End-to-end outbound prospecting: detect intent signals, research companies, find decision-maker contacts, personalize messaging, launch campaign.
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Build and run a complete outbound prospecting system: signal detection → company research → contact finding → personalization → campaign launch.
Based on the client's ICP and motion, select which signals to monitor:
| Signal Source | Best For | Skill |
|---|---|---|
| Job postings | Companies with allocated budget | job-posting-intent |
| Funding announcements | Companies with fresh capital | funding-signal-monitor |
| LinkedIn posts/comments | Practitioners discussing the problem | linkedin-post-research + linkedin-commenter-extractor |
| Conference attendees | People actively engaged with the space | luma-event-attendees |
| Competitor customers | Companies already buying similar solutions | competitor-post-engagers |
Execute selected signal skills with client-specific keywords. Run in parallel.
Output: Raw signal list — companies + signal context.
Skill: lead-qualification
Filter against ICP criteria. Score each lead:
Skill: company-contact-finder
For top qualified companies, find the specific decision-makers:
Skill: contact-cache
Check all leads against the contact cache. Add new leads to cache. Skip any that have been contacted before.
For each lead, generate personalized email sequence using:
Skill: cold-email-outreach
Set up the outreach campaign in your chosen tool:
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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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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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Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different they must be (generate 3x so the user can choose), the test frame, the success metric, and safety rules for the user to accept. Decides what launch-meta-ad-campaign then executes, and re-reads the strategy after a deep check to show which assumption changed. Use it after intake and before any creative is generated or any campaign is built.
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