AB huawei-cloud-cc-central-network-query
Queries Huawei Cloud Cloud Connect (CC) Central Network resources via hcloud CLI. Covers central network instances (single + list), central network attachments (single + list, including ER route table and GDGW attachment types), and central network connections (single + list). No write operations. Use this skill when the user needs to inspect central network topology, check central network connection status, review attachment configurations, or audit central network deployment. Triggers: 中心网络, Central Network, CC Central Network, 中心网络实例, 中心网络附件, 中心网络连接, central network instance, central network attachment, central network connection, 查询中心网络, central network query.
Queries Huawei Cloud Cloud Connect (CC) Central Network resources via hcloud CLI.
As a process B 70/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting
How to improve
- 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: 10. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "triggers"
Process rating: all ten parameters 70/100
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 6 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 60Tools and files. Uses tools (bash) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 10 steps, 1 vague phrases
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2490 tokens
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
- -31 of 1 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 672: enough signal without eating the budget
- +4Structure: 16 headings
- +3Step-by-step instructions: 10 items
- +4Has examples (13 code blocks)
- +4Reference files are cited in the instructions (6 of 6)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.