subagent-driven-development

You can automate iterative software development by dispatching independent AI subagents for each task within an implementation plan, managing a two-stage review process for spec compliance and code quality. This facilitates rapid iteration and high-quality code generation.

The two-stage code review process first checks spec compliance to ensure the code meets requirements, then evaluates code quality. AI subagents manage feedback loops to fix identified issues, ensuring high-quality code generation through iterative refinement.

Yes, the subagent-driven development approach supports TDD alongside task automation. It orchestrates independent AI subagents that implement tasks iteratively, integrating with Git worktrees to maintain isolated development environments during testing.

This Skill automatically decomposes a development plan into manageable, independent tasks, assigning a fresh AI subagent to each task for focused implementation. It manages the entire process from initial coding to final review.

Yes, the iterative development approach integrates with Git worktrees to provide isolated development environments. Each AI subagent works independently on its assigned task, ensuring clean separation while the system orchestrates the overall implementation plan.

The approach requires a pre-defined implementation plan as input and depends on AI subagents to handle each task independently. Complex, tightly-coupled tasks may challenge the independent dispatch model, and the two-stage review process needs clear specs to function effectively.