Deploying To Production

Community

ZhanlinCui

deploying-to-production

Automate creating a GitHub repository and deploying a web project to Vercel. Use when the user asks to deploy a website/app to production, publish a project, or set up GitHub + Vercel deployment.

Overview

Publisher	ZhanlinCui
Repository	`Agent-Skills-Hunter`
Skill name	`deploying-to-production`
Stars	186
Forks	26
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by ZhanlinCui 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/ZhanlinCui/Agent-Skills-Hunter.git /tmp/Agent-Skills-Hunter
mkdir -p .claude/skills
cp -r /tmp/Agent-Skills-Hunter/dev/deploying-to-production .claude/skills/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

Use this workflow when a user says "deploy this website/app" or similar. Follow the checklist in order and do not skip steps.

Deployment Workflow

Step 1: Run build and verify no errors
Step 2: Create GitHub repository
Step 3: Push code to GitHub
Step 4: Deploy to Vercel
Step 5: Verify deployment

Step 1: Run build

Run:

npm run build

If build fails, read the errors, fix issues, and run again. Only proceed when build succeeds.

Step 2: Create GitHub repository

Create a new GitHub repository for the project. If the repo already exists, confirm whether to reuse or create a new one.

Step 3: Push code to GitHub

Initialize git if needed, add remote, and push the default branch. Confirm the repository contains the expected code.

Step 4: Deploy to Vercel

Deploy the GitHub repo to Vercel. Capture the deployment URL.

Step 5: Verify deployment

Verify the live deployment by opening the URL or checking a response. If verification fails, diagnose and redeploy.

Frequently asked questions

What does the Deploying To Production AI skill do?

Automate creating a GitHub repository and deploying a web project to Vercel. Use when the user asks to deploy a website/app to production, publish a project, or set up GitHub + Vercel deployment.

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/ZhanlinCui/Agent-Skills-Hunter/tree/main/dev/deploying-to-production. TypingMind reads its SKILL.md 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 ZhanlinCui 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

Deployment Workflow

Step 1: Run build

Step 2: Create GitHub repository

Step 3: Push code to GitHub

Step 4: Deploy to Vercel

Step 5: Verify deployment

Frequently asked questions

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