| name | optimize-for-gpu |
| description | GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants to speed up NumPy, pandas, scikit-learn, scikit-image, NetworkX, GeoPandas, or Faiss workloads. Covers physics simulation, differentiable rendering, mesh ray casting, particle systems (DEM/SPH/fluids),. |
| source_skill_id | k-dense-ai-scientific-agent-skills-scientific-skills-optimize-for-gpu-skill-md |
| category | Game, mobile & visual QA |
| source_mirror | ../../../../../skills/by-category/game-mobile-visual-qa/latest-release-community/optimize-for-gpu/SKILL.md |
| benchmark_status | artifact_gated |
optimize-for-gpu
Use this skill when the task matches the description above or the source path clearly applies. Start with this concise entrypoint; open ../../../../../skills/by-category/game-mobile-visual-qa/latest-release-community/optimize-for-gpu/SKILL.md only when implementation details, commands, assets, or references are needed.
Workflow
- Confirm the task matches this skill's scope.
- Read the local source mirror if more detail is required.
- Follow repository-level
AGENTS.md; use one AI session only.
- Keep claims tied to files, commands, citations, or benchmark artifacts.
Verification
- Source mirror:
../../../../../skills/by-category/game-mobile-visual-qa/latest-release-community/optimize-for-gpu/SKILL.md
- Source commit:
eb20fb0dcb0b1dadaa3db2737188f0755bbc4770
- Static benchmark results: see
docs/benchmark-results.md
- Runtime artifacts recorded by this entrypoint:
0
- Assigned scenarios:
skill-proof-k-dense-ai-scientific-agent-skills-scientific-skills-optimize-for-gpu-skill-md, game-mobile-and-visual-qa-air-defense-android-benchmarks, game-mobile-and-visual-qa-coco-captions, game-mobile-and-visual-qa-owasp-juice-shop
Do not claim this skill passed a runtime benchmark until a validated artifact exists.
