AD smyx-uv-safety-monitor-analysis
AI-powered UV disinfection safety monitor for pets. Real-time camera analysis detects whether a pet enters an active UV-C disinfection zone and whether the UV lamp is on (via blue-purple glow recognition or smart-home API linkage). When both conditions are met, it auto-triggers a high-risk alert, recommends shutting off the UV lamp, and logs the event to prevent corneal burns or skin damage. Scenarios: smart homes, pet households, pet boarding facilities. | 通过智能家居摄像头实时识别宠物是否进入正在进行紫外线消毒的区域，自动关闭UV灯并推送提醒，防止宠物因误入消毒区而受到紫外线伤害。结合目标检测（宠物识别）与UV灯状态感知（可通过画面蓝紫色光晕/光谱特征或智能家居API联动），实现主动式安全防护。应用场景：智能家居、宠物家庭、宠物寄养场所。
As a process D 35/100 · Unfinished process — weak spots: steps, result and completion, when it triggers
How to improve
- 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: 30. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 35/100
- 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
- 25Steps. 1 steps
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1533 tokens
- 100Running it twice. No mutating operations
- low 13 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
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- -275 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 606: enough signal without eating the budget
- +4Structure: 23 headings
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.