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Symbolic Scribe

A specialized prompt engineering tool that uses mathematical frameworks to generate precise, structured prompts for AI interactions.

Key Features & Benefits

Mathematical Framework Integration

  • Set Theory Templates: Model complex relationships and hierarchies
  • Category Theory: Define abstract transformations and mappings
  • Abstract Algebra: Structure group operations and symmetries
  • Topology: Explore continuous transformations and invariants
  • Complex Analysis: Handle multi-dimensional relationships

Practical Applications

  • Information Security: Model threat vectors and attack surfaces
  • Ethical Analysis: Structure moral frameworks and constraints
  • AI Safety: Define system boundaries and safety properties
  • Domain Adaptation: Apply mathematical rigor to any field

User Experience

  • Interactive Console: Terminal-style interface with modern aesthetics
  • Real-time Preview: Test prompts with multiple AI models
  • Template Library: Pre-built frameworks for common use cases
  • Mobile Responsive: Full functionality on all device sizes
  • Local Storage: Secure saving of prompts and preferences

Security Features

API Key Management

  • Encrypted local storage of API keys
  • Optional environment variable configuration
  • No server-side key storage
  • Automatic key validation

Data Privacy

  • Client-side only processing
  • No external data transmission except to OpenRouter API
  • No tracking or analytics
  • Configurable model selection

Quick Start

  1. Installation
git clone https://github.com/yourusername/symbolic-scribe.git
cd symbolic-scribe
npm install
  1. Configuration
cp .env.sample .env
# Edit .env with your OpenRouter API key
  1. Development
npm run dev
  1. Production Build
npm run build
npm run preview

Usage Guide

Basic Prompt Generation

  1. Select a mathematical framework template
  2. Choose your target domain
  3. Define your variables and relationships
  4. Generate structured prompts

Template Customization

  1. Navigate to Templates page
  2. Select a base template
  3. Modify variables and relationships
  4. Save for future use

Testing & Iteration

  1. Use the Preview function to test prompts
  2. Select different models for comparison
  3. Refine based on responses
  4. Export final versions

InfoSec Overview

Threat Model

  • Client-side only architecture
  • No persistent server storage
  • Encrypted API key storage
  • Input sanitization

Best Practices

  • Regular API key rotation
  • Use environment variables in production
  • Monitor API usage
  • Review generated prompts for sensitive data

Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

  1. Fork the repository
  2. Create a feature branch
  3. Install dependencies
  4. Make your changes
  5. Run tests
  6. Submit a PR

Support

  • Documentation: /docs page in app
  • Issues: GitHub issue tracker
  • Community: Discord server (coming soon)

License

MIT License - see LICENSE file for details

Acknowledgments

  • OpenRouter for AI model access
  • shadcn/ui for component library
  • Tailwind CSS for styling
  • Vite for build tooling

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  • TypeScript 98.0%
  • Other 2.0%