subagent-driven-development

Automating AI development workflows with independent task subagents involves dispatching fresh subagents per task to isolate context and prevent cross-talk. This Skill orchestrates that lifecycle in-session, applying prescribed prompts and templates to manage rapid implementation iterations.

A two-stage code review process for AI-generated code enforces sequential quality gates: spec compliance review verifies the implementation matches the plan, followed by code quality review to catch structural gaps. This ensures fast iteration does not compromise the final output.

The best way to execute an implementation plan in a single AI session is to dispatch an implementer subagent for the first task and let it proceed through an automated review workflow. This supports rapid progress with clear quality checkpoints for largely independent tasks.

Yes, you can use subagent orchestration for TDD and rapid iteration workflows. It automates per-task subagent dispatching and uses built-in review gates to maintain code quality, making it suitable for fast-paced development automation where tasks require quick cycles.

You should avoid using subagent-driven development when tasks are highly dependent on each other or require shared context across steps. The approach relies on fresh subagents isolating context per task, making it unsuitable for tightly coupled implementation plans.