A developer built Good To Go (gtg) to solve a common AI agent problem: knowing when a pull request is truly ready to merge. The tool aggregates CI status, classifies review comments, and tracks thread resolution in one command. It's MIT licensed and written in pure Python, designed for daily use in agent orchestration systems.

The core issue stems from AI agents like Claude Code endlessly polling CI, missing actionable feedback buried in automated suggestions, or declaring victory prematurely. Without deterministic answers, agents either burn tokens looping or give up too early. Good To Go provides a single, unambiguous status—READY, ACTION_REQUIRED, or CI_FAILING—to end the guesswork.

Good To Go parses patterns from popular automated reviewers like CodeRabbit and Greptile, distinguishing critical bugs from minor nitpicks. It returns structured JSON for agents or human-readable text, with semantic exit codes. The tool is positioned as a CI gate or agent workflow component, aiming to embed determinism into the development lifecycle.