AC alibabacloud-polardb-mysql-inspection
Health inspection for Alibaba Cloud PolarDB MySQL instances, generating visual HTML reports. Supports five inspection dimensions: 1. Resource Monitoring — CPU, memory, IOPS, connections usage trends (including Proxy nodes) 2. Space Analysis — Top 20 table space usage, auto-increment primary key usage 3. Slow Query Log — Slow SQL statistics and analysis 4. Session Diagnostics — Current active connections and session status 5. Alert History — CloudMonitor alert records Supports single-instance, multi-instance, and full-account inspection modes. Trigger keywords: PolarDB inspection, instance health check, database inspection report, CPU/memory/IOPS usage, disk space analysis, table space usage, largest tables, top table space, slow query statistics, slow SQL analysis, alert records, alert history, connection monitoring, active sessions, database performance check.
Health inspection for Alibaba Cloud PolarDB MySQL instances, generating visual HTML reports.
As a process C 63/100 · Has gaps — weak spots: failures and branches, running it twice
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 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
- 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
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medium Dangerous commands
cmd-pipe-to-shellSKILL.md:147Downloads and executes remote code from an unrecognised host (pipe to shell) (quoted — discussed, not commanded)> run `curl -fsSL https://aliyuncli.alicdn.com/setup.sh | bash` to install/update,
quoted
Files scanned: 9. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 63/100
- 0Failures and branches. Linear process with no failure handling
- 30Running it twice. 2 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 44 steps, 3 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3666 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- 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 873: 120–800 characters recommended
- +1No license
- +2Single-language instructions
- +4Structure: 16 headings
- +3Step-by-step instructions: 44 items
- +3Output format is stated explicitly
- +4Has examples (15 code blocks)
- +4Reference files are cited in the instructions (4 of 4)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.