Habit Coach App Blueprint

A complete architecture for an AI-powered habit coaching app — daily check-ins, personalized AI coaching prompts, streak tracking, weekly reviews, and a Postgres-backed progress database. 9 nodes, ready to export to Cursor or Claude Code.

What this app does

A habit coaching app helps users build and maintain daily habits through a simple check-in flow, AI-generated coaching messages, and visual progress tracking. Users log whether they completed their habit each day. The AI analyzes their streak data and check-in patterns to deliver personalized encouragement, identify when motivation is dipping, and suggest adjustments.

This blueprint maps out 9 nodes across frontend pages, backend APIs, AI prompts, a database, and a scheduler. It's designed as a mobile-first PWA that works offline and syncs when connected.

Architecture diagram

Node breakdown

Frontend pages

• Daily Check-in — a simple, fast screen where users log their habit for the day. One tap to mark complete, optional notes field. Shows current streak count and today's coaching message from the AI.
• Progress Dashboard — visual overview of habit history: calendar heatmap, streak statistics, longest streak, completion rate, and weekly AI review summaries. Charts show trends over 7/30/90 days.
• Settings — manage habit list (add, edit, remove), set reminder times, configure coaching tone (encouraging, direct, humorous), and set weekly review day.

Backend APIs

• Habits API — GET /api/habits (list user's habits), POST /api/habits (create new habit), POST /api/habits/:id/checkin (log daily check-in), GET /api/habits/:id/history (paginated check-in history).
• Streaks API — GET /api/streaks (current streaks for all habits), GET /api/streaks/:habitId (detailed streak data: current, longest, broken streaks), POST /api/streaks/recalculate (recompute streaks from check-in history).

AI prompts

• AI Coach Prompt — input: user's habit, current streak, last 7 days of check-ins, coaching tone preference; task: generate a personalized coaching message for today; output: 1-2 sentence encouraging message tailored to streak status (e.g. "You're on a 12-day streak — keep going!" or "You missed yesterday. No judgment — let's start fresh today.").
• Weekly Review — input: 7 days of check-in data, streak changes, habit completion rates, user notes; task: generate a weekly summary with insights and one actionable suggestion; output: 3-4 paragraph review covering what went well, what slipped, and a focus area for next week.

Database & services

• Progress DB (PostgreSQL) — tables: habits (id, user_id, name, frequency, color, created_at), check_ins (id, habit_id, date, completed, note, created_at), streaks (id, habit_id, current_count, longest_count, last_completed_date, updated_at), users (id, email, name, coaching_tone, reminder_time, review_day, created_at), weekly_reviews (id, user_id, week_start, summary_text, suggestion, created_at).
• Reminder Scheduler — a cron job that sends push notifications at the user's configured reminder time. Also triggers the AI Coach Prompt to generate the day's coaching message before the notification goes out, so the message is ready when the user opens the app.

Build this app exactly as described.
Start with database schema and auth, then APIs, then frontend pages.

## Daily Check-in (Frontend Page)
Simple, fast screen for logging daily habits. One tap to mark complete,
optional notes field. Shows current streak count and today's AI
coaching message. Mobile-first PWA design.

## Progress Dashboard (Frontend Page)
Visual overview: calendar heatmap, streak statistics, longest streak,
completion rate, and weekly AI review summaries. Charts for 7/30/90 days.

## Settings (Frontend Page)
Manage habit list (add/edit/remove), set reminder times, configure
coaching tone (encouraging, direct, humorous), set weekly review day.

## Habits API (REST Endpoint)
- GET /api/habits — list user's habits
- POST /api/habits — create new habit
- POST /api/habits/:id/checkin — log daily check-in
- GET /api/habits/:id/history — paginated check-in history

## Streaks API (REST Endpoint)
- GET /api/streaks — current streaks for all habits
- GET /api/streaks/:habitId — detailed streak data
- POST /api/streaks/recalculate — recompute from check-in history

## Progress DB (PostgreSQL)
- habits: id, user_id, name, frequency, color, created_at
- check_ins: id, habit_id, date, completed, note, created_at
- streaks: id, habit_id, current_count, longest_count, last_completed_date, updated_at
- users: id, email, name, coaching_tone, reminder_time, review_day, created_at
- weekly_reviews: id, user_id, week_start, summary_text, suggestion, created_at

## AI Coach Prompt (AI Prompt)
Input: habit name, current streak, last 7 days of check-ins, tone preference
Task: generate a personalized coaching message for today
Output: 1-2 sentence encouraging message tailored to streak status

## Weekly Review (AI Prompt)
Input: 7 days of check-in data, streak changes, completion rates, user notes
Task: generate weekly summary with insights and one actionable suggestion
Output: 3-4 paragraph review with what went well, what slipped, and focus area

## Reminder Scheduler (Cron Job)
Daily job at user's configured reminder time. Triggers AI Coach Prompt
to generate the day's message, then sends push notification. Also triggers
Weekly Review on the user's configured review day.