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An AI skill that researches your ex-USSR family history, with proof

This is a skill for AI coding agents like Claude Code or Codex that turns them into careful, evidence-first genealogists for the former USSR and Russian Empire, marking clearly what is proven and what is only a guess.

The AI agent reads archival documents — church books, civil records, military and repression files — and separates what's in the document from its own assumptions.

When it helps

Useful once you already have documents or leads about an ancestor in the former USSR and want a rigorous analysis; it won't search the archives for you.

Pros

  • Sorts findings into five clear statuses: hypothesis, single source, multiple, contradicted, unresolved
  • Copies of one document never count as two independent sources
  • Keeps people with the same name separate without clear proof
  • Covers all 15 former Soviet republics using historical borders, not today's

Cons

  • Won't search archives for you — that part is still your work
  • Final PDF report needs Chrome, Chromium, or Edge installed locally

How to set it up — step by step

  1. 1Open a terminal on the computer where Claude Code or Codex is installed.
  2. 2Run git clone https://github.com/DenisSergeevitch/ex-ussr-kinship-research ~/.claude/skills/ex-ussr-kinship-research (for Codex, use ~/.codex/skills/ instead).
  3. 3Confirm Python 3 is already on your machine — no other software needed.
  4. 4Send the agent the text from the block below describing your case.
  5. 5Check which status the agent gave the finding and which sources it cited.
  6. 6Install Chrome, Chromium, or Edge if you want a finished PDF report.

Text for your agent

Copy this and send it to your agent — Claude Code, Codex, any of them:

Have your agent install the ex-ussr-kinship-research skill from https://github.com/DenisSergeevitch/ex-ussr-kinship-research and describe a concrete case - a great-grandfather exiled in the 1930s, or a 19th-century parish in Podolia - then show me what confirmation status the agent gave the finding and which sources it quoted.

Runs on a regular laptop - it's a set of instructions for the agent, no separate program needed, plain Python 3 is enough. The exception is the final PDF-report script, which needs Chrome, Chromium or Edge on the same machine.

Open on GitHub