Brand name research

Brand Name Research

Turn a multi-hour, repetitive naming slog into one guided pass. The golden rule: never raise hopes on a name that won't survive. Only fully-screened, PASSING names are shown — taken ones are filtered out silently (with a count of how many died).

Operating principle: passive checks only (no front-running)

Domain "search" boxes on registrars (GoDaddy, Namecheap, etc.) and their availability APIs can log your query and get the domain front-run (someone registers it before you do). This skill NEVER touches those. It only uses:

• dig / whois (DNS + registry lookups — read-only, no "search intent" leaked)
• Public read APIs: iTunes Search API (App Store), npm registry, PyPI, GitHub profile pages
• WebSearch / WebFetch for Play Store, trademarks, existing companies, social handles

When the user picks a winner, tell them to register the .com (and chosen TLDs) immediately through a real registrar — don't sit on it.

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Phase 0 — Inputs

If --check "name1,name2" is passed: skip Phases 1–2, go straight to screening those. If --from-readme <path> (or a */README.md is obviously relevant): read it to seed the grill, then still confirm the gaps below.

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Phase 1 — Grill me (the interview)

Use AskUserQuestion. Keep it to 2 batches. Skip anything already known from a README.

Batch A — the product & the vibe

1. What is it, in one sentence? (who it's for + what it does) — prefill from README if given.
2. Market / category? (e.g. dev tools, hockey analytics, fintech, consumer notes app) — this defines "same-market collisions" to reject.
3. Name style (multi-select): real word · coined/invented · compound (two words) · misspelling/respelling · metaphor/evocative · short & abstract · person/place · acronym.
4. Vibe / adjectives — 3–5 words it should feel like (e.g. "fast, sharp, technical" or "warm, playful, human"). Names you already like (any field) to triangulate taste.

Batch B — hard constraints 5. Desired TLDs, in priority order (e.g. .com required, then .io, .dev, .ai). Note if .com is mandatory or just preferred. 6. Length / syllables ceiling (e.g. ≤ 7 letters, ≤ 2 syllables, "must be typeable"). 7. Must include / must avoid — letters, sounds, words, themes. Languages to avoid bad connotations in. Competitors whose names it must NOT resemble. 8. Channels that must be clean (multi-select): domain · Apple App Store · Google Play · US trademark · GitHub org · npm · PyPI · X/Twitter handle · Instagram handle. (Only these are treated as PASS-blocking; others are "nice to have, reported.")

Echo back a 4–6 line naming brief and get a thumbs-up before generating.

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Phase 2 — Generate candidates

Brainstorm 40–60 candidates matching the brief. Use varied techniques so the pool is diverse, not 50 variations of one root:

• Real words & evocative metaphors from the product's domain.
• Coined words (blend two roots; add suffixes -ly, -ify, -io, -ory, -al; drop vowels).
• Compounds (Adjective+Noun, Noun+Noun) and clipped compounds.
• Greek/Latin/other-language roots tied to the concept (sanity-check meaning).
• Sound-symbolism matching the vibe (plosives = sharp/fast; liquids/nasals = smooth/calm).

Internally note each candidate's style + why it fits. Do not show the raw 60 yet — they haven't been screened.

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Phase 3 — Screen (passive, batched)

ORDER MATTERS: go BROAD before NARROW. The #1 failure mode of this skill is trusting the App Store API + a market-qualified search and declaring a name "clean" — then a plain Google of the bare word instantly surfaces a Google-Play/international app, an apparel label, a crypto token, or a supplement vendor on the exact name. Always run the broad gut check (3a) FIRST and let it kill names before you spend lookups on anything else.

Phase 3a — BROAD GUT CHECK (mandatory, runs first, no market qualifier)

For EVERY surviving candidate, before any other check:

1. Bare-word web search — WebSearch "<name>" and WebSearch "<name> app".
   • Do NOT append your market keyword here ("fitness", "hockey", etc.). Broad first — you're looking for anyone at all on the exact string. A market qualifier hides collisions in adjacent categories (apparel, crypto, supplements, gaming, music).
   • Read the top ~10–15 results and catalog every exact-spelling entity in ANY category: apps (iOS and Android and web, any country), companies/startups, clothing/merch brands, crypto tokens, supplement/peptide vendors, bands/musicians, gamers/streamers, products. Note what each is + how active/prominent.
2. App stores, both, explicitly — WebSearch "<name> site:play.google.com" AND WebSearch "<name> site:apps.apple.com". The iTunes API in the script misses Google Play and many international iOS apps — this catches them.
3. Socials sweep (always run, not just if selected) — WebFetch each for 404-vs-profile: x.com/<name>, instagram.com/<name>, tiktok.com/@<name>, youtube.com/@<name>, twitch.tv/<name>, github.com/<name>. Report which exact handles are taken and by whom.

Reject in 3a (hard FAIL) if any of: an exact-name app exists on any store; a prominent global brand owns the word; any entity uses the exact name in or adjacent to the product's category (for a fitness app that includes activewear, supplements, wearables, sports gear, gyms, athletes); or the name is so widely used that it's effectively un-ownable. A few tiny unrelated entities in far-off categories are acceptable but must be reported, not hidden — the user decides.

Only names that survive 3a proceed to 3b.

Phase 3b — Structured checks (only on 3a survivors)

Run the bundled screener:

scripts/check-name.sh "<candidate>" --tlds <com,io,dev,...>

It reports per-candidate: domain status per TLD, Apple App Store match, GitHub/npm/PyPI, and a final VERDICT: PASS|FAIL. Run candidates in batches (independent — fire several Bash calls in parallel). Then:

• Existing company in the same market — NOW you may add the market keyword: WebSearch "<name> <market keyword>" → a real player in the category = reject.
• US trademark (if selected): WebSearch "<name> trademark" or check tmsearch.uspto.gov; flag live marks in related classes. (Advisory, not legal advice.)
• Domains/handles: confirm the specific TLDs and the exact handles you'd actually use.

A candidate PASSES only if (a) it cleared the broad gut check 3a, and (b) every channel the user marked PASS-blocking (Phase 1 Q8) is clear. Keep a tally: screened N → M passed, and note why each died (broad-search collision / app / domain / company / trademark).

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Phase 4 — Deliver only survivors

Aim to present 8–15 PASSING names (generate another batch and re-screen if too few). Rank by fit to the brief (vibe + style + length + how clean across channels).

Output a table — every row is a name that already survived screening (incl. the 3a broad gut check):

Name	Style	Why it fits	Broad web (any exact-name entity?)	.com	other TLDs	App stores (iOS+Play)	TM signal	Handles
Saber	real word	sharp, fast, technical	only a tiny unrelated EU firm	✅ free	.io ✅ .dev ✅	clear both	none seen	@saber taken→@saberhq ✅

Then:

• Top 3 picks with a one-line rationale each.
• Footer: "Screened {N} candidates, {N−M} eliminated (apps/domains/companies)."
• Action: "Register the .com + chosen TLDs now before sharing the name anywhere — searches elsewhere can tip off squatters."

Offer to: save the report to the product folder (Write a NAMING.md next to the README), run another round with a tweaked brief, or deep-dive trademark on a finalist.

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Notes & limits

• DNS-clear is a strong but not 100% signal a domain is unregistered (rarely, a registered domain has no DNS); the screener confirms with whois. Conservative by design — it would rather call a free name "registered" than the reverse.
• Trademark output is a signal, not legal clearance. For anything you'll build a business on, do a proper search / consult counsel before filing.
• This skill checks availability and collisions; it does not register anything.

Judgment weave (see /judgment)

• Before delivering the winner: names are /door territory — one-way once shipped. Enumerate the lock-in (domains bought, handles claimed, SEO) before the user commits.