Trainlytics

A multi-tenant fitness analytics platform where every user's training data is isolated at the database layer — workout logging, bodyweight and calorie tracking, dashboard trends, and an AI insights chat, for lifters who want low-friction tracking they can trust with their data.

Live demo → trainlytics-two.vercel.app

Screenshots

Not yet captured. These are the shots that would explain Trainlytics fastest. Drop each file at the path shown and uncomment the matching line.

Screenshot	Path	Why it matters
The dashboard with strength, muscle-group, and energy-balance charts	docs/screenshots/dashboard.png	The analytics surface — the reason the project exists
The workout logger mid-session, with a split selected and sets entered	docs/screenshots/workout-logger.png	Shows the core logging loop and per-user exercise management
The AI insights chat answering a question about recent training	docs/screenshots/insights-ai.png	Shows the LLM layer grounded in the user's own workout history

What it demonstrates technically

• Per-user isolation enforced by PostgreSQL row-level security. Every app table (exercises, workout_sessions, workout_sets, bodyweight_logs, calories_logs, profiles) has RLS enabled with separate owner policies for select, insert, update, and delete. db/audit/rls_policy_audit.sql re-verifies that coverage against the live database, so isolation is auditable rather than assumed.
• Route access decided in one place. proxy.ts classifies every request as public or protected, checks the Supabase session, and enforces session timeout before a page renders — so a new page cannot accidentally ship unguarded.
• A defensively built LLM endpoint. /api/insights-ai caps request bodies at 24 KB, truncates chat history to the last six messages, and aborts the provider call on a bounded, configurable timeout, so a slow or hostile request cannot hang the route.
• Migrations are a versioned plan validated in CI. db/plan.json plus npm run db:check-plan runs on every build, which catches schema drift before deploy.
• CI gates the merge, not just the branch — lint, typecheck, unit tests with coverage, a production build, the DB plan check, and a Playwright suite over the auth-critical flows.

Why Trainlytics

Trainlytics is built for lifters who want low-friction tracking and trustworthy data isolation.

• OTP-backed signup and password reset flows
• Workout logging by split (push, pull, legs, core)
• User-scoped exercise management with archive, unarchive, and permanent delete flows
• Guided workout export by category, muscle group, or exercise in CSV, XLSX, and PDF
• Bodyweight and calorie tracking
• Dashboard and insights trends
• Strong route guarding + Supabase RLS isolation
• Built-in monitoring endpoint for runtime/auth/API issues

Table of Contents

• Screenshots
• What it demonstrates technically
• Why Trainlytics
• Tech Stack
• Quick Start
• Environment Variables
• Architecture
• Quality Checks
• Status
• License

Tech Stack

• Next.js 16 (App Router)
• React 19 + TypeScript
• Tailwind CSS 4
• Supabase (Auth, Postgres, Storage, RLS)
• xlsx, jspdf, jspdf-autotable for workout exports
• Vitest (unit tests)
• Playwright (auth-critical E2E)

Quick Start

npm install
npm run dev

App URL: http://localhost:3000

Environment Variables

Create .env.local:

# Required
NEXT_PUBLIC_SUPABASE_URL=https://YOUR_PROJECT_REF.supabase.co
NEXT_PUBLIC_SUPABASE_ANON_KEY=YOUR_SUPABASE_ANON_KEY
SUPABASE_SERVICE_ROLE_KEY=YOUR_SUPABASE_SERVICE_ROLE_KEY

# Optional (Insights AI chat only)
OPENAI_API_KEY=YOUR_OPENAI_API_KEY
OPENAI_MODEL=gpt-4o-mini
OPENAI_BASE_URL=https://api.openai.com/v1

• Never expose SUPABASE_SERVICE_ROLE_KEY in browser/client code.
• NEXT_PUBLIC_SUPABASE_ANON_KEY must be your anon key, not service role.

Architecture

Core directories:

• app/ routes and API endpoints
• features/ feature modules (UI + domain logic)
• lib/ shared services/utilities (auth, routes, monitoring, helpers)
• db/ schema, migrations, audits, migration plan
• scripts/db/ DB plan runner + validator
• e2e/ Playwright suite

System Diagram

flowchart LR
  U[User Browser] --> N[Next.js App Router]
  N --> P[proxy.ts Route Guard]
  P -->|Public| A[Auth Pages<br/>/login /signup /forgot-password]
  P -->|Protected| F[Feature Pages<br/>dashboard/log/bodyweight/calories/profile/insights]

  A --> SC[lib/supabaseClient]
  F --> SC
  SC --> SA[(Supabase Auth)]
  SC --> SD[(Supabase Postgres + RLS)]
  SC --> SS[(Supabase Storage)]

  F --> API2["/api/insights-ai"]
  API2 --> OAI[(OpenAI API)]

  N --> MON["/api/monitoring/error"]
  A --> MON
  F --> MON

Runtime Flow

1. Requests hit proxy.ts, enforcing public/protected route access.
2. Client pages use lib/supabaseClient for auth/session and scoped data.
3. Supabase RLS policies enforce per-user table isolation.
4. Server routes handle optional AI insights and operational monitoring.
5. Client/server runtime errors are reported via /api/monitoring/error.

Key Product Behaviors

• Active exercises are managed directly from the logger for faster workout setup.
• Archived exercises are managed from Profile, where users can unarchive or permanently delete them.
• Permanent delete removes the exercise row and related workout history from Supabase for that user.
• Export is contextual to the logger and supports category-level, muscle-group-level, and exercise-level history downloads.

Quality Checks

Every command below is a script in package.json.

npm run lint           # ESLint
npm run typecheck      # tsc --noEmit
npm run test           # Vitest unit tests
npm run test:coverage  # Vitest with coverage
npm run check          # lint + typecheck + coverage, in one pass

npm run db:check-plan  # validate the migration plan (also runs in CI)
npm run db:migrate     # apply the migration plan

npm run e2e:install    # one-time: install the Chromium build Playwright uses
npm run e2e            # Playwright suite over the auth-critical flows

CI runs lint, typecheck, unit tests with coverage, a production build, the DB plan check, and the Playwright auth suite on every pull request.

Status

Deployed and actively maintained. Running in production on Vercel with Supabase as the auth and data layer; a scheduled workflow polls the deployment for uptime. Built as a portfolio project rather than a commercial product.

License

MIT — see LICENSE.