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You automate complex software engineering tasks by orchestrating a swarm of AI agents that handle research, planning, implementation, and validation phases. This approach uses complexity routing and persistent agent teams to optimize token usage throughout the development lifecycle.

Complexity routing is a mechanism that assigns complexity-appropriate effort to medium and full complexity tasks. It ensures AI agents apply the right level of research and implementation detail by evaluating task requirements before allocating computational resources.

You trigger the task automation workflow to initiate codebase research and feature implementation. The agent team packages context once for shared reading, plans the architecture, and pauses at user review checkpoints before proceeding to inline diffing and validation.

Yes, it is designed for large codebases requiring medium to full complexity task automation. The skill uses context packaging and read-once context sharing to enable persistent expert teammates to understand large architectures without exceeding token limits.

The best way to coordinate parallel research and architecture planning is using persistent agent teams. This method ensures clarity before research begins, shares context efficiently among expert teammates, and threads specifications throughout the development lifecycle.

You should avoid using an AI agent team for trivial tasks that do not require codebase research, architecture planning, or complex implementation workflows. The complexity routing and persistent teammates are optimized specifically for medium to full complexity development tasks.