---
name: ascend-triton-operator-development
description: Develop a first correct Ascend Triton operator from a PyTorch reference or migrate an existing GPU Triton kernel to Ascend, including semantic audit, explicit task contracts, hardware-aware grid and tiling design, implementation, and handoff to correctness validation. Use for new kernel implementation, CUDA/GPU Triton migration, or repairing a candidate that has not yet passed correctness. Do not use for a kernel that already passes all planned cases and only needs performance tuning, for isolated torch_npu or ACLNN debugging, or for model-level graph failures.
---

# ascend-triton-operator-development

Implement a first correct Ascend Triton operator or migrate an existing GPU Triton kernel.

Resolve semantics from the reference and callers before selecting a grid or tile. Separate logical shape from physical layout and reductions. GPU launch assumptions need an Ascend-specific design; choose a simple correct candidate before tuning.

## Agent entry

Run from the repository root using the platform's Python launcher. The workspace
selects its installed platform environment automatically.

```text
python .agents/skills/ascend-triton-operator-development/scripts/triton_development.py --config operator.json --kernel kernel.py --validation-manifest validation/manifest.json
```

The business config contains op_name, mode, source, reference, target, cases and tolerances. The report consumes the actual kernel and validation manifest, checking kernel identity and passing case coverage. Optional --semantic-report and --sketch attach useful design artifacts.

Run ascend-triton-kernel-validation for the candidate. Continue to optimization only after the planned correctness cases pass.

Read the relevant detail only when needed:

- [behavior](references/behavior.md)
- [semantic review](references/semantic-review.md)
- [architecture and codegen](references/architecture-and-codegen.md)

- [Business input example](references/inputs.md)