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-msmodelslim-model-analysisgit 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-msmodelslim-model-analysis)<a href="https://agentmods.dev/skills/huaweicloud/huaweicloud-skills/huawei-cloud-msmodelslim-model-analysis"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-msmodelslim-model-analysis/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-msmodelslim-model-analysis"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-msmodelslim-model-analysis.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 | $0.00151 | $0.02959 |
| Opus 5.5 | $0.00060 | $0.01184 |
| Sonnet 5.5 | $0.00030 | $0.00592 |
| Haiku 4.5 | $0.00015 | $0.00296 |
Grade A, and why
huawei-cloud-msmodelslim-model-analysis 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 1mo 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 — 363 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Huawei Cloud msModelSlim Model Analysis
Overview
This skill analyzes candidate models before adapter implementation for msModelSlim.
Architecture: Implementation Source Detection → Model Type Classification → Structural Feature Analysis → Risk Assessment
Related Skills:
huawei-cloud-msmodelslim-model-adapt- Adapter creation based on analysis results
Architecture Components
This skill involves the following cloud services and components:
- msModelSlim: Huawei Cloud's model quantization framework
- Transformers Library: Hugging Face Transformers for model loading
- ModelScope: Model download and management platform
- config.json: Model configuration file for analysis
Architecture Diagram:
┌─────────────────────────────────────────────────────────────┐
│ msModelSlim Model Analysis Skill │
├─────────────────────────────────────────────────────────────┤
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Model │───▶│ Source │───▶│ Structure │ │
│ │ Input │ │ Detection │ │ Analysis │ │
│ │ (config) │ │ │ │ │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Type │ │ MoE │ │ Risk │ │
│ │ Classification│ │ Assessment │ │ Assessment │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
└─────────────────────────────────────────────────────────────┘
Use Cases
Typical Problem Scenarios:
- Assessing model adaptation feasibility before creating msModelSlim adapters
- Analyzing model structure and type classification
- Evaluating MoE compatibility for quantization
- Determining if a model can be quantized with msModelSlim
- Identifying potential risks before adapter development
What ships with it
4 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.
- 1mo ago First seen · 363 lines · 151 tokens per session scan A 64e6ed64df44
huawei-cloud-msmodelslim-model-analysis is a skill published in the GitHub repository huaweicloud/huaweicloud-skills (59 stars, last pushed yesterday), licensed MIT. It adds 151 tokens to every session and 2,959 once invoked, about $0.0006 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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