name-audition

Name Audition — «Кастинг имён»

Audition candidate names before you cast one. Brandability is not availability, and a free domain is not a safe name — the audition separates the three. Candidates try out; the best one gets cast; the rest simply don't make the cut.

The core lesson this skill encodes

A name can sound perfect, score well, have every domain free — and still be the wrong choice. Three ways a candidate fails its screen test, worst first:

1. Adjacent-domain collision is the worst kind. A product already operating in the target vertical means a name doesn't make the cut even when the string is free to register — confusion and SEO dilution are fatal in the same space.
2. Descriptive / generic names are domain-free but weak. Easy to register, hard to own — bad for trademark, bad for SEO, easy for competitors to crowd.
3. Search visibility ≠ availability. "I didn't see it in results" is not proof a name is free. Verify with authoritative sources before casting.

Workflow — the casting call

Run these stages in order. Stages 3a and 3b run together.

1. Brief. Establish: (a) what is being named (product / app / company / feature / benchmark), (b) scope + one-line description, (c) the adjacent domain — the vertical it lives in (healthcare, coaching, privacy/security, dev tooling); the user supplies this, (d) tone / vibe, (e) which TLDs matter (default .com .org .ai .io .app .co). If (a)–(c) is missing, ask first — the adjacent domain is what makes collision research meaningful.
2. The audition. Generate 4–8 candidate names matching the tone. Favor short, pronounceable, ownable coinages over descriptive compounds. Note for each what it means / why it fits.
3. The screen test (run 3a and 3b together):
   • 3a — Domains (authoritative). Run scripts/check_domains.sh NAME [NAME ...] -- com ai io ... for a name × TLD availability table. WHOIS no-match + no NS = registrable; Creation Date / Registrar / NS present = taken; ambiguous = verify by hand. Authoritative for registration, never for trademark.
   • 3b — Collision research. For each candidate, use the firecrawl skill or web search (never beautifulsoup) to check the sources below.
4. Callbacks. Build a per-candidate risk table and rank by safety + ownability.
5. Casting report. Use the present skill to build an interactive HTML deck — one slide per finalist plus a ranked comparison and a "cast it?" slide.
6. Branding (optional, gated). Only if the user wants it: draft a wordmark/logo per finalist with nano-banana or gpt-image-2 (draft quality), embed in the slides.
7. Cast → user decides. Give a clear top pick with reasoning; the user makes the final call. Names that fail "didn't make the cut" — never "killed".