AD smyx-vaccination-reminder-analysis
Triggers when a user provides a pet facial image or video URL/file for vaccination reminder analysis; uses AI facial recognition to confirm pet identity, automatically queries the linked vaccination records (last dose date, vaccine type) from the hospital management database, and compares with current date. When the gap since last vaccination exceeds 11 months (or the preset reminder cycle), outputs a due/overdue reminder and suggests re-vaccination. Helps pet hospitals automate client management, raise vaccination coverage and avoid missed doses. Application: hospital front-desk registration, boarding center check-in, pet insurance underwriting. Does NOT provide medical advice — only returns database-comparison results. | 当用户提供宠物面部图像或视频URL/文件时，触发本技能进行疫苗到期提醒分析；利用AI面部识别确认宠物个体身份，自动关联数据库中该宠物的疫苗接种记录（上次接种日期、疫苗类型）并与当前日期比对；若距离上次接种超过11个月（或预设的提醒周期），输出到期/逾期提醒，并建议尽快补种。该技能可帮助宠物医院实现自动化客户管理、提升疫苗接种率、防止漏种。应用场景：宠物医院前台登记、宠物寄养中心入住检查、宠物保险核保。仅输出基于数据库比对的结果，不提供医疗建议。
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 1469 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 957: 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.