What is build-and-deploy?

This Claude skill automates the end-to-end workflow for building and deploying Next.js applications integrated with LangChain and Supabase vector stores. It streamlines the entire production readiness process, including environment variable synchronization, database schema initialization for pgvector, and optimized deployment strategies for platforms like Vercel and Netlify.

When should I use build-and-deploy?

build-and-deploy is useful in the following scenarios: • Rapidly bootstrapping a Retrieval-Augmented Generation (RAG) application with a pre-configured Next.js and LangChain stack. • Automating the setup of Supabase vector databases, including the creation of document tables and vector matching functions. • Executing reliable production deployments to Vercel using the mandatory prebuilt mode to avoid Git permission issues in automated environments. • Synchronizing local environment variables and secrets with cloud hosting providers for seamless project transitions. • Preparing and validating project builds to ensure compatibility with production-grade vector search requirements.

name	build-and-deploy
description	Build and deploy this Next.js LangChain retrieval application with Supabase vector store. Use when building, deploying, setting up vector store, or preparing the project for production.

Build and Deploy LangChain Retrieval

CRITICAL: For Vercel, you MUST use vercel build --prod then vercel deploy --prebuilt --prod. Do NOT use vercel --prod or vercel deploy --prod directly - these will fail due to Git author permission issues in VM environments.

Workflow

1. Setup Environment Variables

Read .env.example to see all required variables:

cat .env.example

Create .env by reading values from current environment:

For each variable in .env.example, read the value from the current environment and write to .env. Example approach:

# Read .env.example and create .env with values from current environment
while IFS= read -r line || [[ -n "$line" ]]; do
  # Skip comments and empty lines
  [[ "$line" =~ ^#.*$ || -z "$line" ]] && continue
  # Extract variable name (before = sign)
  var_name=$(echo "$line" | cut -d'=' -f1)
  # Get value from environment
  var_value="${!var_name}"
  # Write to .env
  echo "${var_name}=${var_value}" >> .env
done < .env.example

Or manually inspect .env.example and create .env with the required values from environment variables.

2. Install Dependencies

yarn install

3. Setup Supabase Vector Store

Get the database connection URL from Supabase Dashboard:

Go to Settings → Database → Connection string → URI
Copy the URI and add it to .env as SUPABASE_DB_URL

yarn setup-db

This creates the pgvector extension, documents table, and match_documents function in your Supabase project.

4. Build

yarn build

5. Deploy

Vercel:

All vercel CLI commands require -t <token> or --token <token> for authentication.

# Pull project settings (also links project, creates .vercel/project.json)
vercel pull --yes -t $VERCEL_TOKEN

# Push env vars to Vercel (first time only)
# Must add to each environment separately
while IFS='=' read -r key value; do
  [[ "$key" =~ ^#.*$ || -z "$key" || -z "$value" ]] && continue
  for env in production preview development; do
    printf '%s' "$value" | vercel env add "$key" $env -t $VERCEL_TOKEN
  done
done < .env

# Build locally for production
vercel build --prod -t $VERCEL_TOKEN

# Deploy prebuilt
vercel deploy --prebuilt --prod --yes -t $VERCEL_TOKEN

Netlify:

# Import all env vars from .env (first time only)
netlify env:import .env

# Deploy
netlify deploy --prod

Critical Notes

VERCEL PREBUILT MODE IS MANDATORY: Always use vercel build --prod followed by vercel deploy --prebuilt --prod. Never use vercel --prod or vercel deploy --prod without --prebuilt flag.
Supabase Required: Need a Supabase project (free tier works)
Vector Store Setup: Run yarn setup-db to create tables automatically
Environment Variables: All values come from current environment - inspect .env.example for required variables
OpenAI for Embeddings: OPENAI_API_KEY is always required for vector embeddings
No Dev Server: Never run yarn dev in VM environment

build-and-deploy

When & Why to Use This Skill

Use Cases