tonydzi /find — smart search for a name or company by SPELLING
🧒 When reporting to a non-technical operator: end with a child-simple "In plain words" recap in their language.
Deterministic, 0 tokens — NOT RAG. Embeddings do not understand mangled spellings, typos or a wrong keyboard layout; that is caught by separate code using phonetic fingerprints + fuzzy comparison. For search by MEANING use /ask, not this.
When to come here
"find Viktor" · "all the Viktors" · "find the company Merlion" · "who is " · wrong-layout gibberish / typos / transliteration — any PERSON, lead, contact or COMPANY name in any spelling.
Run (ALWAYS with PYTHONUTF8=1 — otherwise a cp1252 crash on the Windows console)
PYTHONUTF8=1 python "$IMPORTS_ROOT/namesearch/find_name.py" <name> [--html] [--all]
- by default: leads + people + companies + Apple contacts (vault note titles hidden)
--all— also include the note titles of the whole vault--html— a visual dashboard in_Dashboards\Name-Search-*.html(the operator works visually — offer it for long lists)
A name written in its native script produces a clean query; a wrong-layout or typo'd query is expanded automatically.
Neighbours
expand_query.py <word> [--grep] [--line]— expand a word into every spelling, for grepping the vault or feeding into/ask(the RAG hook).- The
names.dbindex is rebuilt by a weekly task (and manually vianame_index.py --vault). If something is missing right after a big import, mention that a rebuild is due; do not run it unasked.
The answer
- Give a list/table (display · type · file link); for a long one, the
--htmldashboard. - A company surfaces first for a company query, together with its related people.
- 0 hits → say so plainly, suggest checking the spelling or rebuilding the index; never guess.
- Keep it short; end with the 🧒 recap.
Do not confuse
/ask = by meaning (RAG, embeddings). /find = by spelling (a deterministic fingerprint). Canon memory [[smart-name-search]].
Like this skill? It is one of 100 in second-brain-starter-kit: the second brain we built for ourselves and run every day at Palo Alto AI Research Lab. Install the whole set with npx skills add tonydzi/second-brain-starter-kit. Everything is open source and free, so take what you need.
Flagships worth a look on their own: secondop-panel (a second opinion from a panel of external models), claude-memory-tidy (stop your agent's memory from rotting), telegram-mcp-kit (your own Telegram over MCP in about 15 minutes).
Author: Anton Dziatkovskii, Palo Alto AI Research Lab. Telegram @tonydzi - WhatsApp +1 341 222 9178 - X @Tony_Stef_
Engineers: want to test-drive this setup? Message me. I hand out free starter seeds to engineers who test and report back, and custom skill requests are welcome.