Wordsmith

Wordlist Smith

Generate smart, size-controlled wordlists and clean existing ones.

Core Principle: Smart, Not Mechanical

Never generate dumb sequential permutations (00000, 00001, 00002...). That is what crunch does; this skill exists to do better. Generate words ranked by real-world likelihood:

• Semantic expansion: from seed words, generate related words in the same domain (teams, names, places, slang, brands, cultural terms).
• Locale awareness: for Spanish/Mexican targets, include ñ/á/é variants, common name patterns, LADA/city terms, fútbol clubs, date formats (DDMMYYYY), typical combos (chivas2024, tijuana123).
• Ranked output: most probable words first, not alphabetical.

Common Human Password Habits (apply as mutations)

These are what real people do — use them as the mutation vocabulary:

1. Leetspeak swaps: a→4/@, e→3, i→1/!, o→0, s→$/5, t→7, b→8, g→9
2. Suffix padding: 123, 1234, !, !!, 01, birth year, current year
3. First-letter capitalization only: Password (the lazy complexity trick)
4. Name + year: juan1995, maria2024
5. Word + word combos: perro_negro, chivasgana
6. Keyboard walks: qwerty, asdf, 1qaz2wsx
7. Reversed words: drowssap
8. Doubled words: abcabc, mexicomexico

Apply mutations selectively to likely candidates — never explode every mutation across every word (that recreates the dumb-permutation problem with extra steps).

User-Specified Substitutions

If the user asks to replace specific letters with numbers/chars (e.g. "replace all e with 3"), use the cleaner's deterministic swap instead of hand-editing lines:

# keep originals AND emit swapped variants
python3 scripts/clean_wordlist.py INPUT -o OUTPUT --swap a=4,e=3

# emit ONLY swapped variants
python3 scripts/clean_wordlist.py INPUT -o OUTPUT --swap-all a=4,e=3

Swaps are case-insensitive (a=4 also rewrites A). Always pass the user's exact mapping — do not substitute the default leet table unless the user just says "leetspeak it" without specifics.

Input Modes

1. Natural language: "top 20 most used Mexican passwords, expand to 1000 similar words" → generate seeds, expand semantically, cap size.
2. Seed file: user provides a .txt → clean it first (below), then expand if asked.
3. SecLists: user points at a SecLists path (Kali: /usr/share/seclists/) → read, clean, expand/combine as requested.
4. Raw pasted text: user pastes content copied from a website/article (numbered rankings, comma-separated blobs, words in prose). Save the paste to a temp .txt, then:
   • Numbered/listed format (1., 3), - ) → run the cleaner as-is; it strips ranking prefixes and surrounding quotes/punctuation automatically.
   • Comma/space-separated blob → run the cleaner with --split.
   • Words embedded in full article prose → extract the candidate words yourself first, write them to a .txt, then clean.

Size Control (Hard Rule)

The user's requested count is a hard cap. "Make 1000 words" = exactly 1000, never 1001. If expansion would exceed the cap, keep the highest-likelihood words. If it falls short, say so and ask before padding.

Cleaning Existing Lists

Always run the bundled cleaner on any user-fed .txt before processing:

python3 scripts/clean_wordlist.py INPUT -o OUTPUT [--max N] [--min-len N] [--max-len N]

It handles:

• Encoding: detects and converts UTF-8 BOM, UTF-16, cp1252, latin-1 → clean UTF-8 output
• Dedupe: exact duplicates removed, original order preserved
• Ghost characters: zero-width (U+200B/FEFF), control chars, stray spaces, non-printables stripped
• Blanks/length filters: empty lines dropped; optional min/max length

Check the stderr report (kept=X dups=Y) and relay it to the user in one line.

Output

• Plain .txt, one word per line, UTF-8, no BOM, trailing newline.
• Default save location: /home/kali/Desktop/wordlists/ on Kali (create it with mkdir -p if missing). If the user names a different location for a specific request, that overrides the default.
• Default filename from the task (e.g. mexico_top1000.txt).
• Report in one line: total words, cap applied, save path. No filler.