ais.spec.design

You can convert AI feature specifications into design documentation by running a structured workflow that parses spec documents and generates a design.md file with technical context, verification strategy, and implementation planning.

Project context integration works by informing design decisions with the project constitution, tech stack, and verification requirements, ensuring the generated design artifacts align with existing architecture and constraints.

The best way to plan verification requirements is to use a structured design workflow that generates design artifacts, explicitly mapping out a verification strategy alongside technical context and implementation planning.

Yes, you can handle independent sub-specs during feature design; the workflow supports sub-specs that inherit no parent state, allowing you to manage isolated design artifacts and update spec frontmatter for tracking.

You need research tasks to clarify and justify AI design choices when specifications are ambiguous; the workflow integrates research tasks directly into the design process to support contextual decision making.

You sync design status back to the original feature spec by updating the spec frontmatter with the current design status, enabling accurate tracking and reporting across the project lifecycle.