Agent skill · nvidia
warp-eval
Evaluate whether an existing hot path is a credible NVIDIA Warp candidate. Use for irregular or spatial queries, particle or geometry simulation, branch-heavy loops, many small launches, host fallbacks, or large intermediates. CPU-only code and absent GPU dependencies are normal unless NVIDIA is prohibited. Exclude required cross-vendor or CPU-only deployment, vendor-lowered dense or NN layers, general Warp API questions, and already-selected Warp kernels. Contribution policy alone is not exclusion.
What it needs
About 11k tokens when loaded.
What this skill does
Warp evaluation Purpose Collect reproducible evidence about how a narrow seam in an existing codebase would behave in NVIDIA Warp. Report facts; the user decides. Name Warp as the option under evaluation in the first line, state that no adoption recommendation will follow, and do not treat the triggering performance request as authorization to experiment. Measured evaluations produce warp-evaluation-report/: the report, one independently applicable diff per solution, the drivers, and raw results. Never modify production code. Exits before measured work create no directory. Hard rules These override any local reasoning. 1. Default objectives are latency, throughput, and peak or retained memory. Count maintainability, ergonomics, packaging, extensibility, autodiff or new functionality only when the user names it; otherwise report them as constraints or costs, not benefits. 2. Correctness is a gate. If the incumbent is buggy or its contract unclear, abort that comparison until an independent oracle or clarified contract exists. Report the defect without prescribing a response. 3. Never predict savings from source shape or another workload. Materialized memory comes from profiler or allocator evidence, not source a compiler may fuse. 4. "It can be written in Warp" is a hypothesis, never a proven opportunity. 5. Gates are exact, never adjacent or analogous. Fire a gate only when every condition in its definition is established. Gate D requires a maintained implementation confirmed to execute with CUDA on an NVIDIA GPU; a fast native CPU library is a baseline, not Gate D. 6. Label every claim as observed fact, measurement, hypothesis or unknown. 7. Time the user-visible stage, not the kernel. Include Warp's cold import/init/JIT in the real process regime, transfers, launches, Python launch loops, structure build/refit, allocation, conversion, validation, compaction and synchronization. Report each cost and the end-to-end difference. 8. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/warp-eval