| 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
- 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).
- Scaffold —
tools/mkop/mkop.sh <Name> → the wired skeleton; gate with
python3 tools/make_op.py <Name> --phase scaffold (SCF-green).
- 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).
- 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.
- 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.
- 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.
