BD db-monitor
数据库健康监控，支持健康检查、异常检测、容量预测、高级容量预测、趋势分析、基线对比。 智能数据源选择： - Oracle 数据库自动使用 Zabbix 监控 - MySQL 数据库优先使用直连，其次使用 Prometheus - 支持 Z 系列资产组（如 Z18, Z5）自动识别 使用场景： - 用户说"检查健康" -> 执行 health - 用户说"有异常吗" -> 执行 anomalies - 用户说"容量够吗" -> 执行 capacity - 用户说"采集指标" -> 执行 collect - 用户说"看历史" -> 执行 history - 用户说"高级容量预测" -> 执行 capacity-advanced - 用户说"趋势分析" -> 执行 trend - 用户说"基线对比" -> 执行 compare 用法： - dbskiter --output-mode=ai --database=<name> monitor health - dbskiter --output-mode=ai --database=<name> monitor anomalies - dbskiter --output-mode=ai --database=<name> monitor capacity --resource=disk - dbskiter --output-mode=ai --database=<name> monitor collect - dbskiter --output-mode=ai --database=<name> monitor history <metric> - dbskiter --output-mode=ai --database=<name> monitor capacity-advanced --resource=disk - dbskiter --output-mode=ai --database=<name> monitor trend --metric=cpu_usage - dbskiter --output-mode=ai --database=<name> monitor compare --metric=qps --value=1250 --baseline=2026-04-01
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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 · 0
✓ No critical or high findings
Files scanned: 2. 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 49/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (db-monitor) differs from the folder (dbskiter-db-monitor)
- 100Tools and files. No external tools needed
- 100Steps. 18 steps
- 100Execution cost. Instruction body is 959 tokens
- 100Running it twice. No mutating operations
- low The response is described with custom markup (8 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)
- +3Description length 986: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 18 headings
- +3Step-by-step instructions: 18 items
- +4Has examples (17 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.