AC alibabacloud-csas-device-health-monitor
Read-only health and risk monitoring for endpoint devices enrolled in Alibaba Cloud SASE (CSAS). Diagnose a device or scan a fleet for CPU or memory pressure, battery health, disk saturation, and online status. Use when users report a slow, hot, laggy, or draining endpoint, or ask about sustained CPU or memory use, battery health, disk capacity, online status, or devices with recent performance risks. It does not count inventory, assess idle devices, check client versions, or lock, recall, delete, or otherwise modify devices. Triggers: "CSAS device health", "slow endpoint", "CPU or memory pressure", "battery health", "disk saturation", "endpoint online status".
Read-only health and risk monitoring for endpoint devices enrolled in Alibaba Cloud SASE (CSAS).
As a process C 58/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice
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: 11. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
edit-residuethe text marks something as outdated (lines 29): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 58/100
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 2 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 14 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1282 tokens
- low The response is described with custom markup (6 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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 6 example trigger phrases
- +3Description length 669: enough signal without eating the budget
- +4Structure: 6 headings
- +3Step-by-step instructions: 14 items
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (7 of 7)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 96.