Add a new operation to the OpenVINO GPU plugin — OpenCL kernel design, oneDNN-backed paths, sub-group/LWS tuning, and functional tests.
57
66%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Low
Low-risk findings worth noting
Fix and improve this skill with Tessl
tessl review fix ./.github/agents-prototype/skills/add-gpu-op/SKILL.mdWhen the Core OpSpec agent has produced a new op spec and you need to implement GPU plugin support: kernel implementation, registration, and testing.
Execute in order — each step produces artifacts consumed by the next.
| Step | File | Purpose |
|---|---|---|
| 0a | step0-plan.md | Read op spec, build implementation plan, decide kernel vs oneDNN path |
| 0b | step0-parse-spec.md | Parse op spec JSON/MD into GPU-readable format |
| 1 | step1-hardware-analysis.md | Identify hardware constraints, sub-group size, memory layout requirements |
| 2 | step2-file-structure.md | Create kernel and primitive files, register in factory |
| 3a | step3-kernel-development.md | Write OpenCL kernel (blocked reads, sub-groups, LWS tuning) |
| 3b | step3-write-tests.md | Write layer tests |
| 3c | step3-run-tests.md | Run and verify tests |
| 3d | step3-profiling.md | Profile and tune kernel |
| 4 | step4-onednn-integration.md | Add oneDNN-backed primitive path where applicable |
| 5 | step5-optimize.md | Final performance optimizations |
When invoked by the GPU Agent or Enable Operator Agent, start from orchestrator.md — it selects the relevant subset of steps based on the op type and available hardware.
4c20980
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.