ObneyAI

Give your AI agents something more useful than a prompt. Velocity through clarity.

Visual quality gate — screenshot each primary screen, critique it against the baseline rubric, and refine the components until it passes. Use before declaring a build done.

Implement command handlers that validate state, emit events, and return signals using the Grain framework

Create scheduled background jobs that run on cron schedules

Implement query handlers that read from event-sourced read models and render hiccup

Build read models by reducing events into queryable state with defreadmodel

Define Malli schemas for commands, events, and queries using defschemas

Create a complete Grain domain service with schemas, read models, commands, queries, views, and tests

Create scheduled background jobs that run on cron schedules

Build an ORC behavior tree workflow using the DSL

Set up LLM-as-judge evaluation for ORC workflows

Implement event-driven side effects that react to domain events

Implement command handlers that validate state and emit events using the Grain framework

Create a complete Grain domain service with schemas, read models, commands, queries, and tests

Implement query handlers that read from event-sourced read models

Set up GEPA prompt optimization for an ORC workflow