AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
JSON·YAML·TOML·XML을 위한 하나의 스키마·데이터 모델, 스키마 변경 안전성을 증명하는 결정 가능한 스키마 대수 제공
One canonical schema and data model for JSON, YAML, TOML, XML — with a decidable schema algebra that proves schema changes are safe instead of guessing. Five independent language ports, one spec.
개발 도구PythonGitHub @omnist-dev온라인운영자 미확인
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Python reference implementation of Omnist: one data model, many formats — JSON, YAML, TOML, XML, and OML.
저장소 README에서 가져옴
README 전체를 불러오는 중입니다. 아래는 저장된 발췌입니다.
Omnist ("omni-structure") is one canonical data model for JSON, YAML,
TOML, XML, and its own native OML (Omnist Markup Language) — read any of
them into a single tree, validate it against a schema, compare schema
versions, and write it back out to any of the others.
Three things you can do with Omnist that are genuinely hard anywhere else:README 전문 보기 ↗지금 README 전체를 불러올 수 없습니다. 원문 링크에서 읽을 수 있습니다. 아래는 저장된 발췌입니다.
Omnist ("omni-structure") is one canonical data model for JSON, YAML,
TOML, XML, and its own native OML (Omnist Markup Language) — read any of
them into a single tree, validate it against a schema, compare schema
versions, and write it back out to any of the others.
Three things you can do with Omnist that are genuinely hard anywhere else:README 전문 보기 ↗