Objectives

  • Deliver an onboarding wizard that maps Sahil Bloom’s wealth pillars to the AI coaches the student selects (defaulting to all personas per area).
  • Stand up the TickTick (Tic-Tac MCP) integration so action items can be synchronized, tagged, and monitored.
  • Persist the full AI coach matrix, wealth areas, and to-do list associations in the database for downstream phases.
  • Provide administrative controls for managing AI coach personas, embeddings, and integration credentials.

Functional Scope

Wealth Area & AI Coach Selection

  • Build a multi-step onboarding wizard that educates users on Time, Finance, Relationship, Career, and optional Personal Growth wealth types.
  • Present available AI coaches (Atomic Habits, The Magic of Thinking Big, Richard Feynman, Elon Musk) with persona summaries and allow multi-select per wealth area.
  • Support default assignments (all AI personas) with the option to reorder or remove personas per area.
  • Store selections in user_coach_assignments with priority ordering and metadata (e.g., rationale notes from the student) to drive AI routing.
  • Provide edit controls in the /coach/home dashboard so users can revisit AI persona choices.

TickTick / Tic-Tac MCP Setup

  • Implement OAuth login with TickTick (or configure Tic-Tac MCP API credentials) and secure token storage.
  • Fetch available TickTick lists and allow the user to map or auto-create one list per wealth area.
  • Configure default tags for each AI coach persona and wealth area combination to support analytics.
  • Build backend service for task CRUD that conforms to the MCP tool contract (list tasks, create, update, complete, apply tags).
  • Document fallback plan if TickTick is unavailable (local queue, manual completion logging).

Data Model & Services

  • Extend database with tables: coach_profiles (AI persona metadata), wealth_areas, user_coach_assignments, ticktick_accounts, ticktick_lists, action_items (skeleton for future phases).
  • Seed canonical AI coach data (persona prompts, source material references, knowledge embedding pointers).
  • Provide admin UI to edit AI coach metadata, toggle availability, and upload knowledge sources for future retrieval.
  • Add service layer modules for AI coach assignment CRUD, wealth area queries, and TickTick API interactions.

Experience Glue & Analytics

  • Update personalization dashboard with a summary of selected AI coaches per wealth area and TickTick linkage status.
  • Emit analytics events when users change AI coach selections or connect/disconnect TickTick.
  • Add health indicators for integration status (last sync time, outstanding errors).
  • Create migration scripts to backfill wealth area data for existing Phase 1 users.

Technical Considerations

  • Handle AI coach selection concurrency: optimistic UI with background validation, server resolves conflicting updates.
  • Encrypt TickTick access/refresh tokens using KMS or libsodium before storage.
  • Normalize tags for TickTick tasks (e.g., ai_coach:atomic-habits, wealth:time) and document taxonomy for analytics consumers.
  • Use MCP tool schema definitions stored in agents/contracts so multi-agent workers can reuse the integration reliably.
  • Rate limit TickTick API calls and implement exponential backoff with jitter for retries.

Multi-Agent Workstream

| Agent | Responsibilities | Deliverables | | --- | --- | --- | | AI Coach Matrix Architect | Design onboarding wizard UX, AI persona selection logic, and persistence APIs. | Wizard flows, selection components, API schemas. | | Integration Engineer | Build TickTick OAuth, MCP toolset, and synchronization services. | OAuth routes, MCP tool definitions, sync jobs. | | Knowledge Curator | Seed AI coach personas, maintain embeddings, and author persona prompts. | AI coach dataset, embedding pipeline docs. | | Database Engineer | Implement migrations for wealth areas, assignments, TickTick tables, and seeding scripts. | SQL migrations, seed CLI. | | Analytics Lead | Instrument events and dashboards for AI coach selection and integration health. | Event catalog, dashboard configs. | | DevOps Steward | Manage secrets rotation, webhook endpoints, and monitoring alerts. | Secret management plan, uptime dashboards. |

Exit Criteria

  • Users can select and save AI coaches per wealth area during onboarding and edit them later.
  • TickTick accounts connect successfully, with at least one list mapped to each wealth area and verified via API call.
  • MCP toolset exposes listTasks, createTask, updateTask, and completeTask functions with schema validation and tests.
  • Admins can view and update AI coach metadata through a secured interface or CLI.
  • Analytics dashboard highlights AI coach selection distribution and TickTick integration health metrics.

Risks & Mitigations

| Risk | Mitigation | | --- | --- | | Users overwhelmed by AI coach persona choices. | Provide recommended defaults, highlight most popular personas, allow “decide later” option with reminders. | | TickTick OAuth complexity or rate limits. | Cache metadata, defer heavy sync tasks to background jobs, communicate sync status clearly to users. | | Coach data drift (persona updates). | Version persona prompts, run regression tests on knowledge responses, and require review before publishing changes. | | Data consistency across wealth areas and TickTick lists. | Enforce constraints ensuring each area has exactly one default list; add nightly reconciliation job. |

Dependencies & Notes

  • Requires Phase 1 tables (users, favorites, base analytics) and environment scaffolding to be in place.
  • Coordinate with legal/compliance on storing TickTick tokens and presenting consent copy.
  • Prepare support documentation so users understand why TickTick access is needed and how data is used.
  • Share AI coach selection data schema with Phase 3 team to seed session context retrieval.