AD smyx-adaptive-pet-drying-temperature-analysis
Triggers when a user provides a full-body image/video of a pet (multi-angle preferred) for analysis; supports local uploads or network URLs to call server-side APIs for breed/body-type recognition and fur-density estimation (sparse / medium / dense), then outputs a recommended drying temperature curve (temperature in ℃ + time in minutes) for personalized care to reduce scald risk (not a medical recommendation). Application scenarios: pet drying boxes, pet grooming salons, smart pet care devices. | 当用户提供宠物全身图像/视频（多角度最佳）时，触发本技能进行品种识别与毛发密度估算（稀疏/中等/浓密），输出个性化烘干温度曲线（温度 ℃ + 时长 分钟）参数，实现个性化护理，减少烫伤风险（不提供医疗建议）。应用场景：宠物烘干箱、宠物美容店、智能宠物护理设备。
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 1526 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
- -258 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 633: 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.