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SkillsMP has collected 12 skills from CVCUDA/CV-CUDA. Open a skill to review its source and details.
Showing 12 of 12 collected skills.
Scaffold a new CV-CUDA operator — a complete, wired, building skeleton — and delegate the implementation to a human or another AI. Use when asked to set up / stub out a new operator without implementing it, or to bootstrap one for someone else to finish.…
Add a new CV-CUDA operator end-to-end per .agents/guidance/MAKE_OP_GUIDELINES.md, with a deterministically-enforced definition-of-done. Use when asked to create/add a new operator, scaffold one, or verify that a new operator is complete (approved spec, wired…
Verify a new CV-CUDA operator against the deterministic final regression checklist (the /make-op done-gate). Use when asked whether a new operator is complete/done, or to gate it before merge - gold reference, bit-exact coverage across the declared support…
Check whether a CV-CUDA operator is READY to optimize (correctness + bench coverage + captured baseline + profiling) per .agents/guidance/OPTIMIZATION_GUIDELINES.md. Use before starting an optimization campaign to confirm the readiness gate is clean.
Drive a single-operator optimization campaign per .agents/guidance/OPTIMIZATION_GUIDELINES.md, with a deterministically enforced definition-of-done and versioned MR summary. Use when asked to optimize an operator, run or finish a performance campaign,…
Verify a CV-CUDA optimization campaign's deterministic definition-of-done and concise versioned MR summary per .agents/guidance/OPTIMIZATION_GUIDELINES.md. Use to gate whether a perf campaign or performance MR is ready, including reference-SKU statistics,…
Find and safely apply per-operator refactoring / redundancy-reduction opportunities in a CV-CUDA operator (near-duplicate Tensor/VarShape kernels, reinvented shared utilities, dead code). Use when asked to reduce code duplication, de-duplicate or unify an…
Review a CV-CUDA operator's BENCHMARK coverage — drivers, layout axis, baselines, the basic-tier floor, row counts, and coverage statistics. Use when asked whether an operator's benchmarks/baselines are complete or to find/fill bench gaps.
Review a CV-CUDA operator's DOCS & API artifacts — operator_list row, Python autofunction (fn + _into), Limitations-table-vs-code consistency, docstrings, and SPDX headers. Use when asked whether an operator's docs/API surface is complete and consistent.
Review a CV-CUDA operator end-to-end (support / test / bench / docs coverage). Use when the user asks to review an operator, audit its input-type/layout/dtype support, test coverage, benchmark coverage, or docs/API, or to find & fix per-operator coverage…
Review a CV-CUDA operator's input-type, layout, dtype, and channel support matrix. Use when asked which inputs an operator supports, whether required layouts are complete, or whether support is enforced consistently across C, C++, and Python APIs.
Review a CV-CUDA operator's test coverage, including C++ correctness, required cross-layout parity, correctness rigor, and the Python API surface. Use when asked whether an operator is adequately tested or to find and fill test gaps.