AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
great_cto는 Claude Code와 Codex 기반 파이프라인으로 코딩 에이전트를 실행합니다
great_cto runs your coding agent as a pipeline — the full pipeline on Claude Code, skills and an MCP server on Codex, built for Claude Fable 5.1 and OpenAI Astra 6 — architecture, data model, backend,
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You already have the agent. This is everything around it. great_cto runs Claude Code as a pipeline of 70 specialist agents — an independent model checks each stage before the next builds on it, spending caps refuse rather than warn, and three decisions stay yours: what gets built, how, and whether it ships.
저장소 README에서 가져옴
README 전체를 불러오는 중입니다. 아래는 저장된 발췌입니다.
Ship products with the coding agent you already have.
Your coding agent ships code. This is what checks it.
You write a spec. Your Claude Code builds against it, a second model from
another family reads the same diff, and where they disagree you see the
disagreement and decide. Three decisions stay yours — what gets built, how, and
whether it ships. What lands is a repository you own and a URL that works.
Seven products built this way in the open benchmark: median $171 in tokens,
measured 2026-07-10. You pay your own LLM provider; great_cto is MIT and bills nothing.README 전문 보기 ↗지금 README 전체를 불러올 수 없습니다. 원문 링크에서 읽을 수 있습니다. 아래는 저장된 발췌입니다.
Ship products with the coding agent you already have.
Your coding agent ships code. This is what checks it.
You write a spec. Your Claude Code builds against it, a second model from
another family reads the same diff, and where they disagree you see the
disagreement and decide. Three decisions stay yours — what gets built, how, and
whether it ships. What lands is a repository you own and a URL that works.
Seven products built this way in the open benchmark: median $171 in tokens,
measured 2026-07-10. You pay your own LLM provider; great_cto is MIT and bills nothing.README 전문 보기 ↗