Borrowing it
Nothing to install: this file belongs to maoxx241/vllm-ascend-workspace. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/maoxx241/vllm-ascend-workspace/main/.agents/skills/ascend-triton-operator-development/SKILL.mdgit clone --depth 1 https://github.com/maoxx241/vllm-ascend-workspaceWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/maoxx241/vllm-ascend-workspace/ascend-triton-operator-development)<a href="https://agentmods.dev/skills/maoxx241/vllm-ascend-workspace/ascend-triton-operator-development"><img src="https://agentmods.dev/badge/skills/maoxx241/vllm-ascend-workspace/ascend-triton-operator-development/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/maoxx241/vllm-ascend-workspace/ascend-triton-operator-development"><img src="https://agentmods.dev/badge/skills/maoxx241/vllm-ascend-workspace/ascend-triton-operator-development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00118 | $0.00648 |
| Opus 5 | $0.00059 | $0.00324 |
| Sonnet 5 | $0.00024 | $0.00130 |
| Haiku 4.5 | $0.00012 | $0.00065 |
Grade A, and why
ascend-triton-operator-development scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ascend Triton Operator Development
Produce a traceable candidate and prove its correctness through the validation Skill before claiming success.
Workflow
- Record the exact source, reference, target SoC, CANN/Triton-Ascend versions, supported shapes, dtypes, layouts, strides, scalar options, tolerances, and side effects.
- Query
.agents/knowledge/for target capability and known failure signatures. Treat absent facts as unknown. - Run
scripts/triton_development.py planto create the task contract and development Run Manifest. - Complete the generated semantic report before changing code. For GPU Triton input, audit every load, store, mask, index, grid dimension, reduction identity, atomic, and alias.
- Write one hardware-aware sketch: logical work, physical-core mapping, tile sizes, estimated UB live set, padding semantics, and specialization boundaries.
- Implement the smallest correct candidate. Keep the host wrapper limited to allocation, metadata extraction, dispatch, and launch; keep core computation in
@triton.jit. - Use
ascend-triton-kernel-validationon a managed remote NPU. Do not runtorch_npulocally. - Run
finalizewith the candidate, completed audit, sketch, and terminal validation manifest.
Entry point
scripts/triton_development.py provides:
plan: validate the task config and create audit/sketch templates plus Run Manifest v1;finalize: hash and register the candidate artifacts, consume terminal correctness evidence, and generate the development report.
Read:
- Behavior contract for task and finalization semantics.
- Semantic review before migrating or implementing.
- Architecture and code generation while designing the kernel.
- Command recipes for controller usage.
- Acceptance before claiming the first correct kernel exists.
Rules
- Preserve the reference semantics; do not optimize away masks, padding, dtype width, or side effects without proof.
- Do not hard-code core count, UB capacity, alignment, or compiler capability across SoCs and versions.
- Keep all planned cases; never reduce a multi-shape task to the easiest case.
- Do not use GPU latency as the NPU acceptance baseline.
- A development run passes only when its linked validation manifest passes.
- Keep run state under
.vaws-local/ascend-triton/development/.
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 270 B
- references/acceptance.md 971 B
- references/architecture-and-codegen.md 2.3 KB
- references/behavior.md 2.1 KB
- references/command-recipes.md 1.0 KB
- references/semantic-review.md 2.7 KB
- scripts/triton_development.py 15 KB runs code
- tests/test_triton_development.py 4.2 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 44 lines · 118 tokens per session scan A e125803f09f1
ascend-triton-operator-development is a skill published in the GitHub repository maoxx241/vllm-ascend-workspace (36 stars, last pushed 7d ago), licensed MIT. It adds 118 tokens to every session and 648 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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