A high-performance AI agent skill designed to locate fitness centers and gyms based on real-time geolocation data and user preferences.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install gyms
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 gyms 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 Gyms?
The Nearby Gyms skill empowers AI agents to identify and list fitness facilities in the immediate vicinity of a user. As a part of the Openclaw Skills ecosystem, it provides a bridge between raw geolocation data and actionable fitness information. The skill focuses on delivering a standardized data format, making it exceptionally easy for developers to integrate gym-finding capabilities into larger health or lifestyle applications.
By leveraging this skill, developers can ensure their agents provide accurate, filtered, and localized results. It handles complex tasks such as radius calculations, result limiting, and amenity filtering (like swimming pools or private coaching) out of the box, allowing for a more personalized user experience within any platform utilizing Openclaw Skills.
Nearby Gyms Use Cases
- Locate fitness centers within a specific radius of a user's current coordinates.
- Filter local gyms based on specialized amenities such as private coaching or swimming pool availability.
- Generate a standardized list of health-related Points of Interest (POI) for data visualization in fitness apps.
How Nearby Gyms Works
- The AI agent triggers the skill when a user asks for nearby gyms or fitness centers.
- The skill receives the mandatory latitude and longitude coordinates from the user's authorized location data.
- It processes optional parameters such as search radius, result limits, and specific amenity filters.
- The skill queries a location provider to retrieve relevant fitness center data.
- It formats the data into a standardized JSON response, ensuring consistency across the Openclaw Skills framework.
- The final list is presented to the user or passed to a frontend interface for rendering.
Nearby Gyms Setup
To integrate this skill into your environment, follow these standard steps for Openclaw Skills:
# Navigate to your agent's skill directory
cd openclaw-agent/skills
# Install the nearby-gyms package
npm install @openclaw/skill-nearby-gyms
# Set up your environment variables for location providers
export LOCATION_PROVIDER_API_KEY="your_key_here"
Nearby Gyms Data Schema & Taxonomy
The skill utilizes a structured schema to maintain consistency with other Openclaw Skills. Below are the primary input and output specifications:
| Parameter | Type | Default | Description |
|---|---|---|---|
| location | object | Required | Contains lat and lng coordinates. |
| radius_meters | integer | 3000 | The search radius for the query. |
| limit | integer | 20 | Maximum number of gyms returned (Max 50). |
| filters | object | null | Options for swimming, coaching, or ratings. |
| category | string | "gyms" | Fixed category identifier for the response. |
Nearby Gyms Advanced Features
- Privacy-centric design that supports grid-based location obfuscation to protect user coordinates.
- Standardized error handling for scenarios such as invalid locations, provider downtime, or rate limiting.
- Seamless integration with the Openclaw Skills standard response format for consistent multi-agent workflows.
- Configurable result filtering to prioritize highly-rated facilities or specific service types.
SKILL.md
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