BD smyx-eye-anomaly-detection-analysis
AI-powered pet eye anomaly detection from close-up facial images/video. Detects conjunctival redness, abnormal tearing/tear stains, and pupil/cornea opacity (cataract / corneal edema), then outputs anomaly alerts to help owners catch eye disease risks early. Scenarios: daily home health self-check, boarding center routine inspection, animal hospital triage, senior pet cataract monitoring. | 通过宠物摄像头捕捉宠物面部近景视频，利用AI视觉分析技术检测眼部充血（结膜颜色发红）、异常流泪（泪痕严重或持续性溢泪）、瞳孔区域浑浊（可能为白内障或角膜水肿）等异常征象，输出异常提示，帮助主人及早发现眼部疾病风险。适用于日常健康监测、老年宠物护理及宠物医院预检。应用场景：宠物家庭日常健康自检、宠物寄养中心巡检、宠物医院门诊初筛、老年宠物白内障监测。
As a process D 35/100 · Unfinished process — weak spots: steps, result and completion, when it triggers
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 35/100
- 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
- 20When it triggers. No condition that starts the skill
- 25Steps. 1 steps
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1500 tokens
- 100Running it twice. No mutating operations
- low 12 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
- -269 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 569: enough signal without eating the budget
- +4Structure: 22 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: 69.