BF huawei-cloud-monitoring-query
Queries Huawei Cloud monitoring and enterprise project resources (CES/EPS). Covers alarm rules, alarm histories, alarm templates, dashboards, notification masks, resource groups, one-click alarms, and enterprise projects (list/detail/quotas/bound resources/migration records). No write operations. Use this skill when the user needs to check alarm status, view monitoring dashboards, query alarm rules, or manage enterprise project info. Triggers include: 云监控, 告警, 告警规则, 告警历史, 仪表盘, 企业项目, CES, EPS, 告警模板, 资源分组, alarm, monitoring, alert.
Queries Huawei Cloud monitoring and enterprise project resources (CES/EPS).
As a process F 63/100 · Will not run — References files that are not bundled: scripts/<service_category>/, scripts/as/list_scaling_groups.py, references/<service>/guide.md
How to improve
- The text references files that are not there: add them or drop the references.
- 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 · 3
✓ No critical or high findings
Medium and low: 3
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low Dangerous commands
cmd-execpolicy-bypassSKILL.md:95Runs PowerShell with execution policy bypassed (quoted — discussed, not commanded)- Windows: `skill action=exec: powershell -ExecutionPolicy Bypass -File skill://scripts/chec…ps1`
quoted -
low Dangerous commands
cmd-execpolicy-bypassSKILL.md:125Runs PowerShell with execution policy bypassed (quoted — discussed, not commanded)- Windows: `skill action=exec: powershell -ExecutionPolicy Bypass -File skill://scripts/chec…ps1`
quoted -
low Dangerous commands
cmd-execpolicy-bypassSKILL.md:160Runs PowerShell with execution policy bypassed (quoted — discussed, not commanded)- Windows: `skill action=exec: powershell -ExecutionPolicy Bypass -File skill://scripts/chec…ps1`
quoted
Files scanned: 41. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: scripts/<service_category>/ - warning
missing-refreference to a missing file: scripts/as/list_scaling_groups.py - warning
missing-refreference to a missing file: references/<service>/guide.md - warning
missing-refreference to a missing file: scripts/<resource_category>/ - warning
missing-refreference to a missing file: references/<resource_category>/guide.md
Process rating: all ten parameters 63/100
- 0Tools and files. 5 referenced file(s) missing: scripts/<service_category>/, scripts/as/list_scaling_groups.py, references/<service>/guide.md
- 55Failures and branches. 1 branches
- 60Steps. 77 steps, 7 vague phrases
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2983 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- low 14 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (17 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)
- +4No input/output examples
- -41 reference files, but SKILL.md never points to them: the model will not open them
- -32 of 4 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 535: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 77 items
- +3Output format is stated explicitly
Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.