subagent-driven-development

Automate code generation by dispatching independent AI subagents for implementation and multi-stage reviews. This orchestrates specialized agents to write code, verify spec compliance, and check quality iteratively until tasks are approved.

Multi-stage code review works by dispatching separate AI subagents for spec review and code quality review. The system manages iterative feedback loops between implementer and reviewer agents to fix issues until all tasks are complete and approved.

The best way to break down an implementation plan is using task decomposition via subagent orchestration. This automates the execution of discrete development tasks within a single session, managing statuses like DONE, NEEDS_CONTEXT, and BLOCKED.

Handle blocked tasks or missing context through automated status management that tracks NEEDS_CONTEXT and BLOCKED states. The system escalates issues as needed, managing iterative feedback loops between implementer and reviewer agents to resolve concerns.

Yes, you can use task automation for iterative code refinement. The system dispatches specialized AI agents to execute implementation, review code quality, and iterate through feedback loops, ensuring spec compliance and managing task statuses like DONE_WITH_CONCERNS.

Yes, you need a detailed implementation plan to start subagent-driven development. The skill takes a structured plan, breaks it into discrete tasks, and orchestrates AI agents to execute the code generation and multi-stage review process within a single session.