cuda-graphs
Expert skill for CUDA Graph capture and optimization for reduced launch overhead. Capture CUDA operations into graphs, instantiate and execute graph instances, update graph node parameters, profile graph vs stream execution, design graph-friendly kernel patterns, and optimize launch latency for inference.
npx skills add a5c-ai/babysitter --skill cuda-graphs --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# cuda-graphs You are **cuda-graphs** - a specialized skill for CUDA Graph capture and optimization. This skill provides expert capabilities for reducing kernel launch overhead and optimizing execution patterns through graph-based workflows. ## Overview This skill enables AI-powered CUDA Graph operations including: - Capturing CUDA operations into graphs - Instantiating and executing graph instances - Updating graph node parameters - Profiling graph vs stream execution - Designing graph-friendly kernel patterns - Handling conditional graph execution - Integrating graphs with NCCL operations - Optimizing launch latency for inference ## Prerequisites - NVIDIA CUDA Toolkit 10.0+ (basic graphs) - CUDA 11.0+ for graph updates - CUDA 12.0+ for conditional nodes - GPU with compute capability 7.0+ - Nsight Systems for graph profiling ## Capabilities ### 1. Stream Capture Basic Capture stream operations into a graph: domains: [domain:scientific-computing] specializations: [specialization:gpu-programming] skillAreas: [skill-area:cuda-kernels, skill-area:compute-shaders, skill-area:profiling-cuda] roles: [role:computational-scientist, role:ml-engineer] ```cuda #include <cuda_runtime.h> cudaGr
- Overview
- Prerequisites
- Capabilities
- 1. Stream Capture Basic
- 2. Explicit Graph Construction
- 3. Graph Node Types
- 4. Graph Updates (CUDA 11+)
- 5. Kernel Node Parameter Updates
- 6. Graph Performance Benchmarking
- 7. Inference Pipeline with Graphs
- 8. Conditional Graphs (CUDA 12+)
- 9. Graph Debugging and Visualization
- Best Practices
- When to Use CUDA Graphs
What does the cuda-graphs skill do?
Expert skill for CUDA Graph capture and optimization for reduced launch overhead. Capture CUDA operations into graphs, instantiate and execute graph instances, update graph node parameters, profile graph vs stream execution, design graph-friendly kernel patterns, and optimize launch latency for inference.
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill cuda-graphs --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.