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vllm-ascend-workspace: Skill for Codex

.agents/skills/ascend-triton-kernel-optimization/SKILL.md

ascend-triton-kernel-optimization is a skill for Codex from maoxx241/vllm-ascend-workspace. It costs 125 tokens per session (680 once invoked), scanned A, original, MIT.

A workflow for improving one already-correct Ascend Triton kernel, a GPU-like program designed for Huawei Ascend NPUs. It measures the kernel against a device baseline and keeps evidence for each change.

In plain words
What is it for?
Use it to profile per-input-shape performance, identify memory-transfer or arithmetic bottlenecks, test one optimization at a time, and record whether each change should be kept or discarded.
Why use it?
It reduces guesswork when optimizing hardware-specific code by separating real improvements from measurement noise. It also prevents an optimization from replacing the best known version unless the full correctness checks still pass.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: installed under .agents/ (shared by several agents).

This is maoxx241/vllm-ascend-workspace's own configuration. It tells Codex how to work on vllm-ascend-workspace itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything vllm-ascend-workspace configures →

not rated 36repo 2mo ago A scan SkillSpector: pass 125 tokens original MIT
View source ↗ maoxx241/vllm-ascend-workspace
Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/maoxx241/vllm-ascend-workspace/main/.agents/skills/ascend-triton-kernel-optimization/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/maoxx241/vllm-ascend-workspace

Made for: Codex.

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README.md
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<a href="https://agentmods.dev/skills/maoxx241/vllm-ascend-workspace/ascend-triton-kernel-optimization"><img src="https://agentmods.dev/badge/skills/maoxx241/vllm-ascend-workspace/ascend-triton-kernel-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 125 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 680 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00125 $0.00680
Opus 5.5 $0.00050 $0.00272
Sonnet 5.5 $0.00025 $0.00136
Haiku 4.5 $0.00013 $0.00068

Measured 2mo ago against content hash 4cabf6f4b4bd, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ascend-triton-kernel-optimization 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 2mo ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/triton_optimization.py, tests/test_triton_optimization.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.agents/skills/ascend-triton-kernel-optimization/SKILL.md · 45 lines

How it starts

The opening of the file, as written. The whole thing — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Ascend Triton Kernel Optimization

Optimize one correct kernel against a comparable NPU baseline. Preserve correctness and evidence integrity at every round.

Workflow

  1. Require a passed ascend-triton-kernel-validation manifest for the exact candidate hash and case set.
  2. Record target SoC/software versions, NPU baseline, warmup/repeat policy, per-case medians, noise threshold, case-regression limit, and performance objective.
  3. Query .agents/knowledge/ with current profiler signals and failure signatures before selecting an optimization.
  4. Run scripts/triton_optimization.py plan to lock the baseline, objective, cases, and starting kernel.
  5. Use msprof op or the target version's supported operator profiler to compare device time, block count, MTE2/MTE3, Vector, Scalar/FLOWCTRL, and pipeline gaps against theoretical floors.
  6. Form one falsifiable hypothesis, change one primary mechanism, and rerun the full correctness gate on the candidate.
  7. Normalize per-case measurements and run record. The controller chooses KEEP, DISCARD, NOISE, or FAIL; never overwrite the best kernel.
  8. After repeated failures, enter diagnosis and change the bottleneck model rather than retrying the same parameter.
  9. Run analyze only when the objective is met, round budget is exhausted, or the run intentionally stops.

Entry point

scripts/triton_optimization.py provides:

  • plan: validate correctness evidence and lock baseline/configuration;
  • record: enforce sequential rounds, parent hash, full-case validation, measurement coverage, and noise-aware decisions;
  • analyze: report best kernel, cumulative improvement, discarded hypotheses, and terminal objective status.

Read:

Read the full file on GitHub · 45 lines

Files

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.

Changes

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.

  1. 2mo ago First seen · 45 lines · 125 tokens per session scan A 4cabf6f4b4bd

Subscribe to this mod's changes

ascend-triton-kernel-optimization is a skill published in the GitHub repository maoxx241/vllm-ascend-workspace (36 stars, last pushed 2mo ago), licensed MIT. It adds 125 tokens to every session and 680 once invoked, about $0.0005 per session on Opus 5.5. 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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