List Expander — seed companies → lookalikes → mined filters → qualified TAM

The problem this solves

Niche lists come out tiny because we search databases with narrow explicit keywords, but real good-fit companies (e.g. Atlantic Medical Group, Hackensack Meridian) often don't carry that keyword in their database record. Instead of guessing keywords top-down, this skill works bottom-up: take companies you KNOW fit, discover how the database actually tags them, expand via lookalike engines, and only then derive the wide-net filters — with measured precision per filter — before pulling and AI-qualifying at scale.

Pipeline (5 phases)

All scripts live in scripts/, run with npx tsx, and are dependency-free (node ≥ 18 native fetch). Every script takes --help. Keys load from the repo-root .env (see .env.example), falling back to ~/.env. Artifacts land in ~/output/list-expander/{run}/.