AD scheduled-report
将当前对话中已完成的分析流程，提取并固化为定时执行的任务（周报/月报/日巡检等）。本 Skill 是一个纯粹的**编排层**——它不负责定义"怎么分析"，只负责"把你刚才做的分析录下来，变成能定时重放的任务"。 触发场景包括但不限于：用户提到"定时报告""定时分析""定期执行""自动报告""把这个分析变成周报""每周跑一次""定期监控""scheduled report""自动化分析""固化为报告""保存为定时任务""每月生成报告""定期推送""自动跑这个分析""帮我做成周报""以后每周都这么分析""定时汇报""创建定时分析""定期巡检""以后每天都帮我检查一下"，或用户在对话中完成分析后表达希望定期重复执行的意图时，都应使用此 Skill。 **触发判定关键**：用户表达中必须包含**"定时/定期/每X/以后都"等表示重复执行的时间意图**。仅说"月报""周报"不触发本 Skill（→ 触发 analysis-report）；说"每月出一份月报""以后每周都跑"才触发本 Skill。区分方式： - "出一份月报" → analysis-report（一次性，现在就要） - "以后每月都出月报" → scheduled-report（定时重放） - "帮我监控一下销售额" → anomaly-detection（先做一次分析） - "帮我每天监控销售额" → scheduled-report（有"每天"=定时意图，但需先引导完成一轮分析） **前提条件：用户需要先在对话中完成一轮分析（通过 metric-query、metric-attribution、dataset-detail-query 等 Skill），本 Skill 才能提取分析逻辑并固化为定时任务。如果对话中没有分析历史，应先引导用户完成分析，再触发本 Skill。**
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
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 100Steps. 41 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1628 tokens
- 100Running it twice. No mutating operations
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
- -215 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 16 example trigger phrases
- +3Description length 779: enough signal without eating the budget
- +4Structure: 27 headings
- +3Step-by-step instructions: 41 items
- +4Has examples (8 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.