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  VibeClaw

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  Pick your location

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  Finding your vibe

VibeClaw

Inspiration

VibeClaw was born out of a desire to explore OpenClaw, a local AI agent framework that has been generating buzz in the automation community. The core motivation was to replicate a recurring workflow that many travelers face: finding events, activities, and happenings based on current weather conditions or upcoming travel plans. Traditional methods of discovering events involve manually checking multiple platforms—Instagram, TikTok, Eventbrite—each with their own interfaces and no unified API access. The inspiration came from wanting to automate this scattered discovery process into a single, intelligent query that could be triggered from anywhere.

What it does

VibeClaw is a local AI-powered event discovery assistant that runs entirely on your machine. It leverages OpenClaw's browser automation capabilities to search and gather event information from social media and event platforms that lack public APIs. Users can invoke VibeClaw through various communication channels—email, text message, or even cron jobs—to receive up-to-date event information tailored to their location or travel destination. The system checks weather conditions and combines that with event discovery to provide contextually relevant recommendations, whether someone is looking for outdoor activities on a sunny day or indoor events during rainy weather. By setting up automated triggers, users can receive daily or weekly event digests without lifting a finger.

How we built it

The project was built by creating a custom OpenClaw skill that defines the event discovery workflow. OpenClaw is powered by MiniMax, providing the foundation for browser-based automation and intelligent LLM orchestration. The skill structure includes handlers for different communication triggers (email, SMS, scheduled jobs), browser navigation logic for each target platform (Instagram, TikTok, Eventbrite), and result synthesis to compile findings into actionable summaries. A significant portion of the build focused on prompt engineering to ensure the AI could reliably navigate websites, extract relevant event information, and format results consistently. The integration with weather APIs adds an intelligent layer that filters and prioritizes events based on current conditions at the specified location.

Challenges we ran into

Browser automation presented unexpected complexities, particularly with dynamic content loading on social media platforms. The AI sometimes struggled to identify the correct elements to interact with, especially on sites that use infinite scrolling or lazy-loaded content. Prompt engineering required multiple iterations to achieve reliable results—early versions would either miss important event details or get stuck in navigation loops. Handling rate limiting and anti-bot measures on certain platforms required implementing delays and human-like interaction patterns. Another challenge was designing the communication layer to work with different trigger mechanisms while maintaining a consistent response format regardless of how the query was initiated.

Accomplishments that we're proud of

Successfully getting a multi-platform browser automation workflow working end-to-end represents a major accomplishment. Creating a modular skill structure that can be extended to additional platforms without major refactoring demonstrates thoughtful architecture. The weather-aware event filtering system adds genuine intelligence to what could have been a simple scraping tool. Building a flexible trigger system that accepts input via email, text, or scheduled jobs makes the tool genuinely useful in real-world scenarios. Most importantly, the project proves that OpenClaw can handle complex, multi-step workflows that require real-world interaction beyond simple API calls.

What we learned

This project provided deep exposure to OpenClaw (powered by MiniMax) and its capabilities and limitations as a local automation framework. The skill system is more powerful than initially anticipated, offering extensive customization options for defining agent behavior. Prompt engineering for browser-based tasks requires a different approach than typical LLM prompting—context windows, element identification, and error recovery all need special attention. Browser automation at scale introduces considerations around session management, state persistence, and resource cleanup that don't exist in simpler automation scenarios. The project also revealed opportunities for building more sophisticated travel and lifestyle automations beyond just event discovery.

What's next for VibeClaw

The foundation built for event discovery naturally extends to broader travel assistance functionality. Future iterations could incorporate flight and hotel price monitoring, real-time travel alerts, and personalized itinerary building based on discovered events. Expanding platform coverage to include additional event sources, local guides, and venue-specific information would increase utility. Integrating with calendar applications to automatically schedule discovered events would create a seamless planning experience. Building a simple web interface for manual queries and history viewing would make the tool more accessible. The ultimate vision is a comprehensive local AI travel companion that handles everything from inspiration (what events are happening) to planning (where to stay and how to get there) to execution (real-time updates during the trip).

Built With

• cursor
• minimax
• openclaw