The latest release of rover-001/checkmate is v1.0.0. This project is a goal-achievement mode that helps AI coding agents grasp the user's completion conditions first and minimize the necessary work. The repository description and guidance documents state that they provide instructions and per-tool settings. However, the available materials do not separately list what changed in v1.0.0.
The key point is how the rules are delivered to the agent. It is a plugin for Claude Code, and it explains that because rules do not trigger reliably with SKILL.md alone, injecting them through a SessionStart hook is important. It emphasizes checking completion conditions, choosing the minimal path, saving tokens, and validating against requirements. It deploys common rules as configuration files for multiple agent environments, and also guides Node.js-based installation and integration. It is a JavaScript project released under the MIT license.
The repository reports that in a benchmark over its own task set, the pass rate rose from 86.7% to 98.3% and output tokens were cut in half. This is an evaluation result presented by the repository itself, not an independently verified result. Therefore, a rule structure that puts goals and verification first can be considered a practical approach, but whether the reported performance is reproducible across other tasks or environments cannot be judged from this material alone.