Python · By CVCUDA

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).

CVCUDA/CV-CUDA

Drive a single-operator optimization campaign per .agents/guidance/OPTIMIZATIONGUIDELINES.md, with a deterministically enforced definition-of-done and versioned MR summary.

CVCUDA/CV-CUDA

Review a CV-CUDA operator end-to-end (support / test / bench / docs coverage).

CVCUDA/CV-CUDA

Add a new CV-CUDA operator end-to-end per .agents/guidance/MAKEOPGUIDELINES.md, with a deterministically-enforced definition-of-done.

CVCUDA/CV-CUDA

Scaffold a new CV-CUDA operator — a complete, wired, building skeleton — and delegate the implementation to a human or another AI.

CVCUDA/CV-CUDA

Verify a new CV-CUDA operator against the deterministic final regression checklist (the /make-op done-gate).

CVCUDA/CV-CUDA

Review a CV-CUDA operator's BENCHMARK coverage — drivers, layout axis, baselines, the basic-tier floor, row counts, and coverage statistics.

CVCUDA/CV-CUDA

Review a CV-CUDA operator's DOCS & API artifacts — operatorlist row, Python autofunction (fn + into), Limitations-table-vs-code consistency, docstrings, and SPDX headers.

CVCUDA/CV-CUDA

Review a CV-CUDA operator's input-type, layout, dtype, and channel support matrix.

CVCUDA/CV-CUDA

Review a CV-CUDA operator's test coverage, including C++ correctness, required cross-layout parity, correctness rigor, and the Python API surface.

CVCUDA/CV-CUDA

Check whether a CV-CUDA operator is READY to optimize (correctness + bench coverage + captured baseline + profiling) per .agents/guidance/OPTIMIZATIONGUIDELINES.md.

CVCUDA/CV-CUDA