domain-name-brainstormer

Domain Name Brainstormer

Generate and score candidate brand / domain names from seed words using common naming patterns. The script does not check live registration — it produces candidates fast so you can spend your time evaluating the best ones.

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Table of Contents

• [Keywords](#keywords)
• [Quick Start](#quick-start)
• [Core Workflows](#core-workflows)
• [Tools](#tools)
• [Reference Guides](#reference-guides)
• [Best Practices](#best-practices)

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Keywords

domain, domain name, naming, brand naming, product name, company name, .com, brainstorm, naming brainstorm, brand identity, naming strategy

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Clarify First

Before generating names, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• [ ] Seed words — 3-7 words describing the product, value, or feeling; every candidate is built from these
• [ ] Brand tone — serious/technical vs playful/coined steers which patterns (blend, vowel-drop, prefix-suffix) to favor
• [ ] TLD preference — .com vs .ai/.io/.co changes the shortlist and whether the tld_suffix pattern applies

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

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Quick Start

Generate 200 Candidates in 30 Seconds

python scripts/name_generator.py "data,insight,signal" --count 200

Then:

1. Eliminate anything > 12 characters
2. Eliminate anything that's hard to spell after hearing it once
3. Eliminate anything that sounds like a competitor
4. Take the top 10-15 to a registrar (manually) to check availability across .com / .ai / .io / .co

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Core Workflows

Workflow 1: Seed-Word Brainstorm

Goal: Convert a few keywords describing the product into 100+ ranked candidates.

Steps:

1. List 3-7 seed words that describe the product, value, or feeling
2. Run: python scripts/name_generator.py "seed1,seed2,seed3" --count 200
3. Sort the output by score (highest first)
4. Apply the elimination filter from references/naming_framework.md
5. Pick a shortlist of 10-15 to manually check for trademark and domain availability

Expected Output: Ranked list of candidates with scores, classified by pattern (vowel-drop, blend, prefix-suffix, TLD-as-suffix).

Time Estimate: 5-10 minutes.

Workflow 2: Pattern-Specific Generation

Goal: Get more of one specific pattern (e.g., only blends, or only TLD-as-suffix names).

Steps:

1. Run with pattern filter: python scripts/name_generator.py "fast,ship" --pattern blend --count 100
2. Available patterns: vowel_drop, prefix_suffix, blend, tld_suffix, repeat, all
3. Iterate seeds until you have 20+ candidates worth taking to availability checks

Expected Output: Pattern-specific list.

Time Estimate: 5 minutes per pattern variation.

Workflow 3: Trademark / Availability Pre-Check

Goal: Avoid wasting energy on names that are obviously taken.

Steps:

1. Take the shortlist from Workflow 1 or 2
2. Manually check each on:

• A domain registrar (Namecheap, Cloudflare, Porkbun) for .com / .ai / .io / .co
• The USPTO TESS database (or your jurisdiction's trademark office) for live trademarks in the relevant class
• A regular Google search for existing usage

1. Drop anything with a live trademark in the same product class, an active product on a similar domain, or a trademarked .com that you do not own

> The script does not automate registrar lookups — those need real network calls and rate-limited APIs. Doing this step manually for a 10-name shortlist takes 10-15 minutes.

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Tools

name_generator.py

Generates candidate names by applying naming patterns to seed words and scores each on length, pronounceability, and uniqueness.

# Default: 100 candidates, all patterns
python scripts/name_generator.py "data,signal,insight"

# More results
python scripts/name_generator.py "data,signal" --count 300

# One pattern only
python scripts/name_generator.py "data,signal" --pattern vowel_drop

# JSON for programmatic use
python scripts/name_generator.py "data,signal" --json

Patterns implemented:

• vowel_drop — Remove inner vowels: "data" → "dta", "insight" → "nsght"
• prefix_suffix — Add common naming prefixes/suffixes: "ly", "ify", "io", "lab", "labs", "hq", "co", "stack", "kit", "app"
• blend — Combine two seeds: "data" + "signal" → "dasignal", "datignal"
• tld_suffix — Treat TLD as part of the name: "send.fast" reads as "sendfast"
• repeat — Doubling pattern: "data" → "datadata"

Score (0-100) factors:

• Length 5-10 chars scores highest
• Pronounceability via consonant-vowel ratio
• Penalty for common-word collisions
• Penalty for hyphens or numbers (these dilute brand)

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Reference Guides

• references/naming_framework.md — Why names matter, the elimination filter, naming-pattern playbook, common pitfalls

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Best Practices

• Don't pre-commit to .com. A .io or .ai is fine for most B2B products in 2026; .com matters less than it did a decade ago.
• Say it out loud. If you can't tell someone the domain in a noisy bar and have them spell it correctly, drop it.
• Avoid naming-collisions. A "DataLoop" in your space and a "DataLoop" in adjacent SaaS will cause confusion forever.
• Don't pick the first one. Generate 200, filter to 30, shortlist 10, sit on the shortlist for 24 hours. The one that still feels right after sleeping is the one.
• Trademark before launching. A great name with a trademark conflict will cost you a rebrand later.

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Integration Points

• Pairs with marketing/brand-strategist/ for brand-narrative work
• Pairs with marketing/landing-page-generator/ for messaging once a name is chosen
• Used by c-level-advisor/ workflows during company / product launches