AD smyx-ear-health-snapshot-analysis
Triggers when a user provides a pet ear/scratching/head-shaking video URL or file for analysis; uses smart camera to monitor head-shaking and ear-scratching actions, automatically captures HD ear-canal images, and applies AI vision analysis to identify ear-canal color (pink/red/dark red), presence of black granular discharge (ear mites), and degree of earwax accumulation. When redness, large discharge or suspected ear mites are detected, outputs abnormality alerts and recommends owner check-up or veterinary visit. Application: pet families, boarding centers, pet hospital pre-screening. Helps early detection of ear-canal diseases and prevents deterioration. | 当用户提供宠物甩耳、挠耳或耳道抓拍视频URL或文件时，触发本技能进行耳道健康监测分析；通过智能摄像头实时监测甩耳/挠耳动作，自动触发高清抓拍耳道内部图像，利用AI视觉分析识别耳道颜色（粉红/红/暗红）、有无黑色颗粒状分泌物（耳螨）、耳垢堆积程度等异常指标；当检测到红肿、大量分泌物或疑似耳螨时，输出异常提示，建议主人进一步检查或就医，仅输出基于视觉的客观描述与建议，不提供医疗诊断。应用场景：宠物家庭日常健康监测、寄养中心批量监控、宠物医院预检。
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 1416 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)
- +3Description length 890: 120–800 characters recommended
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- -255 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +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: 78.