rust-gpu-discipline

rust-gpu-discipline is a Go, Rust & systems languages skill for AI agents, published by forecast-bio in the ferray repository on GitHub. Skillspace rates rust-gpu-discipline D (69/100) and it passed the safety scan; the rating is based on static analysis only.

Use this skill aggressively any time the work involves Rust code that touches GPU compute — including ferrotorch (and ferrotorch-* crates), burn, candle, wgpu-rs, cubecl, cudarc, or any project involving CUDA, ROCm, Metal, Vulkan, WGPU, cuBLAS, PTX, WGSL, or kernel dispatch. Trigger on any mention of `.cuda()`, `Device::Cuda(_)`, `.to_device()`, kernel launches, tensor ops, autograd backward passes, JIT codegen, or "GPU acceleration" in a Rust context. Also trigger when the user names a Rust GPU/ML project (ferrotorch, ferray, burn, candle) or asks to implement, port, optimize, or debug compute kernels. This skill exists because the model otherwise systematically defaults to CPU implementations, stubs out GPU code paths, and reports "done" while the work secretly executes on CPU. Use this skill to enforce pre-flight planning, the forbidden-pattern checklist, mechanical verification, and honest reporting so that GPU work actually lands on the GPU.

Grade

D

Quality

69 / 100

Safety

Passed

GitHub stars

22

Unproven: rated by static analysis only, not yet compared head-to-head.

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

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