BA huawei-cloud-cce-metric-analyzer
Huawei Cloud CCE Metric analysis skill using the Python dispatcher with hcloud-backed cloud service queries. Use this skill when the user wants to: (1) query Pod/Node/CoreDNS/nginx-ingress/autoscaler/control-plane CPU, memory, disk, QPS, latency, request, connection, certificate, scaling, or error-rate metrics, (2) get resource usage TopN rankings, (3) query ECS/ELB/EIP/NAT cloud resource metrics, (4) aggregate cluster monitoring data with anomaly detection, (5) detect threshold-based resource anomalies. Trigger: user mentions "metric analysis", "指标分析", "CCE metrics", "CCE 指标", "AOM metrics", "AOM 指标", "CoreDNS metrics", "CoreDNS 指标", "nginx ingress metrics", "nginx-ingress 指标", "autoscaler metrics", "autoscaler 指标", "HPA metrics", "HPA 指标", "apiserver metrics", "etcd metrics", "controller manager metrics", "scheduler metrics", "control plane metrics", "控制面指标", "certificate expiration", "证书过期", "resource metrics", "资源指标", "CPU usage", "CPU 使用率", "memory usage", "内存使用率", "performance monitoring", "性能监控", "TopN", "resource ranking", "资源排名"
As a process A 83/100 · Runs to the end — weak spots: running it twice, progress reporting
How to improve
- Shorten the description to 1024 characters.
- 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 · 0
✓ No critical or high findings
Files scanned: 23. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1054 chars, limit 1024 - warning
body-longSKILL.md body ≈ 7350 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "id"
Process rating: all ten parameters 83/100
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 6 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 7350 tokens
- 100Steps. 66 steps
- 100Result and completion. Output format and completion criterion are stated
- 100When it triggers. States when to use and when not to
- 100Failures and branches. 3 branches, has a failure section
- 100Consistency. Name and required fields are in place
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 14 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (13 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 1053: 120–800 characters recommended
- +1No license
- +2Single-language instructions
- +5Description quotes 25 example trigger phrases
- +4Structure: 39 headings
- +3Step-by-step instructions: 66 items
- +3Output format is stated explicitly
- +4Has examples (10 code blocks)
- +4Reference files are cited in the instructions (8 of 8)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.