build-and-deploy — community build-and-deploy, langchain-retrieval-agent, community, ide skills

v1.0.0

About this Skill

Perfect for LangChain.js Agents needing streamlined deployment with LangGraph and Supabase vector store. Build and deploy this Next.js LangChain retrieval agent application with Supabase vector store. Use when building, deploying, setting up vector store, or preparing the project for production.

rebyteai-template rebyteai-template
[0]
[0]
Updated: 12/4/2025

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 7/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Locale and body language aligned
Review Score
7/11
Quality Score
36
Canonical Locale
en
Detected Body Locale
en

Perfect for LangChain.js Agents needing streamlined deployment with LangGraph and Supabase vector store. Build and deploy this Next.js LangChain retrieval agent application with Supabase vector store. Use when building, deploying, setting up vector store, or preparing the project for production.

Core Value

Empowers agents to deploy LangChain.js applications with Vercel AI SDK, utilizing LangGraph for knowledge graph management and Supabase for vector storage, while avoiding Git author permission issues in VM environments through proper use of `vercel build --prod` and `vercel deploy --prebuilt --prod` commands.

Ideal Agent Persona

Perfect for LangChain.js Agents needing streamlined deployment with LangGraph and Supabase vector store.

Capabilities Granted for build-and-deploy

Deploying LangChain.js agents with LangGraph
Setting up Supabase vector store for AI models
Automating Vercel deployments for LangChain applications

! Prerequisites & Limits

  • Requires Vercel AI SDK
  • Specific build and deploy commands must be used to avoid permission issues
  • LangGraph and Supabase setup required

Why this page is reference-only

  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Labs Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is build-and-deploy?

Perfect for LangChain.js Agents needing streamlined deployment with LangGraph and Supabase vector store. Build and deploy this Next.js LangChain retrieval agent application with Supabase vector store. Use when building, deploying, setting up vector store, or preparing the project for production.

How do I install build-and-deploy?

Run the command: npx killer-skills add rebyteai-template/langchain-retrieval-agent/build-and-deploy. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for build-and-deploy?

Key use cases include: Deploying LangChain.js agents with LangGraph, Setting up Supabase vector store for AI models, Automating Vercel deployments for LangChain applications.

Which IDEs are compatible with build-and-deploy?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for build-and-deploy?

Requires Vercel AI SDK. Specific build and deploy commands must be used to avoid permission issues. LangGraph and Supabase setup required.

How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add rebyteai-template/langchain-retrieval-agent/build-and-deploy. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use build-and-deploy immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

build-and-deploy

Install build-and-deploy, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Build and Deploy LangChain Retrieval Agent

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:

bash
1cat .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:

bash
1# Read .env.example and create .env with values from current environment 2while IFS= read -r line || [[ -n "$line" ]]; do 3 # Skip comments and empty lines 4 [[ "$line" =~ ^#.*$ || -z "$line" ]] && continue 5 # Extract variable name (before = sign) 6 var_name=$(echo "$line" | cut -d'=' -f1) 7 # Get value from environment 8 var_value="${!var_name}" 9 # Write to .env 10 echo "${var_name}=${var_value}" >> .env 11done < .env.example

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

2. Install Dependencies

bash
1yarn 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
bash
1yarn setup-db

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

4. Build

bash
1yarn build

5. Deploy

Vercel:

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

bash
1# Pull project settings (also links project, creates .vercel/project.json) 2vercel pull --yes -t $VERCEL_TOKEN 3 4# Push env vars to Vercel (first time only) 5# Must add to each environment separately 6while IFS='=' read -r key value; do 7 [[ "$key" =~ ^#.*$ || -z "$key" || -z "$value" ]] && continue 8 for env in production preview development; do 9 printf '%s' "$value" | vercel env add "$key" $env -t $VERCEL_TOKEN 10 done 11done < .env 12 13# Build locally for production 14vercel build --prod -t $VERCEL_TOKEN 15 16# Deploy prebuilt 17vercel deploy --prebuilt --prod --yes -t $VERCEL_TOKEN

Netlify:

bash
1# Import all env vars from .env (first time only) 2netlify env:import .env 3 4# Deploy 5netlify 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

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