BB alibabacloud-apigw-inspection
Perform instance inspection on Alibaba Cloud Cloud-Native API Gateway, AI Gateway, and API Gateway, and query Cloud Monitor metric data. Use when the user needs to view monitoring metrics of gateway instances (such as CPU usage, memory usage, connections, network IO, bandwidth, rate limiting, etc.), obtain metric data for a specific point in time or time range, or evaluate resource utilization. Trigger scenarios include: "check if the Cloud-Native API Gateway has enough resources", "view monitoring data of the AI Gateway instance", "inspect this API Gateway instance", "how is the gateway performing", "check if the gateway has any anomalies", "check gateway health status", etc.
Perform instance inspection on Alibaba Cloud Cloud-Native API Gateway, AI Gateway, and API Gateway, and query Cloud Monitor metric data.
As a process B 68/100 · Nearly there — weak spots: inputs and preconditions, running it twice, progress reporting
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- 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 · 1
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high Dangerous commands
cmd-pipe-to-shellSKILL.md:42Downloads and executes remote code from an unrecognised host (pipe to shell)curl -fsSL https://aliyuncli.alicdn.com/setup.sh | bash
Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5509 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 68/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 4 mutating operations with no state check
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 70Execution cost. Instruction body is 5509 tokens
- 85Steps. 27 steps, 1 vague phrases
- 100Result and completion. Output format and completion criterion are stated
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- low 13 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
- +4Description does not say when NOT to use the skill (false activations)
- +1No license
- +2Single-language instructions
- +5Description quotes 6 example trigger phrases
- +3Description length 685: enough signal without eating the budget
- +4Structure: 24 headings
- +3Step-by-step instructions: 27 items
- +3Output format is stated explicitly
- +4Has examples (9 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.