A real-time transit data integration for querying MBTA subway, bus, commuter rail, and ferry information.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install mbta-transit
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 mbta-transit 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 MBTA Transit Skill?
The MBTA Transit Skill is a specialized integration designed for the Openclaw Skills ecosystem that connects AI agents directly to the Massachusetts Bay Transportation Authority (MBTA) V3 API. It enables developers and automated agents to retrieve live transit data, including precise departure predictions, active service disruptions, and the geographic positions of vehicles across the Boston metropolitan area.
By leveraging this skill, users can transform raw transit data into actionable intelligence. Whether you are building a personal assistant to monitor your morning commute or a logistics bot tracking commuter rail efficiency, this Openclaw Skills entry provides the technical bridge to the MBTA's comprehensive transit network.
MBTA Transit Skill Use Cases
- Tracking next departure times for specific subway lines or bus routes from any Boston station.
- Monitoring real-time service alerts to proactively route around delays or closures.
- Visualizing live vehicle positions to check the exact location and occupancy of incoming trains.
- Finding specific stop IDs and platform information for complex transit hubs like South Station or Harvard Square.
How MBTA Transit Skill Works
- The user or AI agent initiates a query using the
mbta.pyscript with specific parameters such as a stop ID or route name. - The skill interfaces with the MBTA V3 API, using an optional API key to ensure high-velocity data retrieval.
- Data is fetched for the requested endpoint (predictions, alerts, or vehicles) and processed into a clean, readable format.
- The formatted output is returned to the agent, providing technical details like occupancy, heading, and predicted arrival times.
MBTA Transit Skill Setup
To get started with this skill in the Openclaw Skills framework, follow these steps:
- (Optional) Obtain a free API key from api-v3.mbta.com for higher rate limits.
- Set your environment variable:
export MBTA_API_KEY='your_api_key_here'
- Run the Python script to verify the connection:
python3 scripts/mbta.py routes --type 1
MBTA Transit Skill Data Schema & Taxonomy
The MBTA Transit Skill organizes transit data into specific categories to ensure high-fidelity responses.
| Data Type | Key Metadata Fields |
|---|---|
| Stops | Stop ID, Name, Platform Info, City |
| Departures | Predicted Time, Route Name, Destination |
| Alerts | Severity, Effect Type, Description, Last Updated |
| Vehicles | Label, Current Status, Heading, Occupancy |
MBTA Transit Skill Advanced Features
- Support for Parent Station IDs (e.g.,
place-sstat) to aggregate data across multiple platforms and lines. - Live occupancy tracking to determine how crowded a specific bus or train is before arrival.
- Multi-modal filtering, allowing agents to browse routes by specific types such as Commuter Rail (Type 2) or Ferry (Type 4).
- Seamless integration with AI agents, allowing natural language requests to be translated into structured API calls.
SKILL.md
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