A high-efficiency utility to scrape and structure tech event data from lu.ma for any global city without requiring authentication.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install luma
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 luma 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 Luma Events Fetcher?
The Luma Events skill is a specialized tool designed to help developers and AI agents navigate the tech ecosystem by programmatically fetching event data from Luma (lu.ma). As a key component of the Openclaw Skills library, it allows users to search for startup meetups, conferences, and networking events across cities like Bangalore, San Francisco, and London. By extracting data directly from Luma's server-side rendered metadata, it provides real-time access to event details, host information, and ticket availability without the overhead of API keys.
This skill is particularly valuable for those building automated research workflows or personal assistants that need to stay updated on the latest industry gatherings. It bridges the gap between public web data and structured agent memory, ensuring that your automated systems always have a clear view of the tech landscape.
Luma Events Fetcher Use Cases
- Tracking upcoming AI and developer meetups in specific tech hubs.
- Automating the collection of conference data for professional networking.
- Building a local cache of startup events to reduce redundant web searches.
- Comparing tech event density across different cities for travel planning.
- Identifying free vs. paid networking opportunities within a specific timeframe.
How Luma Events Fetcher Works
- The user provides one or more city slugs (e.g., 'new-york' or 'bengaluru') and optional filters like date range.
- The skill initiates a request to the public Luma city page to fetch the raw HTML.
- It specifically targets the NEXT_DATA script tag which contains pre-rendered JSON event metadata.
- The internal logic parses this JSON to extract key fields such as event names, locations, timestamps, and host names.
- The skill converts the data into a standardized format, offering both human-readable text and machine-ready JSON.
- Finally, the results are saved to a local memory file, allowing AI agents to reference the data in future interactions.
Luma Events Fetcher Setup
To start using this tool within your Openclaw Skills environment, navigate to your local scripts directory and execute the Python script directly.
# Fetch events for specific cities for the next 14 days
python3 scripts/fetch_events.py san-francisco london --days 14
# Output events in JSON format for automated processing
python3 scripts/fetch_events.py mumbai --json
This skill requires Python 3.6 or higher and utilizes only standard libraries, meaning no additional pip installations are necessary.
Luma Events Fetcher Data Schema & Taxonomy
The skill organizes extracted data into a clean, hierarchical structure, making it easy to integrate with other Openclaw Skills.
| Property | Description | Format |
|---|---|---|
name |
The title of the event | String |
start |
The UTC start time | ISO 8601 |
url |
The direct lu.ma event link | URL |
venue |
Neighborhood or specific address | String |
ticketStatus |
Current availability (e.g., free, available) | String |
lastFetched |
Timestamp of the data retrieval | ISO 8601 |
All data is persisted in ~/clawd/memory/luma-events.json to enable long-term context for your agent.
Luma Events Fetcher Advanced Features
- Multi-city support allows for batch fetching of event data across multiple global regions in one command.
- Intelligent persistence merging ensures that new data is synchronized with existing local records without creating duplicates.
- Zero-dependency architecture ensures maximum compatibility and easy deployment across different environments.
- Configurable depth limits allow users to control the number of events fetched per city to manage processing overhead.
- Seamless integration with jq or other CLI tools via the --json output flag for advanced data filtering.
SKILL.md
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