What problem does it solve? Cold emails with generic or shallow personalization get ignored. This Skill turns raw prospect data into structured, signal-based personalization so outbound emails reference real observations about the prospect rather than just their name and company. ## Core Features & Use Cases - Signal Detection Framework: Six ranked signal categories (hiring, funding, technology, content, company events, performance) with data sources and personalization angles for each. - Personalization Hierarchy & Scoring: A 0-5 quality scale and effort-to-impact tiers that match personalization depth to deal size and volume. - Variable Architecture & Bucket Strategy: Three-tier variable system (auto-populated, enrichment-derived, research-derived) plus a bucket method for personalizing 1,000+ lead campaigns with segment-specific openers. - Use Case: An SDR team has 2,000 leads and needs personalized openers at scale. Segment the list by signal type, write five bucket-specific opening lines, and layer in Tier 1 variables so every email feels researched. ## Quick Start Use the personalization-engine skill to build a signal-based personalization plan for my outbound campaign targeting VPs of Sales at Series B SaaS companies.