BF alibabacloud-network-health-inspection
Comprehensive health inspection tool for Alibaba Cloud network products (EIP, CBWP, NAT Gateway, CEN, Transit Router, Physical Connection, VBR, Global Accelerator, CLB, ALB, NLB). Analyzes bandwidth utilization, connection count, QPS, and packet loss using Cloud Monitor data; generates a Markdown inspection report with monitoring charts, risk assessment, and scaling recommendations. All API calls are read-only. Use when users want to inspect network product usage, check bandwidth headroom, assess capacity before a business launch, or identify over-limit risk. Triggered by: inspect network products, network health check, bandwidth inspection, EIP/NAT/CLB/ALB/NLB/CEN/VBR/GA inspection, network utilization analysis, pre-launch network inspection, capacity assessment, bandwidth over-limit risk.
Comprehensive health inspection tool for Alibaba Cloud network products (EIP, CBWP, NAT Gateway, CEN, Transit Router, Physical Connection, VBR, Global…
As a process F 49/100 · Will not run — References files that are not bundled: ..., url
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Bashallowed-tools: Bash Read
Files scanned: 17. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 7318 tokens (recommended < 5000); move details to references/ - warning
missing-refreference to a missing file: ... - warning
missing-refreference to a missing file: url
Process rating: all ten parameters 49/100
- 0Tools and files. 2 referenced file(s) missing: ..., url
- 0Result and completion. Does not say what the result is
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 7318 tokens
- 85Steps. 91 steps, 3 vague phrases
- 100Failures and branches. 4 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 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)
- +3Description length 801: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -31 of 14 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +4Structure: 22 headings
- +3Step-by-step instructions: 91 items
- +4Has examples (11 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.