review-op-test-coverage

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.

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Repository: CVCUDA/CV-CUDA
Last source activity: August 11, 2026 at 21:59
Detected SKILL.md language: English
Stars: 2,729
Forks: 264

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name: review-op-test-coverage
description: 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.

[//]: # "SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved." [//]: # "SPDX-License-Identifier: Apache-2.0" # Review Op — test coverage Thin entry point. Run `python3 tools/review_op.py <Operator> --domain test` and interpret per the **test** section (TST-*) of `.agents/guidance/REVIEW_OP_GUIDELINES.md`: independent gold, required cross-layout parity, axis-coverage mirror, negative tests, tolerance discipline (bit-exact by default), and the Python API surface. Resolve `MANUAL` items at the cited locations. When fixing (`--fix`), author the missing positive, negative, or layout-parity cases - **never introduce or widen a tolerance silently** (emit rationale + needs-human) — and re-run to confirm. Findings-first.

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make-op

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 scaffold, bit-exact regression tests across the declared support matrix, required layout parity, complement negatives, docs + relnote, benched dtypes, tests that run and pass, optimization-ready).

2026-09-16

refactor-op

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 operator's kernels/bindings, remove dead code, or verify a refactor changed nothing observable. Produces a deterministic findings-first report; the apply path is gated by a strict bit-exact / feature-set / test-coverage parity check.

2026-09-16

make-op-scaffold

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. Supports --bare (skip the spec when the definition itself is delegated).

2026-08-11

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