dmps 0.2.1

Dual-Mode Prompt System for AI prompt optimization using 4-D methodology

DMPS - Dual-Mode Prompt System

A secure, enterprise-grade Python package for AI prompt optimization using the 4-D methodology (Deconstruct, Develop, Design, Deliver).

Features

Core Optimization

• Intent Detection: Automatically classifies prompt intent (creative, technical, educational, analytical)
• Gap Analysis: Identifies missing information and optimization opportunities
• 4-D Optimization: Systematic optimization using proven methodologies
• Dual Output Modes: Conversational and structured JSON formats
• Platform Support: Optimized for Claude, ChatGPT, Gemini, and generic platforms

Security & Performance (v0.2.0)

• Enterprise Security: Path traversal protection, RBAC, input sanitization
• Token Tracking: Cost estimation and usage monitoring
• Context Engineering: Performance evaluation and optimization metrics
• Observability: Real-time monitoring and alerting dashboard
• Code Quality: Pre-commit hooks, automated testing, type safety

Installation

From PyPI (Recommended)

pip install dmps==0.2.0

From Source

# Clone the repository
git clone https://github.com/MrBinnacle/dmps.git
cd dmps

# Install in development mode
pip install -e .

Prerequisites

• Python 3.8+
• pip (Python package manager)

Development Installation

1. Clone the repository:

   git clone https://github.com/MrBinnacle/dmps.git
   cd dmps
   

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: .\venv\Scripts\activate
   

3. Install the package in development mode:

   pip install -e .
   

Usage

Quick Start

from dmps import optimize_prompt

# Simple optimization with automatic security validation
result = optimize_prompt("Write a story about AI")
print(result)

Advanced Usage

from dmps import PromptOptimizer

optimizer = PromptOptimizer()
result, validation = optimizer.optimize(
    "Explain machine learning",
    mode="conversational",
    platform="claude"
)

# Check for security warnings
if validation.warnings:
    print("Security warnings:", validation.warnings)

# Access optimization metadata
print(f"Token reduction: {result.metadata['token_metrics']['token_reduction']}")
print(f"Quality score: {result.metadata['evaluation']['overall_score']}")
print(result.optimized_prompt)

Token Tracking & Observability

from dmps.observability import dashboard
from dmps.token_tracker import token_tracker

# Monitor performance
dashboard.print_session_summary()

# Export metrics
dashboard.export_metrics("metrics.json")

# Get performance alerts
alerts = dashboard.get_performance_alerts()
for alert in alerts:
    print(f"Alert: {alert}")

🛡️ Enterprise Security (v0.2.0)

DMPS includes comprehensive security protections:

• CWE-22 Protection: Path traversal attack prevention
• Input Sanitization: XSS and code injection prevention
• RBAC Authorization: Role-based access control for all operations
• Rate Limiting: Protection against abuse and DoS attacks
• Secure Error Handling: Information leak prevention
• Audit Logging: Complete security event tracking
• Token Validation: Secure API token management

Security Compliance: Follows OWASP guidelines and enterprise security standards.

CLI Usage

# Basic usage with automatic security validation
dmps "Your prompt here" --mode conversational --platform claude

# File input/output (automatically validates paths)
dmps --file input.txt --output results.txt

# Interactive mode with security monitoring
dmps --interactive

# REPL shell mode with RBAC protection
dmps --shell

# Show performance metrics
dmps "Optimize this" --metrics

# Export metrics to file
dmps "Test prompt" --export-metrics metrics.json

# Help
dmps --help

Security Features:

• Automatic path traversal protection
• Input sanitization and validation
• RBAC-controlled command access
• Rate limiting and session management
• Secure file operations with extension validation

Development

Setup Development Environment

# Install development tools and pre-commit hooks
python setup-dev.py

# Run quality checks
python scripts/format.py

Running Tests

python -m pytest tests/ -v

Code Quality

# Automated formatting and linting
black src/
isort src/
flake8 src/
mypy src/

# Security scanning
bandit -r src/
safety check

Quality Guardrails

• Pre-commit hooks: Automatic code quality validation
• CI/CD pipeline: Automated testing and security scanning
• Type checking: Full mypy integration
• Security scanning: Bandit and safety checks

See DEVELOPMENT.md for complete guidelines.

What's New in v0.2.0

• Enterprise Security: Complete security hardening with CWE-22 protection
• Token Tracking: Cost estimation and usage monitoring
• Observability Dashboard: Real-time performance monitoring
• Code Quality Guardrails: Pre-commit hooks and automated validation
• Enhanced Performance: 3-5x improvement in pattern matching
• Type Safety: Full mypy integration and type annotations

See CHANGELOG.md for complete release notes.

Contributing

Contributions are welcome! Please read DEVELOPMENT.md for guidelines.

1. Fork the repository
2. Create a feature branch
3. Run quality checks: python scripts/format.py
4. Submit a Pull Request

All contributions must pass security and quality checks.

Links

• PyPI: https://pypi.org/project/dmps/
• GitHub: https://github.com/MrBinnacle/dmps
• Documentation: docs/
• Security Guide: docs/SECURITY_GUIDE.md
• Development Guide: DEVELOPMENT.md

License

This project is licensed under the MIT License - see the LICENSE file for details.

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DMPS v0.2.0 - Enterprise-grade AI prompt optimization with comprehensive security and observability.