Review the design documentation in docs/ai/design/feature-{name}.md (and the project-level README if relevant).

1. Use Memory for Context — Search memory for prior architecture constraints/patterns: npx ai-devkit@latest memory search --query "<feature design architecture>".
2. Summarize:
   • Architecture overview (ensure mermaid diagram is present and accurate)
   • Key components and their responsibilities
   • Technology choices and rationale
   • Data models and relationships
   • API/interface contracts (inputs, outputs, auth)
   • Major design decisions and trade-offs
   • Non-functional requirements that must be preserved
3. Clarify and explore (loop until converged):
   • Ask clarification questions for every gap, inconsistency, or misalignment between requirements and design. Do not just list issues — actively ask specific questions to resolve them.
   • Brainstorm and explore options — For key architecture decisions, trade-offs, or areas with multiple viable approaches, proactively brainstorm alternatives. Present options with pros/cons and trade-offs. Challenge assumptions and surface creative alternatives.
   • Repeat — Continue looping until the user is satisfied with the chosen approach and no open questions remain.
4. Store Reusable Knowledge — Persist approved design patterns/constraints with npx ai-devkit@latest memory store ... when they will help future work.
5. Next Command Guidance — If requirements gaps are found, return to /review-requirements; if design is sound, continue to /execute-plan.