cuda

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.

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