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:

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• Callbacks. Build a per-candidate risk table and rank by safety + ownability.
• 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.

• 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.

• 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".

Stage 3b — collision research checklist

For each candidate, search these surfaces and record URLs:

• SaaS / AI / startups — Crunchbase, Product Hunt, a plain web search of "<name>" + vertical.
• Code namespace — GitHub repos literally named it; PyPI and npm packages with that exact name.
• The adjacent domain (most important) — "<name>" + the user's vertical. A same-vertical hit

is the one that ends an audition.

• Privacy / security tooling — relevant if the thing touches data handling.
• Trademark + ownability — a light USPTO / EUIPO look for live marks in the relevant classes,

plus a judgment call on descriptiveness: distinctive enough to own, or a generic compound a

competitor can crowd?

• Benchmark names — if naming a *benchmark*, the decisive check is the literature, not domains:

is the name already a published dataset/benchmark (arXiv / ACL / Papers with Code)? Citation

clash, not a domain, is what matters there.

Output table:

| Name | Notable existing uses (URLs) | Adjacent-domain clash? | Trademark / ownability | Verdict |

|---|---|---|---|---|

| Acme | github.com/x, acme.io (logistics) | No | Distinctive, no live marks | Callback |

Verdict is Callback (advances) / Cut (out) / Cast (the pick). Apply the Decision rules.

Decision rules

• Adjacent-domain collision → Cut. Even if every domain is free. A competing product in the

same vertical poisons the name.

• Descriptive / generic compound → weak. Domains may be free, but hard to trademark and bad for

SEO. Flag the ownability risk even when registrable.

• Domains-free ≠ safe. Availability is necessary, not sufficient. A name earns the part only

when it is *both* registrable *and* clear of adjacent-domain and trademark collisions.

• Verify before casting. Confirm domains with check_domains.sh and trademark with a registry

lookup, not with "I didn't find anything."

• Rank by safety first, then ownability, then aesthetics.

Example (a real audition)

Naming a privacy-focused de-id toolkit + benchmark for the mental-health / coaching vertical.

Audition: Praxio, Dyad, Sessio, ClientPII, CONFIDE.

| Name | Screen test | Verdict |

|---|---|---|

| Praxio | Sounded great, but Praxis EMR is a mental-health EHR — adjacent-domain collision in the exact vertical. | Cut |

| Dyad | Clean, meaningful, but dyad.sh is a local-AI dev tool and dyad.ai is a healthcare company — collisions in both tech and the vertical. | Cut |

| Sessio | Nice, but sessio.base44.app is a same-vertical product for therapists. | Cut |

| ClientPII | All TLDs free — but a generic descriptive compound, weak to trademark, bad SEO. | Didn't make the cut (as a brand) |

| CONFIDE | Domains all taken (bad product brand) — but as a *benchmark* name, citation-collision is low. | Cast (as the benchmark name) |

One line: domains-free ≠ safe, and brandable ≠ available. Most names that look good fail on

adjacent-domain collisions a domain check alone would never catch.

scripts/check_domains.sh

scripts/check_domains.sh praxio dyad sessio          # default TLDs (.com .org .ai .io .app .co)
scripts/check_domains.sh praxio dyad -- com ai io     # custom TLDs after a --
TLDS="com org ai" scripts/check_domains.sh praxio     # or via env

Per domain it runs whois (following the IANA registry referral when needed) plus dig +short NS,

printing a name × TLD table of free / taken / ?. ? = verify by hand (WHOIS rate-limit or

.ai flakiness). Needs whois and dig on PATH (ship with macOS; apt install whois dnsutils).

Referenced skills

• firecrawl — collision / literature research (stage 3b). Never beautifulsoup.
• present — interactive HTML casting report (stage 5). Pass it the comparison + per-name slides.
• nano-banana or gpt-image-2 — optional draft branding (stage 6). Draft quality by default.

Safety & limits

• WHOIS is authoritative for registration, not trademark. A free domain can still infringe a

live mark. Always do the separate trademark read.

• .ai WHOIS is flaky. Treat ? as "check the registrar's search," not "free."
• The script proves registrability, not legal clearance — no trademarks, social handles, or

app-store conflicts. For a name you'll build a business on, get an attorney's clearance.

• Branding is optional and gated — generate logos only when the user asks; draft quality unless

told otherwise.

• The user casts. The skill recommends; it does not commit.

Install

Portable across Claude Code and Codex — plain-prose workflow, one bash script, no Claude-only tools.

cp -R name-audition ~/.claude/skills/   # Claude Code
cp -R name-audition ~/.agents/skills/   # Codex