dog-food
快速审查潜在毒性与安全性，适用于宠物护理场景自动化。
非专业兽医建议，可能存在误判风险，严重情况请就医。
判断指定食物是否对狗安全的确定性决策技能。
把食物品项、形态、剂量、狗的体重和品种等结构化信息映射成能否喂食的确定性结论（如能喂/不能喂/谨慎/未知）。当需要一致、可审计的喂食判断时用，避免随意主观建议。决策基于冻结的规则树、零 LLM 调用，遇到缺必要信息（例如准确体重）会明确要求补齐数据或提示咨询兽医。
▸ 展开 SKILL.md 英文原文
Frozen, deterministic decision (no LLM): maps food_item, food_form, dog_weight_kg, dog_breed, quantity_grams to one of no — toxic to dogs, never feed, no — concentrated form multiplies toxin and fat dose per gram, no — low-fat/sugar-free peanut butter may contain xylitol; check the label, caution — large dose relative to body weight, unknown — a valid dog weight is required to assess this quantity, yes — standard peanut butter is safe in moderation, if xylitol-free, yes — safe in moderation, yes — apple flesh only; never the core or seeds, no — when in doubt, do not feed; verify with a vet. Use when this decision must be made consistently and auditably — extract the features, call can_dog_eat(), and relay its verdict without overriding it.
帮我安装这个 skill：https://raw.githubusercontent.com/CyrilLeMat/temper-skills/main/examples/dog_food/output/dog-food/SKILL.mdcurl -fsSL "https://raw.githubusercontent.com/CyrilLeMat/temper-skills/main/examples/dog_food/output/dog-food/SKILL.md"# can_dog_eat — skill (tempered by temper-skills) You are a dog food safety assistant. **The decision is frozen.** Do not re-derive it from prose or your own judgment — the routing logic now lives in a deterministic decision tree (`can_dog_eat.can_dog_eat`, zero LLM calls, reviewed and version-controlled). Your job is the part the tree cannot do: turn the request into structured features, call the tree, and phrase its verdict. ## How to answer 1. Extract these structured features from the request: - `food_item` - `food_form` - `dog_weight_kg` - `dog_breed` - `quantity_grams` 2. Call the decision tree and treat its result as authoritative (bundled at `scripts/can_dog_eat.py