make-op

name

make-op

description

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).

Make Op

Thin entry point. The workflow + rules live in .agents/guidance/MAKE_OP_GUIDELINES.md and the narrative how-to in docs/sphinx/advanced/make_operator.rst; the definition-of-done is gated by the deterministic checker tools/make_op.py. Keep this skill thin; do not duplicate the guidelines.

Workflow

1. Spec & approval (Part 0) — propose the operator's semantics + a cited reference oracle (e.g. mimic TorchVision/OpenCV, or an explicit formula) + API + the support matrix (dtype × channel × layout × container); get user approval (or an explicit oracle waiver for a novel custom op), then record the contract in Op<Name>.h (@brief + Reference: + Limitations matrix).
2. Scaffold — tools/mkop/mkop.sh <Name> → the wired skeleton; gate with python3 tools/make_op.py <Name> --phase scaffold (SCF-green).
3. Implement — kernel + an independent CPU gold reference + tests/bench per the COV-* rules: bit-exact for every declared variant, required equivalent-layout parity, complement negatives, and every declared dtype benched. Image operators support interleaved and planar layouts by default; record an operator-local reason when image layouts do not apply. The scaffold emits the always-on NVTX markers (C-API submit / priv operator() / Python NvtxTrace); keep them and add the op to the OPERATORS registry in tests/cvcuda/python/test_nvtx_markers.py (NVTX-1).
4. Done gate — python3 tools/make_op.py <Name> --phase done --run: composes /review-op (all domains) + /optimize-op preflight and adds COV/EXEC/DOC-REL. Must be green; loop on the verdict — for each GAP apply its named fix and re-run. Inviolable: never fabricate baselines (missing → GAP [requires CI] → CI regen); bit-exact is the default (an EXPECT_NEAR on an interleaved path is a GAP); layout-support gaps are author work.
5. Calibrate + seed baselines — calibrate each bench config to 1–2 ms nvbench GPU time (per-dtype; measure with bench_<op> and adjust the batch), exercise with bench/run_bench.py --operator <op> --lang both (noise < 5% + C++/Python parity), then trigger the named CI baseline-regen workflow to seed baselines on the reference SKUs and import its artifacts with bench/_internal/update_baseline.py --from <artifact-dir> --operator <op>. This closes BEN-7/RDY-1 (never fabricate baselines; absolute timings are SKU-specific, so baselines come from CI). Follow bench/README.md for the workflow and ci/README.md for CI selection.
6. Hand off to /optimize-op <Name> for the performance campaign.

For a wired skeleton with the implementation delegated to a human/other AI, use make-op-scaffold (optionally --bare); make-op-verify is the done-gate. Findings-first.