How do I install the Parking skill?

Run npx skillmds@latest add cyrillemat/parking in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Parking safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Parking?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Parking free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Parking?

CyrilLeMat (@cyrillemat) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Parking by cyrillemat — Agent Skill

Name: Parking
Author: CyrilLeMat

Parking

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.

CyrilLeMat 889d222 6 files · 18.2 KB Updated Sep 17, 2026

File contents

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

Extract these structured features from the request:
- zone_type
- day
- hour
- is_public_holiday
- has_resident_permit

Call the decision tree and treat its result as authoritative (bundled at scripts/can_i_park.py):

from scripts.can_i_park import can_i_park
verdict = can_i_park({"zone_type": zone_type, "day": day, "hour": hour, "is_public_holiday": is_public_holiday, "has_resident_permit": has_resident_permit})

Relay verdict to the user. Do not override it. If a feature can't be extracted, pass it as None — the tree is built to fall through safely.

Gray zones to surface

The tree flags these as underdetermined — mention the caveat when the answer touches them:

(n2) Suspended on public holidays (is_public_holiday true falls through). Window is the clock hours 11 and 12 (>=11 and <13).
(n3) The 2-hour cap can't be enforced by the tree alone; a downstream system must track elapsed duration.

Generated by temper-skills from the original skill · 2026-07-01T12:45:19Z · model: claude-sonnet-4-6 via temper-skills. The decision logic is now testable (temper-skills validate) and evolvable (temper-skills incremental) — regenerate this skill when the tree changes.

---name: parkingdescription: 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.---# 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 answer1. 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 from scripts.can_i_park import can_i_park verdict = can_i_park({"zone_type": zone_type, "day": day, "hour": hour, "is_public_holiday": is_public_holiday, "has_resident_permit": has_resident_permit}) ```3. Relay `verdict` to the user. **Do not override it.** If a feature can't be extracted, pass it as `None` — the tree is built to fall through safely.## Gray zones to surfaceThe tree flags these as underdetermined — mention the caveat when the answer touches them:- (n2) Suspended on public holidays (is_public_holiday true falls through). Window is the clock hours 11 and 12 (>=11 and <13).- (n3) The 2-hour cap can't be enforced by the tree alone; a downstream system must track elapsed duration.---Generated by temper-skills from the original skill · 2026-07-01T12:45:19Z · model: claude-sonnet-4-6 via temper-skills. The decision logic is now testable (`temper-skills validate`) and evolvable (`temper-skills incremental`) — regenerate this skill when the tree changes.

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