LLM 파이프라인에 대한 통계 문서 인프라. Canonicalization, schema validation, 문법 컴파일 및 MCP 도구 튼튼한 AI artifacts.

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이 README는 3명의 청중들을 봉사합니다. 우리는 그 특이한 것을 알고 있으며, 프로젝트 자체가 모든 3의 교차점에 앉아 있기 때문입니다.

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엔지니어

OCTAVE는 MCP 서버와 CLI를 가진 구조화된 문서 형식입니다. 문서는 단일 운하 형태에 정상화, 자신의 스키마에 대한 검증, 그리고 모든 변환을 기록. 압축, 다중 시약, 감사를 살아야 하는 AI 문서의 인프라입니다.

# 빠른 예 : 변경 사항

프로젝트 상태를 요약하기 위해 LLM을 물어주세요.

또 다른 LLM은 영어를 회복하고 파라페라세스가 될 것입니다. - 조용히 떨어뜨리는 사실, merging details, escalating “priority area” to “vulnerability.” LLM의 버그가 아닙니다.

OCTAVE MCP Server

Deterministic document infrastructure for LLM pipelines. Canonicalization, schema validation, grammar compilation, and MCP tools for durable AI artifacts.

pip install octave-mcp

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This README serves three audiences. We know that’s unusual, and we’re being upfront about it — because the project itself sits at the intersection of all three.

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For Engineers

OCTAVE is a structured document format with an MCP server and CLI. Documents normalise to a single canonical form, validate against their own schema, and log every transformation. It’s infrastructure for AI documents that need to survive compression, multi-agent handoffs, and auditing.

Quick example: what changes

Ask an LLM to summarise a project status and you get prose:

The auth migration has been running for three sprints and keeps hitting
the same problems. There's a security audit in six weeks — the auditors
specifically flagged session management as a priority area. We've burned
through 60% of the quarterly budget on this migration alone.

Ask another LLM to restate that in English and it will paraphrase — silently dropping facts, merging details, escalating “priority area” to “vulnerability.” That’s not a bug in the LLM. It’s what prose invites: retelling.

OCTAVE structures the same information as labelled fields:

migration::auth_service[3_sprints∧SISYPHEAN_FAILURES]
CHRONOS::audit_6wk
  ARTEMIS::session_mgmt_targeted
DEMETER::"60%_quarterly_burned[this_migration_alone]"

Pass that to any LLM — even one that has never seen OCTAVE — and it translates each field separately. ARTEMIS (monitoring/targeting domain) reconstructs as “specifically targeted,” not “vulnerability.” CHRONOS (time pressure) stays distinct from DEMETER (budget). Facts don’t merge because the structure tells the agent “these are labelled data points,” not “this is a narrative to retell.”

The mythology terms (SISYPHEAN, ARTEMIS, CHRONOS, DEMETER) work like semantic zip files — compressed meaning that’s already in the model weights. No training needed. Why that works.

If an LLM writes it slightly wrong (extra spaces, ASCII -> instead of →), the normaliser autocorrects and gives you a receipt: