AC alibabacloud-mongodb-health-inspect
Run a read-only health inspection on Alibaba Cloud MongoDB (DDS) instances and produce a standardized report. Supports Sharding and ReplicaSet architectures across single-instance, multi-instance, and whole-account scopes. Use this skill when the user asks for a MongoDB health check, DDS inspection, resource usage review, slow query analysis, current session snapshot, alert review, or configuration risk assessment. Triggers: MongoDB 巡检, MongoDB 健康检查, DDS 巡检, 实例巡检, MongoDB health check, MongoDB inspection, 资源使用率, 慢日志, 慢查询, 当前会话, 报警历史, 配置检查, 风险评估, 数据库健康报告, 性能巡检, 巡检报告.
Run a read-only health inspection on Alibaba Cloud MongoDB (DDS) instances and produce a standardized report.
As a process C 62/100 · Has gaps — weak spots: result and completion, 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
-
medium Dangerous commands
cmd-pipe-to-shellSKILL.md:116Downloads 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: 1. 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 62/100
- 0Result and completion. Does not say what the result is
- 30Running it twice. 3 mutating operations with no state check
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 20 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1658 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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
- -33 of 4 scripts are never mentioned in SKILL.md
- +1No license
- +2Single-language instructions
- +3Description length 572: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 20 items
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (2 of 4)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.