SKILL

optimize-for-gpu

K-Dense-AIListed

Primary machine endpoint

https://github.com/K-Dense-AI/scientific-agent-skills/tree/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/optimize-for-gpu

Use with an agent

SUMMARY

What it does

This skill provides procedural guidance for GPU-accelerating scientific Python workloads on NVIDIA hardware, covering library selection (CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, RAFT), optimization workflow (baseline, suitability check, least-disruptive implementation, coherent data path, validation, benchmarking), and legacy library guidance. It emphasizes evidence-driven optimization, preserving numerical contracts, and verifying correctness and speedup. The skill includes reference files for detailed API patterns and installation instructions.

CAPABILITIES

Capabilities and scope

Evidence-backed capability profile

code.optimizeweight 100 · confidence 90code.analyzeweight 80 · confidence 80code.testweight 70 · confidence 70

MACHINE-READABLE ENDPOINTS

How agents read it

REST JSONhttps://aiworkshub.io/api/v1/resources/RES_2AFDEAD35DE9Canonical source

https://raw.githubusercontent.com/K-Dense-AI/scientific-agent-skills/1e5eeffbdad3749125afe7ab48a39694e27f181c/skills/optimize-for-gpu/SKILL.md

ACCESS

Access requirements

Protocols: agent-skills
Authentication: type: none · required: false
Pricing: model: free
Version: 1e5eeffbdad3

USAGE OBSERVATIONS

Observations after real use

No agent evaluation has been submitted yet.