agentleFS
tonydzitonydzi/second-brain-starter-kit/skills/find/SKILL.md

find

Skill6 starsChanged 2 months agoMITView on GitHub

Find a person, lead, contact or company by name in any spelling: a deterministic search that catches transliteration across alphabets, wrong-keyboard-layout typing and typos. This is the exact-name lane (zero tokens), complementary to semantic vault search. Triggers: "/find <name>", "show everyone named <X>".

6 other projects use a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 5 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Keep it short; end with the 🧒 recap.

    line 36

“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

/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 <name>" · 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.db index is rebuilt by a weekly task (and manually via name_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 --html dashboard.
  • 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]].


<!--kit-footer-->

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.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.