A specialized geolocation skill for identifying and filtering top-rated brunch locations based on a user's real-time coordinates.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install brunch-spots
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 brunch-spots 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 Nearby Brunch Spots?
The Nearby Brunch Spots skill is a robust location-intelligence tool designed for Openclaw Skills. It enables AI agents to provide users with highly relevant dining recommendations by querying local Point of Interest (POI) data. By integrating geographic coordinates with specific dining intent, it bridges the gap between digital assistance and real-world exploration.
This skill is engineered to return standardized data structures, making it an essential component for developers building travel, lifestyle, or assistant-based AI workflows. It ensures high-quality results by supporting advanced filtering such as minimum ratings, price levels, and current operating hours.
Nearby Brunch Spots Use Cases
- Finding the highest-rated brunch spots within a walking distance of the user's current location.
- Filtering local eateries for weekend brunch based on specific price levels or open-now status.
- Integrating automated dining recommendations into travel itineraries or scheduling agents.
- Providing quick responses to natural language queries like 'brunch near me' or 'where is the best brunch in this city'.
How Nearby Brunch Spots Works
- The AI agent detects a brunch-related intent and captures the user's latitude and longitude coordinates.
- The Openclaw Skills interface invokes the Nearby Brunch Spots skill with specified parameters like search radius and filters.
- The skill queries the underlying POI data provider while adhering to privacy-first coordinate handling.
- Results are processed, filtered according to user preferences (rating, price, etc.), and cached for performance optimization.
- A standardized POI list is returned to the agent to be presented to the user.
Nearby Brunch Spots Setup
To integrate this skill into your Openclaw Skills environment, add the following configuration to your agent manifest:
# Install the skill via the Openclaw CLI
openclaw install nearby-brunch-spots
Ensure your application has the necessary permissions to access geolocation data and configure your API keys for the POI data provider in your .env file.
Nearby Brunch Spots Data Schema & Taxonomy
The skill follows the Openclaw Skills standard response format for POI data.
| Parameter | Type | Description | Required |
|---|---|---|---|
location |
Object | { lat, lng } coordinates |
Yes |
radius_meters |
Integer | Search radius (Default: 3000m) | No |
limit |
Integer | Max results (Default: 20, Max: 50) | No |
filters |
Object | open_now, min_rating, price_level, keywords |
No |
Output Category: Fixed as brunch-spots for consistent metadata taxonomy.
Nearby Brunch Spots Advanced Features
- Smart caching: Automatically caches location-based queries to reduce latency and API costs.
- Privacy-centric Grid Obfuscation: Supports optional grid-based blurring of precise coordinates to protect user privacy.
- Error Handling: Includes specific exception types for
INVALID_LOCATION,RADIUS_TOO_LARGE, andRATE_LIMITEDscenarios. - Multi-Filter Support: Allows complex queries combining ratings, price levels, and keyword-based semantic searches.
SKILL.md
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