 11.0","nvidia-driver >= 470.0","python >= 3.9"] |
CUDA Specialist Skill
AI-powered CUDA development assistant integrating 4 specialist agents with comprehensive GPU development tools.
When Not To Use
- For high-level PyTorch model training that does not need custom kernels -- use the pytorch-ml skill instead
- For distributed neural network training in cloud sandboxes -- use the flow-nexus-neural skill instead
- For general Rust or C++ systems programming without GPU involvement -- use the rust-development skill instead
- For WebAssembly-based compute without GPU -- use the wasm-js skill instead
- For CPU-only performance profiling of swarm operations -- use the performance-analysis skill instead
Overview
The CUDA skill brings professional CUDA development capabilities to Claude Code through:
- 4 AI Specialist Agents: General, Optimizer, Debugger, Analyzer
- CUDA Compilation: nvcc integration with auto-architecture detection
- Kernel Analysis: Pattern detection and optimisation opportunities
- GPU Profiling: Performance measurement and bottleneck identification
- Code Generation: Create optimised kernels from specifications
Reference Files
Load the reference that matches the task:
- references/agents.md — the 4 specialist agents (General, Optimizer, Debugger, Analyzer): when to use each and invocation examples.
- references/mcp-tools.md — full MCP tool catalogue (kernel dev, GPU management, agent routing, file ops) plus JSON call examples.
- references/environment.md — CUDA toolkit checks, container GPUs, and PyTorch CUDA verification.
- references/examples.md — quick-start prompts and integration patterns with PyTorch and Rust skills.
- references/optimisation.md — performance checklist, advanced compilation flags, nsys/ncu profiling, benchmarking, and best practices.
- references/troubleshooting.md — fixes for CUDA-not-found, GPU-not-accessible, and compilation errors.
Related Skills
