자동 연구 계보: AI 지원 계보 연구를 위한 구조화된 프롬프트

Structured prompts, vault templates, and research workflows for AI-assisted genealogy research. Built for Claude Code, adaptable to any AI tool or manual workflow. This project extracts and generalizes methods developed during a real genealogy research effort that produced 105 files spanning 9 generations across 6 family lines, using Claude Code's autonomous research capabilities. - Genealogy researchers who want to use AI to accelerate their family history work without sacrificing source rigor - AI/tech enthusiasts who want a concrete example of autonomous research loops applied to a humanities domain - Anyone who has a box of old photos, a DNA test, and unanswered questions about their family - Clone this repo - Copy the vault-template/ folder into your Obsidian vault (or any markdown editor) - Fill in Family_Tree.md with what you already know (start with yourself, work backward) - Scan any physical documents you have (certificates, photos, letters) - Open Claude Code, paste the contents of prompts/01-tree-expansion.md , and run it - Review the results, then run prompts/02-cross-reference-audit.md to verify See workflows/getting-started.md for the full walkthrough. 12 autoresearch prompts designed for Claude Code's /autoresearch command. Each defines a Goal, Metric, Direction, Verify condition, Guard rails, Iterations, and Protocol. They run autonomously: searching the web, updating your vault, and verifying their own work. | Prompt | Purpose | |---|---| | 01-tree-expansion | Push every branch as far back as possible using web research | | 02-cross-reference-audit | Find and fix discrepancies between your tree and source documents | | 03-findagrave-sweep | Locate Find a Grave memorials for every deceased ancestor | | 04-gedcom-completeness | Ensure your GEDCOM file matches your vault data | | 05-source-citation-audit | Verify every person file cites at least two independent sources | | 06-unresolved-persons | Identify and resolve unnamed people mentioned in your documents | | 07-timeline-gap-analysis | Find life events where records should exist but have not been found | | 08-open-question-resolution | Systematically attack every open research question | | 09-bygdebok-extraction | Extract data from digitized local history books (any country) | | 10-colonial-records-search | Search for colonial American ancestors in pre-1800 records | | 11-immigration-search | Locate passenger manifests and naturalization records | | 12-dna-chromosome-analysis | Analyze per-chromosome ancestry data to map genetic segments | 19 files: a complete Obsidian vault starter kit with YAML frontmatter, plain markdown, readable anywhere. - Core files: Family tree, research log, open questions, data inventory, timeline, genetic profile, chromosome painting, witness network, unresolved persons, research strategy - Templates: Person, transcription, certificate, postcard, region, surname, hypothesis, draft letter 24 country and region-specific guides covering where to find records, what is free vs paid, and what AI tools can access directly vs what requires a browser. Europe: Ireland, England/Wales, Scotland, France, Italy, Spain/Portugal, Germany, Netherlands, Austria, Hungary, Norway, Sweden, Poland, Russia/Ukraine Americas: USA (colonial, immigration, census, vital records), African American, Canada, Mexico/Latin America Oceania: Australia/New Zealand Cross-national: Jewish genealogy 9 methodology documents: confidence tiers, source hierarchy, DNA interpretation guardrails, naming conventions (patronymics, farm names, przydomki), GEDCOM format guide, common pitfalls, glossary, AI capabilities assessment, and the case for autoresearch in genealogy. 7 step-by-step guides: getting started, OCR pipeline, new ancestor intake, document triage, oral history protocol, discrepancy resolution, phase planning. 6 anonymized worked examples showing autoresearch in action: tree expansion session, cross-reference audit, DNA-to-genealogy mapping, name resolution, colonial deep dive. Structured autonomous research with mechanical verification, not AI guessing. Genealogy is different from most AI tasks. There is no compiler. Sources disagree with each other. Confidence is probabilistic, not binary. A name that appears as "Sakkarias" in one record and "Zacharias" in another might both be correct. A date listed as 1820 in one source and 1925 in another is almost certainly wrong somewhere. The autoresearch approach adapts to this by: - Defining measurable metrics (count of sourced claims, count of resolved questions, count of remaining discrepancies) - Requiring verification after every iteration (cross-reference audit, not just accumulation) - Logging negative results (what you searched for and did not find is as important as what you found) - Maintaining confidence tiers (Strong Signal / Moderate Signal / Speculative) rather than treating all claims as equal This is inspired by Andrej Karpathy's autoresearch concept: autonomous goal-directed loops wher