Find short, pronounceable names for brands, products, and projects.

wordloom is a CLI for exploring names that feel like they could be real words. It follows letter patterns learned from 100k+ English words, then checks generated candidates against WordNet so real dictionary words can show their meanings inline.

Some names generated by wordloom:

zynda · aurel · auren · noack · nobel

Explore a sound or shape you like:

npx wordloom --prefix no
npx wordloom --prefix aure
npx wordloom --length 5 --suffix da

Use a sound, fragment, beginning, or ending you already like and keep narrowing until the results fit.

• Startups and brands — explore memorable names around a sound you like
• Apps and products — discover short names that feel intentional
• CLI tools and libraries — find names developers can remember and type
• Side projects — brainstorm without starting from a blank page
• Creative writing — generate fictional places, companies, or technologies

Run without installing:

npx wordloom

Or install globally:

npm install -g wordloom
wordloom --help

Default length is 5. Supported lengths are 2 through 8.

wordloom --prefix no                       # names starting with "no"
wordloom --suffix ut                       # names ending in "ut"
wordloom --contains bel                    # names containing "bel"
wordloom --length 5 --prefix z --suffix da # combine length, prefix, and suffix
wordloom --length 5 --prefix no --suffix el
wordloom --length 6 --prefix absent        # dictionary match with a meaning

For example, the exact absent match includes its WordNet meaning:

┌───┬────────┬──────────────────────────────────────────────┐
│   │ name   │ meaning                                      │
├───┼────────┼──────────────────────────────────────────────┤
│ 1 │ absent │ verb: go away or leave; adjective: not      │
│   │        │ being in a specified place                   │
└───┴────────┴──────────────────────────────────────────────┘

Broad queries can return a lot of results because wordloom enumerates every matching candidate. Add more constraints to narrow the output, or pipe it through standard shell tools such as less.

• Pronounceable, not random — names follow real English letter transitions derived from CMUdict
• Built-in meaning check — dictionary matches show their WordNet definitions inline
• Precise filtering — combine exact length, prefix, suffix, and substring constraints
• Fast and offline — the language model ships with the package, with no API calls or API keys
• Terminal-native — clean table output with dictionary matches highlighted in interactive terminals

-l, --length <number>         Exact name length to generate (2-8, default: 5)
-c, --contains <text>         Literal substring to require anywhere in the name
-p, --prefix <prefix>         Literal starting prefix to validate and continue from
-s, --suffix <suffix>         Literal ending suffix to require
-h, --help                    Show help
-v, --version                 Show version

All text filters accept letters only and can be combined. If no candidate satisfies the constraints, wordloom prints No results found.

wordloom learns which letters naturally follow each other in English by analyzing 100k+ words from CMUdict. Generation follows observed letter transitions rather than choosing characters independently, which is why candidates feel more word-like than random strings.

Each result is checked against WordNet. If a candidate is also a dictionary word, its meaning is shown inline.

The pre-built model and dictionary data ship with the package, so everything runs locally.

wordloom generates naming candidates. It does not check domains, trademarks, company registrations, usernames, or package-name availability. Do the appropriate availability and trademark checks before choosing a name for a real product or business.

Regenerating the model is only needed when refreshing the checked-in data sources:

bun install
bun run derive:model
bun run build
bun test
bun run lint
bun run format:check

Generated data lives in bin/cmudict-model.ts and bin/wordnet-definitions.ts.

MIT