BC huawei-cloud-modelarts-resource-pool-management
Manage Huawei Cloud ModelArts dedicated resource pools (专属资源池) and node pools through full lifecycle operations via hcloud CLI. Covers 53 operations across 10 functional domains: resource pool management, pool nodes, node pool management, network resources, tag management, plugin management, jobs/tasks, scheduled events, OS configuration, and resource flavor/event queries. Includes BSS on-demand pricing inquiry before chargeable operations (create/expand) to inform users of costs. All write operations require user confirmation. Triggers include: "资源池", "专属资源池", "resource pool", "创建资源池", "查询资源池", "删除资源池", "更新资源池", "资源池监控", "资源池节点", "pool node", "节点池", "node pool", "资源池网络", "pool network", "资源池标签", "pool tags", "插件", "plugin", "工作负载", "workload", "定时事件", "scheduled event", "OS配置", "规格列表", "ModelArts resource pool", "manage resource pool", "询价", "pricing", "按需价格", "价格查询".
Manage Huawei Cloud ModelArts dedicated resource pools (专属资源池) and node pools through full lifecycle operations via hcloud CLI.
As a process C 64/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 0
✓ No critical or high findings
Files scanned: 12. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 64/100
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 56 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 45 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3800 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 10 top-level sections: this looks like several domains in one skill
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
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 881: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -218 emoji in the instructions: noise for the model
- -31 of 1 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +5Description quotes 11 example trigger phrases
- +4Structure: 22 headings
- +3Step-by-step instructions: 45 items
- +4Has examples (7 code blocks)
- +4Reference files are cited in the instructions (9 of 9)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.