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Layer Prompt Manager

Created By
vivek10010 months ago
This helps you create a mcp server which your IDE can access to create and save prompts for you code base
Content

Layer Prompt Manager

Layer Prompt Manager

Save, version, and manage custom AI prompts for your code repositories. Seamlessly integrate with AI-powered IDEs like Cursor and GitHub Copilot.

Features

1. Save Prompts from Your IDE

Create and save custom prompts directly from your AI-powered IDE. The MCP server connects your development environment to Layer, allowing you to build a library of effective prompts specific to your codebase.

2. Version Control for Prompts

Track the evolution of your prompts over time. Compare different versions to see which ones produce the best results. Easily roll back to previous versions when needed.

3. Layer Prompts

  • Access all your Layer prompts
  • Edit existing prompts
  • Create new prompts for AI-powered coding assistants
  • Manage prompt versions

4. Templates

  • Use pre-existing templates
  • Create your own templates
  • Standardize AI interactions across your development team
  • Share and reuse common prompt patterns

5. Modern UI

  • Matrix-inspired design
  • Dark mode interface
  • Responsive layout
  • Interactive components

Project Structure

├── frontend/
│   ├── app/
│   │   ├── prompts/      # Prompt management pages
│   │   ├── templates/    # Template management pages
│   │   ├── tools/        # Tool-related components
│   │   └── docs/         # Documentation pages
│   ├── components/       # Reusable UI components
│   └── lib/             # API and utility functions
├── backend/
│   └── main.py          # FastAPI backend server

Setup Instructions

Prerequisites

  • Node.js (v16 or higher)
  • Python 3.8+
  • pip
  • SQLite

Backend Setup

  1. Create a virtual environment:
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
  1. Install dependencies:
pip install -r requirements.txt
  1. Create a .env file in the root directory:
LAYER_API_KEY=your_layer_api_key
LAYER_BASE_URL=https://api.buildwithlayer.com
  1. Start the backend server:
uvicorn main:app --reload

Frontend Setup

  1. Install dependencies:
cd frontend
npm install
  1. Create a .env.local file:
NEXT_PUBLIC_API_URL=http://localhost:8000
  1. Start the development server:
npm run dev

Usage

  1. Creating Prompts

    • Navigate to "Layer Prompts"
    • Click "Create New Prompt"
    • Fill in prompt details, steps, and arguments
    • Save and version your prompt
  2. Managing Templates

    • Go to "Templates"
    • Create new templates or use existing ones
    • Edit and customize templates for your team
  3. Version Control

    • Each prompt can have multiple versions
    • Add change notes for version tracking
    • Compare and restore previous versions

Environment Variables

Backend (.env):

LAYER_API_KEY=your_layer_api_key
LAYER_BASE_URL=https://api.buildwithlayer.com

Frontend (.env.local):

NEXT_PUBLIC_API_URL=http://localhost:8000

.gitignore

# Dependencies
node_modules/
venv/
__pycache__/

# Environment variables
.env
.env.local
.env.development.local
.env.test.local
.env.production.local

# Build outputs
.next/
build/
dist/

# IDE
.vscode/
.idea/

# Database
*.db
*.sqlite3

# Logs
*.log
npm-debug.log*
yarn-debug.log*
yarn-error.log*

# System Files
.DS_Store
Thumbs.db

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Commit your changes
  4. Push to the branch
  5. Create a Pull Request

License

MIT License - feel free to use this project for your own purposes.

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