Deploying To Production

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littleben

Deploying to Production

Automates GitHub repository creation and Vercel deployment for Next.js websites. Use when deploying new websites, pushing to production, setting up CI/CD pipelines, or when the user mentions deployment, GitHub, Vercel, or going live.

Overview

Publisher	littleben
Repository	`awesomeAgentskills`
Skill name	`Deploying to Production`
Stars	187
Forks	31
Bundled files	4
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by littleben on GitHub. Read the source before you install it.

Installation

Install the Deploying To Production AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/littleben/awesomeAgentskills.git /tmp/awesomeAgentskills
mkdir -p .claude/skills
cp -r /tmp/awesomeAgentskills/deploying-to-production .claude/skills/littleben-deploying-to-production

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deploying To Production in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Deploying To Production on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Deploying To Production is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Deploying to Production

Automated deployment workflow for Next.js websites using GitHub and Vercel.

When to use this Skill

Creating a new website and need to deploy it
Setting up GitHub repository for version control
Deploying to Vercel production environment
User mentions: "deploy", "GitHub", "Vercel", "go live", "publish"

Deployment Workflow

Copy this checklist and track your progress:


Deployment Progress:
- [ ] Step 1: Pre-deployment validation (build + E-E-A-T check)
- [ ] Step 2: Create GitHub repository
- [ ] Step 3: Push code to GitHub
- [ ] Step 4: Deploy to Vercel
- [ ] Step 5: Post-deployment verification

Step 1: Pre-deployment validation

Run build and verify no errors:

bash
cd "$PROJECT_DIR"
npm run build

CRITICAL: Only proceed if build succeeds with no errors.

Pre-deployment checklist - See CHECKLIST.md for complete list:

npm run build completes successfully
All environment variables configured
E-E-A-T elements present (About page, author info)
Core Web Vitals acceptable
SEO meta tags complete

Step 2: Create GitHub repository

Run the script to create a private GitHub repository:

bash
bash scripts/create-github-repo.sh <project-name>

What this script does:

Creates a private GitHub repository
Initializes Git (if needed)
Commits all changes
Pushes to GitHub

If the script fails, see TROUBLESHOOTING.md.

Step 3: Verify GitHub push

Check the repository URL:

bash
gh repo view --web

Verify all files are pushed correctly.

Step 4: Deploy to Vercel

Run the deployment script:

bash
bash scripts/deploy-to-vercel.sh <project-name>

What this script does:

Links the project to Vercel
Deploys to production environment
Returns deployment URL

If deployment fails, see TROUBLESHOOTING.md.

Step 5: Post-deployment verification

Verify deployment:

Visit the deployment URL
Test core functionality:
- Homepage loads correctly
- Navigation works
- Core features functional
Check Core Web Vitals (use PageSpeed Insights)
Verify SEO meta tags (use browser inspector)

If issues found:

Review Vercel build logs: vercel logs
Check environment variables in Vercel dashboard
Verify DNS settings (if custom domain)
Return to Step 1 and fix issues

Only mark deployment complete when all verifications pass.

Script locations

All deployment scripts are in the scripts/ directory:

create-github-repo.sh - GitHub repository creation
deploy-to-vercel.sh - Vercel deployment

Important notes

Prerequisites:

GitHub CLI (gh) installed and authenticated
Vercel CLI installed and authenticated
Project must be in /Volumes/Time/go to wild/websites/ directory

Project naming convention:

Format: keyword-site-lang (e.g., pdf-converter-jp)
Use lowercase and hyphens only

Environment variables:

Configure in Vercel dashboard after first deployment
Required variables depend on project features (database, auth, etc.)

Next steps after deployment

Set up monitoring:
- Add Google Analytics or Plausible
- Configure Google Search Console
- Set up Vercel Analytics
Configure custom domain (if needed):
- Add domain in Vercel dashboard
- Update DNS records
- Wait for SSL certificate
Enable automatic deployments:
- Push to main branch auto-deploys to production
- Push to other branches creates preview deployments

For detailed troubleshooting, see TROUBLESHOOTING.md.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Deploying To Production AI skill do?

Why use Deploying To Production on TypingMind?

Because you install it once and use it with any model. Deploying To Production is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Deploying To Production in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/littleben/awesomeAgentskills/tree/main/deploying-to-production. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Deploying To Production?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Deploying To Production?

As many as you like. As long as a model supports skills, you can use Deploying To Production with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Deploying To Production AI skill free?

Yes. It is published on GitHub by littleben under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Deploying to Production

When to use this Skill

Deployment Workflow

Step 1: Pre-deployment validation

Step 2: Create GitHub repository

Step 3: Verify GitHub push

Step 4: Deploy to Vercel

Step 5: Post-deployment verification

Script locations

Important notes

Next steps after deployment

Bundled files

Frequently asked questions

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