breakdown-feature-implementation

To break down a feature, you decompose high-level requirements into granular tasks across technical layers like DB, Backend, Frontend, Infra, and Test, ensuring clarity and efficient execution.

Layered task decomposition is the process of splitting features by technical layers, defining each task with purpose, implementation content, acceptance criteria, and persona hints in a structured format.

You map dependencies using explicit `blocked_by` mapping within a structured YAML format to prevent file conflicts and ensure logical task sequencing for parallel execution.

Yes, you can assign appropriate AI personas such as implementer, researcher, architect, and QA based on the specific nature and technical layer of each generated implementation task.

The best way to structure implementation tasks is using a structured YAML format that defines task purpose, implementation details, acceptance criteria, and inter-task dependencies for workflow automation.

Yes, this method works for full-stack features by generating tasks for database schema changes, API endpoint creation, frontend form development, and associated test cases with clear instructions.