BC alibabacloud-rds-mysql-inspection
Batch health inspection for Alibaba Cloud RDS MySQL instances. Supports single, multiple, or all-instance global scan with configurable time window (default 7 days, max 30). Outputs one HTML report per instance plus a summary HTML. Dimensions: instance basics, resource utilization (CPU/memory/disk/IOPS/connections), CloudMonitor alert history, top 20 space-consuming tables (DAS), slow log statistics (DAS), kernel-version / expiration checks. Cluster instances auto-detected; per-node metrics and slow logs. Triggers: RDS inspection, RDS health check, RDS MySQL inspection, Alibaba Cloud RDS inspection, batch/global/multi-instance inspection, slow log analysis, space analysis, RDS 巡检, 阿里云 RDS 巡检, 数据库巡检, 批量巡检, 慢日志分析, 表空间分析.
Batch health inspection for Alibaba Cloud RDS MySQL instances.
As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches
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
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 Dangerous commands
cmd-pipe-to-shellSKILL.md:57Downloads 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 or upgrade,
quoted
Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 52/100
- 0Result and completion. Does not say what the result is
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 3 mutating operations with no state check
- 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
- 70Execution cost. Instruction body is 4062 tokens
- 85Steps. 57 steps, 2 vague phrases
- 100Inputs and preconditions. Inputs and preconditions are listed
- 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 (5 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
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- -31 of 2 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 728: enough signal without eating the budget
- +4Structure: 31 headings
- +3Step-by-step instructions: 57 items
- +4Has examples (14 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.