AD smyx-pet-water-fountain-intake-analysis
Triggers when a user provides a pet water fountain area video URL or file for analysis; supports local video uploads or network URLs to call server-side APIs for water intake behavior recognition, tracking drinking frequency, single-session duration, and estimated daily intake per pet, comparing against historical baselines to detect sudden drops or spikes in water consumption, outputting early warning alerts for potential kidney disease or diabetes risk. Application scenarios: smart water fountains, multi-pet household health monitoring, pet chronic disease management. | 当用户提供饮水机区域视频URL或文件时，触发本技能进行饮水量行为分析；支持通过上传本地视频或网络视频URL，调用服务端API进行饮水行为识别，统计每只宠物的饮水次数、单次时长、日总饮水量，对比历史基线，对饮水骤降或骤升进行预警，辅助早期发现肾病或糖尿病风险。应用场景：智能饮水机、多宠家庭健康监测、宠物慢性病管理。
As a process D 38/100 · 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: 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 38/100
- 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
- 30Running it twice. 1 mutating operations with no state check
- 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 1504 tokens
- 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
- -258 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 736: 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.