Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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-ascend-small-model-migrate)<a href="https://agentmods.dev/skills/huaweicloud/huaweicloud-skills/huawei-cloud-ascend-small-model-migrate"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-ascend-small-model-migrate/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-ascend-small-model-migrate"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-ascend-small-model-migrate.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 | $0.00214 | $0.03775 |
| Opus 5 | $0.00107 | $0.01887 |
| Sonnet 5 | $0.00043 | $0.00755 |
| Haiku 4.5 | $0.00021 | $0.00378 |
Grade A, and why
huawei-cloud-ascend-small-model-migrate 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 — 506 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Huawei Cloud Ascend Small Model Migration
Overview
This skill guides the migration workflow for small vision models to Ascend NPU, covering structure analysis → migration verification → performance optimization.
Architecture: Model Analysis → Environment Setup → NPU Inference → Performance Profiling → Bottleneck Analysis → Optimization Recommendations
Related Skills:
huawei-cloud-msmodelslim-model-analysis- Model structure analysis for migration path determinationhuawei-cloud-msot-msopprof-operator-profiler- Operator performance data collectionhuawei-cloud-ascend-profiler-db-explorer- Profiling database analysis for bottleneck identificationhuawei-cloud-ascendc-operator-performance-optim- Optional: AscendC operator optimization for bottleneck operators
Architecture Components
This skill involves the following cloud services and components:
- Ascend NPU: Target hardware for model deployment (Ascend 910B series)
- torch_npu: PyTorch adapter for Ascend NPU
- MSProf: Ascend profiling tool for performance analysis
- Ultralytics: YOLO model framework support
- Docker: Container environment for consistent deployment
Use Cases
Typical Problem Scenarios:
- Migrating vision models from GPU to Ascend NPU
- Deploying YOLO/ResNet/UNet models on Ascend hardware
- Optimizing small model performance on NPU
- Verifying model accuracy after migration
- Identifying performance bottlenecks in computer vision models
Typical User Phrases:
- "Migrate YOLOv8 to Ascend NPU
- "How to run ResNet on Ascend?
- "Optimize UNet inference on NPU
- "Verify model accuracy after migration
- "Analyze performance bottlenecks in my vision model
- "YOLOModelMigrationAscendNPU
- "AscendModel？
- "ModelMigrationNPU？
Scope
Supported:
- Encoder-only architectures (ResNet, VGG, EfficientNet)
- Detection models (YOLO, Faster-RCNN, SSD)
- Segmentation models (UNet, DeepLab)
- Other non-Decoder-only LLM models
Not supported:
- Decoder-only LLM (Qwen, LLaMA, DeepSeek) - requires adapter-based quantization approach
- Understanding VLM text backbone - requires adapter-based quantization approach
What ships with it
7 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 · 506 lines · 214 tokens per session scan A 2392e5c3838a
huawei-cloud-ascend-small-model-migrate is a skill published in the GitHub repository huaweicloud/huaweicloud-skills (49 stars, last pushed yesterday), licensed MIT. It adds 214 tokens to every session and 3,775 once invoked, about $0.0011 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
ascend-migration-analysis
A feasibility analysis for moving a PyTorch project from CUDA/GPU hardware to Ascend NPU hardware. It scans the code for hardware-specific dependencies and reviews device code, attention mechanisms, custom operations, distributed communication, precision, third-party packages, and compilation.
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-ascend-transformer-boost
An index of skills for developing and deploying Huawei's Ascend Transformer Boost library, a set of tools for accelerating transformer-based AI models. It covers the main stages of working with Ascend NPUs and related operator libraries.
diffusers-ascend-pipeline
A guide for running image and video generation pipelines on Huawei Ascend NPUs with the Diffusers library. Diffusers is a software library for using generative models, and the guide covers model pipelines, memory settings, LoRA adapters, and multi-card inference.
ai-for-science-ascend-tf-community
A migration guide for running TensorFlow 2.x models natively on Huawei Ascend NPU hardware without converting them to PyTorch. It covers source building, plugin installation, automated and manual changes, and accuracy checks.
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.