BC huawei-cloud-ascend-models-deploy
Huawei Cloud Ascend model deployment and testing skill for large language models on Ascend DevServer (910B series). Supports single-machine and dual-machine deployment for LLM, VL (vision-language), Embedding, and Rerank models. Provides model inference testing, deployment log viewing, and status monitoring with automated model matching and deployment script generation. Use this skill when the user wants to: (1) deploy a model on Ascend DevServer, (2) test model inference, (3) view deployment logs or status, (4) list supported models, (5) check deployment prerequisites. Trigger: deploy, test, model list, deployment log, Ascend, DevServer, 910B, ModelArts, LLM, VL, Embedding, Rerank, multimodal, inference, model catalog, 昇腾, 部署模型, 测试模型, 模型列表, 部署日志, 模型部署, 推理测试
As a process C 62/100 · Has gaps — weak spots: result and completion, running it twice
The same skill appears in 1 more place: ClawHub
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 13
✓ No critical or high findings
Medium and low: 13
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low Secrets in code
secret-high-entropy-tokenreferences/model-catalog.md:12High-entropy token-like string (may be an id, hash or a credential)| Qwen…507 | 16 | /v1/chat/completions |
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low Secrets in code
secret-high-entropy-tokenreferences/model-catalog.md:13High-entropy token-like string (may be an id, hash or a credential)| Qwen…507 | 16 | /v1/chat/completions |
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low Secrets in code
secret-high-entropy-tokenreferences/model-catalog.md:14High-entropy token-like string (may be an id, hash or a credential)| Deep…70B | 4 | /v1/chat/completions |
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low Secrets in code
secret-high-entropy-tokenreferences/model-catalog.md:23High-entropy token-like string (may be an id, hash or a credential)| Qwen…8A8 | 8 | Yes |
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low Secrets in code
secret-high-entropy-tokenscripts/deploy_helper.py:40High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"Qwen…507": {quoted -
low Secrets in code
secret-high-entropy-tokenscripts/deploy_helper.py:46High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"Qwen…507": {quoted -
low Secrets in code
secret-high-entropy-tokenscripts/deploy_helper.py:52High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"Deep…70B": {quoted -
low Secrets in code
secret-high-entropy-tokenscripts/deploy_helper.py:84High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)"Qwen…8A8": {quoted -
low Secrets in code
secret-high-entropy-tokenSKILL.md:166High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)- "qwen3-235b" → Multiple matches, prefer Instruct version (Qwen…507), or ask user
detector -
low Secrets in code
secret-high-entropy-tokenSKILL.md:248High-entropy token-like string (may be an id, hash or a credential)| Qwen…507 | 16 |
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low Secrets in code
secret-high-entropy-tokenSKILL.md:249High-entropy token-like string (may be an id, hash or a credential)| Qwen…507 | 16 |
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low Secrets in code
secret-high-entropy-tokenSKILL.md:250High-entropy token-like string (may be an id, hash or a credential)| Deep…70B | 4 |
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low Secrets in code
secret-high-entropy-tokenSKILL.md:258High-entropy token-like string (may be an id, hash or a credential)| Qwen…8A8 | 8 |
Files scanned: 11. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 7021 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 62/100
- 0Result and completion. Does not say what the result is
- 30Running it twice. 89 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 7021 tokens
- 100Steps. 86 steps
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 13 top-level sections: this looks like several domains in one skill
- low The skill ranks results itself: that belongs to the system behind the tool, not the model
- low The response is described with custom markup (5 tags): a typed call is more reliable
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -4Absolute local paths (C:\Users, /home/…): not portable
- +1No license
- +2Single-language instructions
- +3Description length 768: enough signal without eating the budget
- +4Structure: 46 headings
- +3Step-by-step instructions: 86 items
- +4Has examples (22 code blocks)
- +4Reference files are cited in the instructions (7 of 8)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.