AA alibabacloud-ebs-usage-summary
Analyze Alibaba Cloud block storage (EBS) disk performance and fleet composition. Use it to locate performance bottlenecks (saturated IOPS or bandwidth), compare disks, instances, or availability zones to decide where to tune or resize, review disk count and capacity by category / region / billing type to inform capacity and cost decisions, check disk event history, and reach the right console dashboard for deeper drill-down. Triggers: "EBS monitoring", "disk metrics", "cloud disk performance", "IOPS analysis", "BPS analysis", "disk monitoring data", "export disk monitoring data", "export monitoring data", "metric aggregation", "resource overview", "EBS Lens", "CloudLens for EBS", "disk usage report", "capacity distribution", "event overview", "monitoring dashboard", "EBS dashboard", "storage dashboard", "disk monitoring dashboard", "storage health", "block storage insights", "disk observability", "disk inventory summary", "telemetry console", "block storage analytics".
Analyze Alibaba Cloud block storage (EBS) disk performance and fleet composition.
As a process A 80/100 · Runs to the end — weak spots: running it twice
How to improve
- 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: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 7156 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 80/100
- 30Running it twice. 1 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 70Execution cost. Instruction body is 7156 tokens
- 85Steps. 24 steps, 1 vague phrases
- 100Result and completion. Output format and completion criterion are stated
- 100Inputs and preconditions. Inputs and preconditions are listed
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 13 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (9 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 984: 120–800 characters recommended
- +1No license
- +2Single-language instructions
- +5Description quotes 25 example trigger phrases
- +4Structure: 30 headings
- +3Step-by-step instructions: 24 items
- +3Output format is stated explicitly
- +4Has examples (11 code blocks)
- +4Reference files are cited in the instructions (7 of 7)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.