Guide me through adding a new feature, from requirements documentation to implementation readiness.

1. Capture Requirement — If not already provided, ask for: feature name (kebab-case, e.g., user-authentication), what problem it solves and who will use it, and key user stories.
2. Use Memory for Context — Before asking repetitive clarification questions, search memory for related decisions or conventions via npx ai-devkit@latest memory search --query "<feature/topic>" and reuse relevant context.
3. Create Feature Documentation Structure — Copy each template's content (preserving YAML frontmatter and section headings) into feature-specific files:
   • docs/ai/requirements/README.md → docs/ai/requirements/feature-{name}.md
   • docs/ai/design/README.md → docs/ai/design/feature-{name}.md
   • docs/ai/planning/README.md → docs/ai/planning/feature-{name}.md
   • docs/ai/implementation/README.md → docs/ai/implementation/feature-{name}.md
   • docs/ai/testing/README.md → docs/ai/testing/feature-{name}.md
4. Requirements Phase — Fill out docs/ai/requirements/feature-{name}.md: problem statement, goals/non-goals, user stories, success criteria, constraints, open questions.
5. Design Phase — Fill out docs/ai/design/feature-{name}.md: architecture changes, data models, API/interfaces, components, design decisions, security and performance considerations.
6. Planning Phase — Fill out docs/ai/planning/feature-{name}.md: task breakdown with subtasks, dependencies, effort estimates, implementation order, risks.
7. Store Reusable Knowledge — When important conventions or decisions are finalized, store them via npx ai-devkit@latest memory store --title "<title>" --content "<knowledge>" --tags "<tags>".
8. Next Command Guidance — Run /review-requirements first, then /review-design. If both pass, continue with /execute-plan.