Agent-Skills.md

Agent Skills: Domain Name Brainstormer

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personal-productivityID: borghei/claude-skills/domain-name-brainstormer

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borghei/claude-skills
borgheiLicense: NOASSERTION
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Skill Metadata

Name
domain-name-brainstormer
Description
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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.

Table of Contents

Keywords

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

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.

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

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
  3. 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.

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)

Reference Guides

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

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.

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