parking

仓库创建 2026年7月2日最近提交 21 天前SkillHot 收录 20 天前

▸ 精选理由

可审计的确定性决策，适合合规与自动化场景

这个 Skill 做什么

基于规则树按地段/时间等判定停车许可并返回具体结论

把路段、日期、小时、节假日与居民许可等信息整理成结构化特征，交给内置的规则树 can_i_park() 得出明确结论（如禁止、街道清扫限停、付费 2 小时、免费或需许可）。适合必须一致且可审计的停车判定场景，不能靠聊天理解或临时判断。注意：决策是“冻结”的、零 LLM 调用，规则树已版本控制，你只负责提特征并转述结果，不要改写判定逻辑。

▸ 展开 SKILL.md 英文原文

Frozen, deterministic decision (no LLM): maps zone_type, day, hour, is_public_holiday, has_resident_permit to one of prohibited, no_parking_street_cleaning, pay_and_display_2h, free, permit_required. Use when this decision must be made consistently and auditably — extract the features, call can_i_park(), and relay its verdict without overriding it.

自动化集成停车规则决策合规审计通用

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安装 / 使用

给你的 Agent 一句话（通用）

帮我安装这个 skill：https://raw.githubusercontent.com/CyrilLeMat/temper-skills/main/examples/parking/output/parking/SKILL.md

或 curl 直取 SKILL.md

curl -fsSL "https://raw.githubusercontent.com/CyrilLeMat/temper-skills/main/examples/parking/output/parking/SKILL.md"

SKILL.MD 节选查看完整文件 ↗

# can_i_park — skill (tempered by temper-skills)

You are an 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_i_park.can_i_park`, 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:
   - `zone_type`
   - `day`
   - `hour`
   - `is_public_holiday`
   - `has_resident_permit`
2. Call the decision tree and treat its result as authoritative (bundled at `scripts/can_i_park.py`):

   ```python

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