Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/huaweicloud/huaweicloud-skillsnpx agentmods add skills/huaweicloud/huaweicloud-skills/huawei-cloud-msot-msopprof-operator-profilerWrote 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-msot-msopprof-operator-profiler)<a href="https://agentmods.dev/skills/huaweicloud/huaweicloud-skills/huawei-cloud-msot-msopprof-operator-profiler"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-msot-msopprof-operator-profiler/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-msot-msopprof-operator-profiler"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-msot-msopprof-operator-profiler.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 | $0.00144 | $0.01670 |
| Opus 5 | $0.00072 | $0.00835 |
| Sonnet 5 | $0.00029 | $0.00334 |
| Haiku 4.5 | $0.00014 | $0.00167 |
Grade A, and why
huawei-cloud-msot-msopprof-operator-profiler 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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Huawei Cloud msOT msopprof Operator Profiler
Overview
This skill provides operator-level performance profiling capabilities for Ascend NPU.
Architecture: Profiling Configuration → Data Collection → Report Generation → Analysis
Related Skills:
huawei-cloud-ascend-profiler-db-explorer- Profiling database analysis and queryhuawei-cloud-ascend-small-model-migrate- Migration workflow that uses performance data
Architecture Components
This skill involves the following cloud services and components:
- msopprof: Huawei Cloud operator profiling tool for Ascend NPU
- CANN: AI Computing Platform for NPU runtime support
- Ascend NPU: Target hardware for performance profiling
- Profiling Database: Storage for collected performance data
Architecture Diagram:
┌─────────────────────────────────────────────────────────────┐
│ msOT msopprof Operator Profiler Skill │
├─────────────────────────────────────────────────────────────┤
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Profiling │───▶│ Data │───▶│ Report │ │
│ │ Config │ │ Collection │ │ Generation │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Mode │ │ Operator │ │ Data │ │
│ │ Selection │ │ Execution │ │ Export │ │
│ │ (Device/Sim)│ │ Monitoring │ │ │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
└─────────────────────────────────────────────────────────────┘
Use Cases
Typical Problem Scenarios:
- Collecting operator-level performance data on Ascend NPU
- Profiling model inference performance
- Identifying performance bottlenecks through operator execution
- Comparing operator performance between device and simulator modes
- Generating performance analysis reports for model optimization
What ships with it
6 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.
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 · 226 lines · 144 tokens per session scan A 017a6177c5ac
huawei-cloud-msot-msopprof-operator-profiler is a skill published in the GitHub repository huaweicloud/huaweicloud-skills (49 stars, last pushed yesterday), licensed MIT. It adds 144 tokens to every session and 1,670 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.
Other skills, from other repositories
external-gitcode-ascend-atb-aclnn-operator-migration
A migration workflow for moving ATB operators to ACLNN, Ascend's operator library interface. It handles parameter mapping, runner implementation, device-based switching, and functional and performance checks.
atb-aclnn-operator-migration
A workflow for migrating ATB operators to ACLNN on Huawei Ascend 910B and 950 devices. ATB and ACLNN are libraries for implementing computations on Ascend hardware.
ai-for-science-ai4s-basic
An AI-for-science migration guide for moving CUDA-based projects from NVIDIA hardware to Huawei Ascend NPUs. It covers projects using PyTorch, TensorFlow, or vLLM, including code changes and distributed setups.
external-gitcode-ascend-atb-aclnn-operator-replacement-designer
A design-document generator for replacing ATB operators with ACLNN operators, which are Ascend library interfaces. It turns interface documentation into a structured migration plan with parameter mappings, self-tests, and risks.
ai-for-science-ai4s-profiling
A performance-profiling guide for Huawei Ascend NPUs using `torchnpu.profiler`. Profiling records timing, call stacks, memory use, and other measurements so developers can see where training or inference spends time.
ai-for-science-boltzgen
A deployment and reproduction guide for BoltzGen, a protein-design and inverse-folding system, on Huawei Ascend AI chips.