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perfect-company-list

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Build a near-complete company list for any ICP by learning how your real customers label themselves, pulling every industry and keyword they use, judging every single row with a cheap AI model (Jev or gpt-5-nano), and running a Claygent lookalike loop until it runs dry. The method from the SCULPT 2026 talk "Build the perfect company list". Use when a database filter feels too narrow or too noisy, when someone says "find every company like our customers", "our TAM looks too small", or "why is this list full of junk".

skills/perfect-company-list/SKILL.md

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# Perfect company list

Database filters fail in two directions at once.

- **They miss real fits.** Industry labels are self-reported, or guessed: many LinkedIn company
  pages carry the banner "This listing was automatically created by LinkedIn", which means nobody
  at the company picked the label. We found an FDIC bank listed as *Construction*, an SEC-registered
  wealth manager listed as *Semiconductor Manufacturing*, and a New Jersey school district listed
  as *Retail Luxury Goods and Jewelry*.
- **They pull in junk.** A plain `Banking` + `United States` filter returned 16,895 companies. About
  10,500 of them were not banks: 3,679 mortgage companies and lenders, 2,414 credit unions, 2,210
  dead sites or unrelated businesses (a Toyota dealer, a pawn and gun shop, a youth soccer club),
  839 vendors that sell to banks, and the Federal Reserve Bank of St. Louis.
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