get-qualified-leads-from-luma
End-to-end lead prospecting from Luma events. Searches Luma for events by topic and location, extracts all attendees/hosts, qualifies them against a qualification prompt, outputs results to a Google Sheet, and sends a Slack alert with top leads. Use this skill whenever someone wants to find qualified leads from events, prospect event attendees, or run an event-based lead gen workflow. Also triggers for "find people at events and qualify them" or "who's attending X events that matches our ICP."
npx skills add gooseworks-ai/goose-skills --skill get-qualified-leads-from-luma --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Get Qualified Leads from Luma Events Search Luma for events by topic and location, extract all attendees and hosts, qualify them against your ICP, export to a Google Sheet, and send a Slack alert with the top leads. This is a 5-step pipeline that chains together `luma-event-attendees`, `lead-qualification`, Google Sheets output, and Slack alerting. ## Step 0: Clarify Search Parameters Before doing anything, make sure you have clear answers to these questions. If the user's prompt already covers them, skip ahead. Otherwise, ask: 1. **Location** — Where should events be? (e.g., "San Francisco", "New York", "London") 2. **Topics/Keywords** — What event topics? Suggest 3-5 keyword variations to maximize coverage. For example, if the user says "growth marketing", also suggest: "GTM", "demand gen", "startup growth", "growth hacking", "marketing leadership" 3. **Timeframe** — How recent should the events be? (e.g., "past 2 weeks", "past month", "this quarter"). Default to **past 30 days** if the user doesn't specify. Luma search can return events from months or years ago, so always confirm a timeframe to avoid stale results. 4. **Qualification prompt** — Does the user have an existing q
- Step 0: Clarify Search Parameters
- Step 1: Search Luma and Extract Attendees
- Run parallel searches
- Filter by timeframe
- Deduplicate
- Step 2: Save Attendee Data to CSV
- Step 3: Qualify Leads
- Prepare batches
- Run parallel qualification
- Merge results
- Step 4: Create Google Sheet with Results
- Use Rube MCP for Google Sheets
- Writing strategy for large datasets
- Fallback
Run each search variation in parallel
python3 skills/luma-event-attendees/scripts/scrape_event.py --search "AI San Francisco" --output /tmp/luma_search_1.csv
python3 skills/luma-event-attendees/scripts/scrape_event.py --search "Growth Marketing San Francisco" --output /tmp/luma_search_2.csv
python3 skills/luma-event-attendees/scripts/scrape_event.py --search "GTM San Francisco" --output /tmp/luma_search_3.csv
export APIFY_API_TOKEN="your_token"
Or check skills/luma-event-attendees/.env
curl -X POST -H 'Content-Type: application/json' -d '{"text":"test"}' YOUR_WEBHOOK_URLWhat does the get-qualified-leads-from-luma skill do?
End-to-end lead prospecting from Luma events. Searches Luma for events by topic and location, extracts all attendees/hosts, qualifies them against a qualification prompt, outputs results to a Google Sheet, and sends a Slack alert with top leads. Use this skill whenever someone wants to find qualified leads from events, prospect event attendees, or run an event-based lead gen workflow. Also triggers for "find people at events and qualify them" or "who's attending X events that matches our ICP."
How do I install it?
Run `npx skills add gooseworks-ai/goose-skills --skill get-qualified-leads-from-luma --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From gooseworks-ai/goose-skills, a repository with 1,091 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.