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 huaweicloud/huaweicloud-skills --skill huawei-cloud-ascendc-operator-performance-optimgit clone --depth 1 https://github.com/huaweicloud/huaweicloud-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/huaweicloud/huaweicloud-skills/huawei-cloud-ascendc-operator-performance-optim)<a href="https://agentmods.dev/skills/huaweicloud/huaweicloud-skills/huawei-cloud-ascendc-operator-performance-optim"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-ascendc-operator-performance-optim/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/huaweicloud/huaweicloud-skills/huawei-cloud-ascendc-operator-performance-optim"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-ascendc-operator-performance-optim.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 | $0.00146 | $0.01869 |
| Opus 5 | $0.00073 | $0.00934 |
| Sonnet 5 | $0.00029 | $0.00374 |
| Haiku 4.5 | $0.00015 | $0.00187 |
Grade A, and why
huawei-cloud-ascendc-operator-performance-optim 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 — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Huawei Cloud AscendC Operator Performance Optimization
Overview
This skill provides guidance for developing and optimizing custom operators using AscendC programming language.
Architecture: Performance Analysis → Bottleneck Identification → Operator Development → Optimization → Validation
Related Skills:
huawei-cloud-ascend-profiler-db-explorer- Performance data analysis and bottleneck identificationhuawei-cloud-ascend-small-model-migrate- Migration workflow that may require operator optimization
Architecture Components
This skill involves the following cloud services and components:
- AscendC: Programming language for custom operator development
- CANN: Huawei Cloud AI Computing Platform for NPU
- Ascend 910B: Target NPU hardware for operator deployment
- Ascend Profiler: Performance analysis tool for validation
Architecture Diagram:
┌─────────────────────────────────────────────────────────────┐
│ AscendC Operator Optimization Skill │
├─────────────────────────────────────────────────────────────┤
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Performance │───▶│ Bottleneck │───▶│ Operator │ │
│ │ Analysis │ │ Identification│ │ Development │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Profiling │ │ Optimization│ │ Validation │ │
│ │ Data │ │ Techniques │ │ & Testing │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
└─────────────────────────────────────────────────────────────┘
Use Cases
Typical Problem Scenarios:
- Optimizing performance-critical operators on Ascend NPU
- Developing custom operators for specific workloads
- Improving model inference performance through operator optimization
- Fixing operator bottlenecks identified during profiling
- Implementing missing operators for NPU deployment
What ships with it
20 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.
- references/acceptance-criteria.md 3.4 KB
- references/api-usage-prof.md 6.9 KB
- references/ascendc-api/basic-data-structures-api.md 7.6 KB
- references/ascendc-api/data-copy-api.md 10 KB
- references/ascendc-api/GUIDE.md 5.8 KB
- references/ascendc-api/kernel-constraints.md 4.3 KB
- references/ascendc-api/resource-management-api.md 9.8 KB
- references/ascendc-api/sync-control-api.md 7.1 KB
- references/ascendc-api/vector-compute-api.md 13 KB
- references/data-copy-prof.md 2.5 KB
- references/memory-prof.md 7.2 KB
- references/pipeline-prof.md 4.8 KB
- references/scalar-prof.md 3.3 KB
- references/tiling-prof.md 4.1 KB
- references/troubleshooting.md 3.7 KB
- references/verification-method.md 3.2 KB
- scripts/e2e_compare.sh 4.9 KB runs code
- scripts/e2e_compile_run.sh 568 B runs code
- scripts/e2e_profile_onboard.sh 1.1 KB runs code
- scripts/e2e_profile_simulator.sh 1.3 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 · 236 lines · 146 tokens per session scan A 594720913520
huawei-cloud-ascendc-operator-performance-optim is a skill published in the GitHub repository huaweicloud/huaweicloud-skills (49 stars, last pushed yesterday), licensed MIT. It adds 146 tokens to every session and 1,869 once invoked, about $0.0007 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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