A real-time transit data integration for Austin CapMetro providing live vehicle tracking, arrivals, and service alerts via open data feeds.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install capmetro-skill
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 capmetro-skill 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 CapMetro Austin Transit Skill?
This Openclaw Skills integration enables AI agents to interact directly with Austin's CapMetro transit system. It leverages open-access GTFS and GTFS-RT feeds from the Texas Open Data Portal to provide up-to-the-minute information on bus and rail services without requiring an API key. By integrating this skill, users can query real-time vehicle positions, check for service disruptions, and plan trips across MetroRail, MetroRapid, and local bus routes.
The skill is designed for developers building Austin-centric assistants that need reliable, low-latency transportation data. It synthesizes complex Protobuf and JSON feeds into actionable insights, allowing an AI to provide precise arrival times and location-based transit advice to users navigating the Austin area.
CapMetro Austin Transit Skill Use Cases
- Checking the next arrival time for specific bus routes like the 801 or 803 MetroRapid.
- Monitoring MetroRail Red Line positions and delays during peak commute hours.
- Searching for nearby transit stops based on geographic coordinates or landmarks like The Domain.
- Getting instant notifications about service alerts, detours, or system-wide disruptions.
- Planning trips by accessing comprehensive route information and current fare details for local and rapid services.
How CapMetro Austin Transit Skill Works
- The skill initializes by downloading static GTFS data containing routes and stops to a local cache on the first run.
- When a user queries transit info, the skill fetches live Protobuf or JSON feeds from the Texas Open Data Portal, which are updated every 15 seconds.
- The data is parsed locally to extract vehicle positions, trip updates, or service alerts using the provided mjs scripts.
- The script matches real-time data against static stop and route definitions to provide human-readable context.
- The AI agent presents the formatted results, such as 12-hour formatted arrival times or landmark-relative locations, to the user.
CapMetro Austin Transit Skill Setup
First, ensure you have Node.js and unzip installed. Then, navigate to the skill directory and install the necessary dependencies for this Openclaw Skills component:
npm install protobufjs
Initialize the static transit database by running the refresh command:
node scripts/capmetro.mjs refresh-gtfs
This setup ensures you have the latest schedule data stored locally in ~/.capmetro/gtfs/ for optimal performance.
CapMetro Austin Transit Skill Data Schema & Taxonomy
The skill organizes data into a local directory structure and processes real-time feeds as follows:
| Component | Location/Format | Description |
|---|---|---|
| Static GTFS | ~/.capmetro/gtfs/ |
Local cache of routes, stops, and schedules parsed from ZIP files. |
| Vehicle Positions | JSON/Protobuf | Real-time GPS coordinates and timestamps of active buses and trains. |
| Trip Updates | Protobuf | Estimated arrival times and delay offsets for specific stops. |
| Service Alerts | Protobuf | Text-based notifications regarding detours, cancellations, or disruptions. |
CapMetro Austin Transit Skill Advanced Features
- Real-time vehicle filtering by specific route IDs to isolate tracking for specific lines.
- Fuzzy search capabilities for transit stops based on names or proximity to GPS coordinates.
- Automated calculation of arrivals using both scheduled times and real-time estimated offsets.
- Multi-format data fetching with fallback support between JSON and Protobuf feeds for high availability.
- Localized landmark-relative positioning to help users visualize where a bus or train is currently located.
SKILL.md
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