Can I vibecode SkedPal?

Build a highly configurable, AI-driven personal substitute for SkedPal in an empty repository.
Use exactly this stack: Tauri 2 + React + TypeScript + SQLite.
Primary job: Build a local desktop utility that captures the essential intelligent time-blocking workflow, treating the user's calendar as a dynamic budget and automatically scheduling tasks around existing appointments.

Core Requirements & Architecture:
- Implement an automated scheduling engine that takes tasks with duration, priority, and deadlines, and finds the best slots for them in a calendar view.
- Create "Time Maps" (templates defining availability for specific types of work, e.g., "Deep Work" on weekday mornings).
- Build a hierarchical outline view allowing users to organize tasks into areas, projects, and sub-tasks without rigid structural constraints.
- Develop an "Update Schedule" action that recalculates and reshuffles remaining tasks when priorities shift, a meeting runs over, or a new task is added.
- Add a Kanban-style priority board where manual task ranking informs the auto-scheduler's decisions, ensuring high-priority items are protected.
- Ensure all scheduling logic runs locally to maintain privacy and offline capability.

Technical Constraints:
- Start from an empty folder and create the complete working project.
- Make the default mode single-user and private.
- Store user data locally using the declared self-hosted SQLite database.
- Put every secret and external credential in .env and provide .env.example.
- Implement the smallest polished interface that completes the core loop end to end.
- Include clear empty, loading, validation, success, and failure states.
- Add import and export so the user is not trapped in the app.
- Use accessible keyboard navigation, labels, focus states, and sensible contrast.

Deliberate Exclusions:
- Do not add analytics, telemetry, ads, or third-party accounts.
- Deliberately exclude calendar-provider edge cases, real-time cross-device sync, and deep operating-system integrations.
- Do not fake integrations, network effects, or proprietary AI models; use deterministic heuristic algorithms for scheduling.

Testing & Documentation:
- Write focused unit tests for the core auto-scheduling algorithm and data model.
- Add one end-to-end smoke test that proves the core loop works.
- Create a README with setup, architecture, and limitations.
- Run the tests and build before finishing, then fix errors rather than merely describing them.