Find attendees at conferences/events, research their companies, qualify against ICP, and launch outreach
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End-to-end workflow: find event attendees → research → qualify against ICP → deduplicate → outreach.
For Luma-only qualified lead gen with built-in Google Sheets + Slack alerting, use [[skills/composites/get-qualified-leads-from-luma/SKILL.md]] instead. This playbook is the full pipeline including outreach.
Skills: luma-event-attendees OR conference-speaker-scraper
luma-event-attendeesconference-speaker-scraperluma-event-attendees Apify search mode to find events firstOutput: Person list with names, bios, LinkedIn/Twitter URLs, companies.
Capability: Web search
For each person/company:
Skip if user just wants a raw attendee list.
Skill: lead-qualification
Filter the enriched list against the client's ICP criteria. Score each lead.
Skill: company-contact-finder
For qualified companies, find the specific decision-makers with email addresses.
Skill: contact-cache
Check all leads against the contact cache to prevent duplicate outreach across strategies.
Capability: Google Sheets or CSV export
Export qualified, deduplicated leads with columns: Name, Title, Company, LinkedIn URL, Email, Signal, Score.
Skill: cold-email-outreach
If approved, set up personalized outreach via your chosen outreach tool or direct email via AgentMail API (agentmail.dev).
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Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.
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Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
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For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.
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The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.
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Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).
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Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.
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