deepen-plan

To add implementation details to a feature plan, you can use parallel research agents to automatically enrich each section with technical approaches, code examples, and performance optimizations, making it production-ready.

Automating research for plan enhancement involves spawning dedicated agents for each plan section that run concurrently, discovering available skills, and synthesizing findings into actionable recommendations for maximum coverage.

Agent orchestration for plan enhancement works by parsing the plan structure, spawning a dedicated research agent for each section, and running all available review agents concurrently to synthesize research findings into the original document.

Yes, you can integrate past learnings by incorporating solutions from previously documented problems and discovering relevant existing skills to apply directly to the plan content, enriching it with proven approaches.

Parallel research for plan enhancement requires no external dependencies, but it needs a valid plan file to parse, serving as the structural input for the concurrent research agents to add depth and implementation specifics.

You should use parallel agents to deepen a plan when you have a high-level feature plan that lacks depth and needs concrete technical approaches, security considerations, and performance optimizations added rapidly across all sections.