maestro:implement

Automating development task execution with AI agents is done by passing approved plans to the Skill, which then writes code, runs tests, and commits changes. It supports single-agent, parallel, or team execution modes.

Yes, you can implement planned features following TDD workflows. The Skill adheres to TDD or ship-fast methodologies during AI-driven code generation, ensuring proper test coverage and code quality throughout task execution.

Parallel task execution for AI-driven code implementation works by running multiple tasks concurrently based on plan dependencies. It manages context loading, status updates, and failure recovery across single-agent, parallel, or team modes.

Yes, you need the Maestro CLI to run AI code generation tasks. The Skill requires integration with Maestro CLI and potentially BR/BV modules for proper state management during task execution and failure recovery.

When an AI agent fails during task execution, the Skill manages failure recovery through its integrated state management system. It handles status updates and context loading to resume or retry the failed development tasks.

The best way to transform abstract development plans into concrete code is using an AI-driven implementation workflow. This approach automates task execution, managing dependencies and context loading to accelerate the development cycle.