AI Dance Form Coach

AI Prompts to Build This

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1. 1. Project Setup

   Build the weekend MVP of "AI Dance Form Coach": your phone camera tracks your body, scores your form against a routine, and tells you what to fix.
   
   Stack: Expo (React Native) with TypeScript and Expo Router, on-device pose estimation (MoveNet Lightning or MediaPipe Pose, whichever works better with Expo), Supabase (Auth with email magic link, Postgres, Row Level Security), Claude (Anthropic API) for the post-session notes. Camera preview with expo-camera or react-native-vision-camera.
   Structure: /app for routes, /lib for pose math, /components for the overlays.
   
   Tables (Row Level Security on, each user reads only their own rows):
   - routines(id, style, title, move_template jsonb)
   - sessions(id, user_id, routine_id, segment_scores jsonb, top_faults jsonb, created_at)
   - coaching_notes(id, session_id, summary, corrections jsonb, drill_plan)
   
   Screens: onboarding (camera permission and calibration), routine picker, live coaching with results.
   Env vars (names only): SUPABASE_URL, SUPABASE_ANON_KEY, ANTHROPIC_API_KEY (server side only).
   A README with the local steps.
   
   Do not build: billing, a paywall or free-session limits, Apple Sign In, the studio dashboard or white-label settings, more than one routine style.
   Done when: the app runs in Expo Go, you can sign in, the camera preview shows, and the three tables exist with Row Level Security on.

2. 2. Core Feature

   Build the one feature that proves the idea: live pose scoring against a routine, with plain-language corrections.
   1. Run on-device pose estimation and read the 33 landmarks each frame. Compute these angles: hip-knee-ankle for both legs, shoulder-hip-knee, and elbow-shoulder-hip.
   2. Load a JSON move template per routine segment with target angle ranges and beat timestamps in milliseconds from the start of the song.
   3. Score each segment from 0 to 100: 40% joint-angle compliance, 40% beat proximity (within 150 ms is full credit) and 20% hold stability (variance over 500 ms).
   4. Draw the skeleton over the camera preview and flash green, yellow or red when a key joint drifts outside its range.
   5. Save the session summary: user, routine, segment scores and top faults.
   6. After the session, send { style, top_faults, segment_scores, user_level } to Claude and return { summary, corrections[], drill_plan }: a 2-sentence summary, three plain corrections with no medical claims, and one 5-minute drill. Tone: encouraging, specific, never shaming. Cache by the fault-code hash for 24 hours.
   Rules: aim for 15 or more frames per second on an iPhone 12 or Pixel 6 class phone, and drop to a lower resolution if it falls under 12.
   Empty state: with no sessions, show the routine picker and a line on how to place the phone.
   Done when: a 30-second routine gives each segment a score with a live overlay, and the results screen shows the summary, three corrections and a drill.

3. 3. Landing Page

   Build a one-page landing site for AI Dance Form Coach.
   Hero: "Your phone is now your dance instructor." Sub: "Real-time corrections for home dancers, at a fraction of the cost of a studio lesson." One button: Join the waitlist.
   Sections: the problem (passive video lessons never tell you what you are doing wrong), how it works in three steps (calibrate your space, pick a routine, get live corrections and a score), a short screen recording placeholder showing the skeleton overlay, and an FAQ on camera privacy (pose tracking runs on the device).
   Waitlist: store the email in a waitlist table in Supabase. No other service.
   Voice: encouraging, specific, never shaming.
   Done when: the page renders on a phone and a submitted email appears in the waitlist table.

4. 4. Branding Package

   Use a design or image tool for this one. A coding agent cannot draw a logo.
   Brand for AI Dance Form Coach: a wordmark and an icon that suggest a figure in motion.
   Colors: near-black, warm white and one energetic accent, with green, yellow and red reserved for the form overlay. Type: one rounded sans for the interface.
   Deliverables: wordmark, app icon, skeleton overlay style (joint dots and limb lines), and one launch graphic.
   Done when: each deliverable is saved in one folder and the overlay colors are distinguishable without color alone.