AD db-scheduler
数据库调度工具，支持备份、定时任务管理、任务执行日志。 使用场景： - 用户说"备份数据库" → backup - 用户说"定时任务" → task - 用户说"查看任务日志" → logs 用法： - dbskiter --output-mode=ai --database=<name> scheduler backup --type=full - dbskiter --output-mode=ai --database=<name> scheduler task list - dbskiter --output-mode=ai --database=<name> scheduler task add daily_backup "0 2 * * *" - dbskiter --output-mode=ai --database=<name> scheduler task enable daily_backup - dbskiter --output-mode=ai --database=<name> scheduler task disable daily_backup - dbskiter --output-mode=ai --database=<name> scheduler task run daily_backup - dbskiter --output-mode=ai --database=<name> scheduler logs
As a process D 46/100 · 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 46/100
- 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
- 30Running it twice. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (db-scheduler) differs from the folder (dbskiter-db-scheduler)
- 100Tools and files. No external tools needed
- 100Steps. 11 steps
- 100Execution cost. Instruction body is 1128 tokens
- low The response is described with custom markup (11 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 2 example trigger phrases
- +3Description length 634: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 11 items
- +4Has examples (10 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.