AD smyx-pet-eating-speed-slow-feed-analysis
Triggers when a user provides a video of the pet food-bowl area for analysis; supports local uploads or network URLs to call server-side APIs for eating-speed detection, recording start/end timestamps of feeding, estimating eating speed (g/s and seconds-per-bowl), and when the speed falls below the safety threshold (e.g. < 30 sec/bowl) emitting an intervention signal (slow-feed baffle pop-up or voice prompt) to prevent choking and vomiting (without diagnosing diseases). Application scenarios: smart slow-feeder bowls, pet health management, canine care. | 当用户提供食盆区域视频时，触发本技能进行进食速度检测分析；支持通过上传本地视频或网络视频URL，调用服务端API记录进食开始/结束时间，计算进食速度（克/秒），当低于安全阈值（例如 < 30 秒/碗）时触发外部干预信号（智能慢食碗隔板弹出、语音提醒），预防噎食与呕吐（不诊断疾病）。应用场景：智能慢食碗、宠物健康管理、犬类护理。
Triggers when a user provides a video of the pet food-bowl area for analysis; supports local uploads or network URLs to call server-side APIs for eating-speed…
As a process D 41/100 · Unfinished process — weak spots: steps, result and completion, inputs and preconditions
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: 31. 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 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
- 25Steps. 1 steps
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1483 tokens
- 100Running it twice. No mutating operations
- low 10 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
- -260 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 725: enough signal without eating the budget
- +4Structure: 20 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.