BF huawei-cloud-ces-list
查询华为云 CES（云监控服务）监控指标列表与指标数据。支持指标列表查询（ListMetrics） 按命名空间/指标名/维度/排序过滤，以及指标数据查询（ShowMetricData）按时间范围、 聚合粒度、聚合方式获取监控数据点。只读操作，JSON 输出全部字段，AK/SK 环境变量认证。
查询华为云 CES（云监控服务）监控指标列表与指标数据。支持指标列表查询（ListMetrics） 按命名空间/指标名/维度/排序过滤，以及指标数据查询（ShowMetricData）按时间范围、 聚合粒度、聚合方式获取监控数据点。只读操作，JSON 输出全部字段，AK/SK 环境变量认证。
As a process F 49/100 · Will not run — References files that are not bundled: references/iam-policies.md, references/cli-installation-guide.md, references/verification-method.md
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 0
✓ No critical or high findings
Files scanned: 10. 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") - warning
missing-refreference to a missing file: references/iam-policies.md - warning
missing-refreference to a missing file: references/cli-installation-guide.md - warning
missing-refreference to a missing file: references/verification-method.md - warning
missing-refreference to a missing file: references/dataflow-diagram.md - warning
missing-refreference to a missing file: references/acceptance-criteria.md - note
frontmatter-keyunknown frontmatter key "triggers" - note
frontmatter-keyunknown frontmatter key "tools"
Process rating: all ten parameters 49/100
- 0Tools and files. 5 referenced file(s) missing: references/iam-policies.md, references/cli-installation-guide.md, references/verification-method.md
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 40Result and completion. Does not say what the result is
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Steps. 34 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1525 tokens
- 100Running it twice. No mutating operations
- 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
- +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
- +1No license
- +2Single-language instructions
- +3Description length 146: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 34 items
- +4Has examples (5 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 58.