SKILLEMALL.ai

AD anomaly-detection

AI时序异常检测技能。基于Amazon Chronos-2零样本时序大模型,对用户提供的数据(CSV/Excel/JSON/粘贴)或API接口数据,进行多方法融合异常检测(Z-Score/MAD/IQR/移动平均偏离),自动分类异常类型(点异常/上下文异常/集体异常/水平偏移)并评定严重度(P0-P2),生成包含时序标注图、残差分析、异常分布、热力图、详细列表的交互式HTML可视化报告。触发词:异常检测、异常分析、时序异常、数据异常、检测异常、anomaly detection、找异常点、异常报告。

ClawHub Agent Skills author: bettermen v1.0.0 MIT-0 4 files body ≈ 896 tokens Open the sourceclawhub.ai analyzed 20 h ago

AI时序异常检测技能。基于Amazon…

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
74
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • 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: 4. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "agent_created"

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 (python) that frontmatter does not declare
  • 100Steps. 52 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 896 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

  • +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
  • +1No license
  • +2Single-language instructions
  • +3Description length 252: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 52 items
  • +4Has examples (4 code blocks)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.

External checks

ClawHub: suspicious
The skill appears to be a time-series analysis/reporting tool, but it can silently install Python packages and download model artifacts during normal use, which deserves manual review before installation.
LLM: suspicious (medium) · VirusTotal: · 20 Jun 2026