Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add wenyi-li/awesome-agent-kernel-skills --skill triton-ascend-case-elemwise-zerosgit clone --depth 1 https://github.com/wenyi-li/awesome-agent-kernel-skillsWrote 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/wenyi-li/awesome-agent-kernel-skills/triton-ascend-case-elemwise-zeros)<a href="https://agentmods.dev/skills/wenyi-li/awesome-agent-kernel-skills/triton-ascend-case-elemwise-zeros"><img src="https://agentmods.dev/badge/skills/wenyi-li/awesome-agent-kernel-skills/triton-ascend-case-elemwise-zeros/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/wenyi-li/awesome-agent-kernel-skills/triton-ascend-case-elemwise-zeros"><img src="https://agentmods.dev/badge/skills/wenyi-li/awesome-agent-kernel-skills/triton-ascend-case-elemwise-zeros.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00067 | $0.00431 |
| Opus 5 | $0.00034 | $0.00216 |
| Sonnet 5 | $0.00013 | $0.00086 |
| Haiku 4.5 | $0.00007 | $0.00043 |
Grade A, and why
triton-ascend-case-elemwise-zeros 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 11d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 11d ago First seen · 39 lines · 67 tokens per session scan A fddc8cc71cda
triton-ascend-case-elemwise-zeros is a skill published in the GitHub repository wenyi-li/awesome-agent-kernel-skills (9 stars, last pushed 3mo ago), with no licence file. It adds 67 tokens to every session and 431 once invoked, about $0.0003 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 2026-08-31.
Other skills, from other repositories
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A Triton optimization guide for minimum-value reduction when the reduced dimension is much larger than the other dimension. It uses multiple cores and atomic operations, which combine partial results safely, for extreme shapes such as 16 by 262,144.
triton-ascend-case-reduction-amin-medium
A Triton optimization guide for finding row minimums in a large two-dimensional array on Ascend hardware. It handles cases where the dimension being reduced contains hundreds of thousands of elements.
triton-ascend-case-reduction-amin-small
A Triton optimization guide for finding the minimum value in a medium-sized one-dimensional array on Ascend hardware. It focuses on choosing a suitable amount of parallel work for inputs around 65,536 elements.
triton-ascend-case-reduction-amax-small
A Triton optimization guide for finding the maximum value in a very small array on Ascend hardware. It compares processing the whole input with one core against using several cores.
spark-environment-setup
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spark-memory-thermal-ops
Manage unified memory and thermals during long-running ML jobs on NVIDIA DGX Spark. Use when planning memory headroom for a training run on GB10, when a job OOMs on unified memory, or when monitoring temperature and power during multi-hour training.