What problem does it solve? Physical mail is the most expensive touch in an outreach sequence, and sending letters to every contact wastes budget on low-fit targets. This Skill scores and ranks contacts by acquisition-fit signals so letter spend goes only to the highest-quality subset. ## Core Features & Use Cases - Fit-Based Scoring: Scores companies on revenue fit, headcount fit, company vintage, prior deal intent, and revenue-per-employee efficiency, with a hard independence filter excluding investor-backed firms. - Provider-Agnostic, Cache-First: Reads normalized screening rows already produced by the cycle (from udu, Inven, or both), tolerating missing fields without blocking the ranking. - Operator-Gated Cutoff: Presents a ranked table with coverage stats and cluster breaks, then lets the operator pick the top-N cutoff before handing the subset to /send-letter. - Use Case: In a mid-conviction niche with 20 sequenced contacts, rank them, pick the top 8, and cut postage cost by 60% while the ranked-out contacts still receive email and LinkedIn touches. ## Quick Start Ask Claude Code to run the letter-rank skill for your niche, for example: rank the contacts in my current outreach cycle and suggest a cutoff for the letter send.
