MCP server and agent skill for the TypeSafe AI System One API (Jev): decompose a judgment into Choice / Score / Noul questions, lint them, measure on labelled data, and put calibrated thresholds in co
A repository for using TypeSafe AI (System One API, model Jev) from a coding agent at design time. It has three parts.
Component Location Role --------- Documentation docs/ Map of the official docs to this repo, workflow diagrams and LLM-integration use cases, design doc, runbook MCP server tenbin/ stdio MCP. Question lint, evaluation, batch evaluation (threshold calibration), rerank, taxonomy exploration, cost guard Agent Skill skills/tenbin/ Procedure + templates + offline lint for decomposing a judgment into Choice / Score / Noul, measuring, and putting thresholds in code
Operational procedures (setup, daily work, incident response, maintenance) are in docs/04-runbook.md. Conventions for agents are in AGENTS.md.
Ask your coding agent for a judgment your code needs, and it comes back with a measured design, not a prompt:
You ask What you get Behind it --------- /tenbin / "Where could Tenbin help this project?" Proposals grounded in the current project and conversation: evidence, input and judgment type, integration point, uncertainty handling, and the smallest useful trial skill discovery mode or MCP tenbin prompt; no API call "Design and generate Jev code for this project" Project-specific state/questions, SDK calls, decision logic, failure handling, application wiring and mocked tests skill contextual integration or MCP designintegration; generation needs no API key "Generate state and questions from this context" / "Suggest questions for this input" Matching {state, questions} JSON, field sources, assumptions, type rationale and offline lint; candidates only when s
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