list-expanderlisted

# 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}/`. ``` Phase 1 fingerprint.ts seeds → how each shows up in Prospeo + its live homepage Phase 2 lookalikes.ts seeds → candidates via Prospeo lookalike + Exa + Parallel (qualify candidates → lookalikes-confirmed.csv) Phase 3 mine-filters.ts confirmed fits → candidate filters → scorecard (volume × precision) Phase 4 pull.ts winning filters → wide pull, auto-shard, dedup, exclusions score-batch.ts scale AI qualification (gpt-5-nano by default) verify-website.ts second pass on QUALIFIED rows: live homepage fetch →