What core problem does the Chatbot Creator skill solve for developers?

This skill automates the creation of a complete full-stack environment, providing production-ready boilerplate for authentication, database management, and real-time streaming that typically takes hours to write manually.

Which LLM providers and AI models are supported by the scaffolded project?

The skill generates a generic service layer compatible with major providers like OpenAI, Claude, and Gemini, though you will need to provide your own API keys for the specific models you choose.

What exactly is included in the generated project files?

You receive a complete directory structure containing over 30 files, including a FastAPI backend, a TypeScript React frontend, and a pre-configured SQLite database with SQLAlchemy models.

What technical stack requirements are needed to run the generated application?

The frontend is built with React 18 and Vite using TypeScript, while the backend uses Python 3.11+ with FastAPI, ensuring a modern, type-safe development experience.

Can I run this skill in a directory that already contains code?

Yes, the skill includes a safety check that detects existing directories and will request your explicit permission before overwriting any files to prevent accidental data loss.

Does the generated project include deployment configurations like Docker?

The skill focuses on a robust local development setup using SQLite; while it provides the core application logic, you would need to manually add Docker containers or CI/CD pipelines for production hosting.

chatbot creator skill

Example session with this skill installed

I need to build a new chatbot project from scratch with a React frontend and a FastAPI backend that supports streaming.

Read your context and instructions
Compiled the chatbot creator

I have scaffolded a complete LLM chatbot application. The project includes a FastAPI backend with JWT authentication and LangGraph services, paired with a React TypeScript frontend featuring WebSocket streaming for real-time responses.

Project Structure Overview

Component	Technology	Path
Backend API	FastAPI + SQLAlchemy	`/backend/app/api`
Orchestration	LangGraph + Memory	`/backend/app/services`
Database	SQLite + SQLAlchemy	`/backend/app/database.py`
Frontend	React + Vite + TS	`/frontend/src`
State Mgmt	Custom Hooks (Auth/Chat)	`/frontend/src/hooks`


# Backend Setup
cd backend
python -m venv venv
source venv/bin/activate  # Windows: venv\Scripts\activate
pip install -r requirements.txt

# Set your API keys in .env
uvicorn app.main:app --reload

# Frontend Setup
cd ../frontend
npm install
npm run dev

Next steps

Add your OPENAI_API_KEY or ANTHROPIC_API_KEY to backend/.env.
Run npm run build in the frontend folder to verify the TypeScript types.
Access the dashboard at http://localhost:5173 to register your first user account.

Connects securely to your tools. The creator never sees your data.

About this skill

Building a full-stack LLM application from scratch usually requires hours of boilerplate setup for authentication, database schemas, and WebSocket streaming. This skill automates the entire process, scaffolding a complete FastAPI and React project ready for Claude, OpenAI, or Gemini integration.

What it does

Scaffolds full-stack projects by generating a complete directory structure for backend and frontend.
Configures user authentication using JWT tokens, bcrypt password hashing, and secure login/registration flows.
Enables real-time streaming via native WebSocket implementations in both FastAPI and React.
Manages chat persistence with a pre-configured SQLite database and SQLAlchemy models for sessions and messages.
Integrates LangGraph for sophisticated LLM orchestration using stateful graphs and memory.

How it works

Safety check to identify existing directories and request explicit overwrite permission.
Backend generation of FastAPI modules, security utilities, and LangGraph service layers.
Frontend generation of a TypeScript React app, including custom hooks for auth and chat.
Validation via a Python script to ensure all 30+ files are correctly placed.

Frameworks & tools

Python 3.11, FastAPI, SQLAlchemy, LangGraph, SQLite, React 18, TypeScript, Vite, and JWT.

Why this beats prompting it yourself

Prompting a full-stack app usually results in disconnected snippets or missing imports. This skill enforces a strict, production-ready architecture with consistent schemas across both the Python and TypeScript layers.

Use cases

Launching a private internal AI chatbot for a team.
Prototyping a new LLM-powered SaaS product.
Creating a sandbox for testing LangGraph logic with a real UI.
Scaffolding a technical demonstration for clients.

Known limitations

Does not include Docker configurations, CI/CD pipelines, or social OAuth providers. Limited to SQLite for local development.

How to use

Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

Reviews

No reviews yet

Be one of the first to try it. Every listed skill passes our trust checks below.

Security scanned

Passed our 8-point scan before listing

Fresh listing

Recently published to Agensi

Free forever

No account required to browse

chatbot creator skill