A comprehensive AI agent skill for navigating global parking markets, managing reservations, and maintaining a local memory of favorite parking spots.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install parking
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install parking using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Parking Radar?
Parking Radar is a specialized skill designed for AI agents to handle the complexities of global parking, moving far beyond basic map searches. It serves as an intelligent layer that distinguishes between discovery tools, payment apps, and reservation marketplaces to ensure users find actual, guaranteed inventory. By integrating this into your Openclaw Skills library, you gain a tool that understands the nuanced differences between on-street payment flows and pre-booked event parking.
The skill excels at selecting the right provider based on geographic signals, ensuring that whether you are in North America, Europe, or Asia, the agent uses the dominant local operator. It prioritizes technical precision by verifying vehicle-specific constraints like height limits and license plate requirements before suggesting a spot. As a core component of the Openclaw Skills ecosystem, it maintains high standards for data privacy by keeping sensitive preferences in local memory.
Parking Radar Use Cases
- Locating immediate parking options near a user's current coordinates with live-signal triage.
- Securing guaranteed reservations for high-stress locations like airports, stadiums, and concert venues.
- Managing a local database of favorite parking entrances and recurring city-specific defaults.
- Researching parking provider dominance and API availability for logistics or development projects.
- Filtering parking results based on specific vehicle profiles, such as EV charging needs or height restrictions.
How Parking Radar Works
- The skill classifies the user request into one of five modes: find-now, pre-book, recurring-city, operator research, or API scouting.
- It identifies the most relevant provider family for the target city using a global provider registry.
- The agent searches for facilities, clearly distinguishing between mere listings and reservable inventory.
- It verifies critical constraints, including entry/exit windows, vehicle dimensions, and plate requirements.
- Upon user approval, it facilitates the booking workflow or handoff to the official provider surface.
- Significant discoveries or favorite spots are saved to the local directory to enhance future Openclaw Skills interactions.
Parking Radar Setup
To initialize the Parking Radar skill, ensure the local configuration directory is created. If the path does not exist, you must run the setup sequence to generate the necessary memory files.
# Create the local parking memory directory
mkdir -p ~/parking/
# The agent will then populate the directory with necessary templates:
# memory.md, favorites.md, cities.md, and findings.md
No external API keys are required for general research and discovery, though live bookings will require user-mediated data entry on provider platforms.
Parking Radar Data Schema & Taxonomy
Parking Radar utilizes a structured local file system within ~/parking/ to maintain intelligence without compromising privacy. Data is organized as follows:
| File/Directory | Purpose |
|---|---|
memory.md |
Stores activation defaults, vehicle profiles, and booking boundaries. |
favorites.md |
A ledger of user-approved car parks, recurring venues, and trusted entrances. |
cities.md |
Contains city-by-city provider preferences and local fallback orders. |
findings.md |
Documentation of user-confirmed discoveries, including warnings and quality signals. |
sessions/ |
Log of complex search trails for multi-step trip planning or comparisons. |
archive/ |
Storage for historical notes no longer required in the active path. |
Parking Radar Advanced Features
- Intelligent provider routing that switches between marketplaces like SpotHero and operators like APCOA based on location.
- Local-first memory architecture that prevents sensitive habit tracking on external servers.
- Support for vehicle-specific filtering, ensuring recommendations match the user's car height and type.
- Multi-agent compatibility, allowing it to work alongside other Openclaw Skills for seamless travel and transit planning.
- Evidence-based discovery logging to separate verified parking facts from unverified hearsay.
SKILL.md
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