AD anomaly-detection
对指标进行异常检测，判断当前数据是否偏离正常范围，输出结构化的异常检测报告。当用户希望检查指标是否异常、做健康巡检、或对一组指标做批量异常扫描时，必须使用此 Skill。 触发场景包括但不限于：用户提到"异常检测""有没有异常""是否正常""健康检查""巡检""波动是否正常""数据是不是有问题""帮我看看有没有问题""检查一下XX""哪些指标异常""异常扫描""偏离""超出阈值""涨得不正常""库存健康""销售异常""是不是出问题了""有没有风险""这个数据正常吗"，或用户对某些数据是否处于正常状态表达了疑问时，都应使用此 Skill。 **触发判定关键**：用户的意图是**判断好坏/正常与否**，而非单纯查数值。区分方式： - "上月销售额多少" → metric-query（查数值，不涉及判断） - "上月销售额正常吗" → anomaly-detection（需要判断是否异常） - "帮我看看库存情况" → 如果语境含"有没有问题"的意味 → anomaly-detection；如果只是想看数据 → metric-query **与相邻 Skill 的区分**： - metric-query 负责"查数据"——拿到原始数值 - anomaly-detection（本 Skill）负责"发现问题"——判断数据是否异常 - metric-attribution 负责"解释问题"——定位异常的原因 本 Skill 处于查询和归因之间。如果检测到异常，可以建议用户使用 metric-attribution 做进一步归因。 **重要：本 Skill 的所有数据查询均**委派 metric-query Skill 执行**，不直接调用 Gateway API。获取数据后在本地进行异常判定。**
As a process D 41/100 · 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 41/100
- 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 (anomaly-detection) differs from the folder (aloudata-anomaly-detection)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 26 steps
- 100Execution cost. Instruction body is 2663 tokens
- 100Running it twice. No mutating operations
- low 11 top-level sections: this looks like several domains in one skill
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
- -213 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 10 example trigger phrases
- +3Description length 756: enough signal without eating the budget
- +4Structure: 30 headings
- +3Step-by-step instructions: 26 items
- +4Has examples (15 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.