BD smyx-pet-stool-morphology-recognition-analysis
Triggers when a user provides an image/video URL or file of dog toilet area or outdoor dog-walking path for analysis; supports local uploads or network URLs to call server-side APIs for pet stool morphology recognition, analyzing stool color (brown, black, red, white), shape (formed, loose/soft, watery, granular hard), and the presence of blood or mucus, outputting standardized abnormal observation features to help early discovery of gastrointestinal diseases (without diagnosing diseases). Application scenarios: dog toilets, outdoor dog-walking path cameras, pet health monitoring, multi-pet households. | 当用户提供狗厕所或户外遛狗路径区域的粪便图像/视频时，触发本技能进行排便形态识别分析；支持通过上传本地文件或网络URL，调用服务端API识别粪便颜色（棕、黑、红、白）、形状（条状、稀糊、颗粒）、是否带血或粘液，输出异常特征观察结果，帮助早期发现肠胃疾病（不诊断疾病）。应用场景：狗厕所、遛狗路径摄像头、宠物健康监测、多宠家庭。
Triggers when a user provides an image/video URL or file of dog toilet area or outdoor dog-walking path for analysis; supports local uploads or network URLs…
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: 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 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 1411 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)
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- -254 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 775: enough signal without eating the budget
- +4Structure: 19 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.