🧠 CIP-Core: Cognition Index Protocol Implementation

AI-Enhanced Repository Automation for Scientific Computing

---

📢 Project Status Update (October 2025)
Due to increased work commitments over the next 2 months, all roadmaps are temporarily paused. The project is not frozen—work continues behind the scenes—but updates and documentation will be delayed. Expected to resume regular updates in December 2025. Thank you for your patience and continued interest.

---

🎯 Overview

CIP-Core is the foundational implementation of the Cognition Index Protocol (CIP), providing AI-enhanced automation for repository management, validation, and cross-ecosystem navigation. Originally designed for the Dawn Field Theory ecosystem, CIP-Core enables reproducible scientific computing through intelligent metadata generation and protocol-driven validation.

✨ Key Features

• 🤖 AI-Enhanced Metadata: Intelligent directory descriptions using local Ollama integration
• � Comprehensive Validation: Schema compliance, quality checks, and ecosystem link validation
• 📊 Quality Assurance: Automated detection of generic content and meaningful description requirements
• 🗺️ Cross-Repository Navigation: Seamless linking and discovery with repo:// URL scheme
• 🛠️ Developer CLI: Complete command-line toolkit for repository automation
• 📋 AI Instruction Generation: Automated guidance files for AI agents and repository understanding
• � Ecosystem Integration: Multi-repository workflows and dependency management

🚀 Quick Start

Installation

# Install from source (development)
git clone https://github.com/dawnfield-institute/cip-core.git
cd cip-core
pip install -e .

# Verify installation
cip --version

# Alternative: Run directly without installation
python -m cip_core.cli.main --version

Basic Usage

# Initialize CIP metadata in your repository
cip init --type protocol --title "My Project"

# Generate AI-enhanced metadata for all directories
cip ai-metadata --force --model codellama:latest

# Generate AI instruction files
cip generate-instructions --validate

# Validate repository compliance
cip validate

# Bootstrap complete CIP automation
cip bootstrap --type sdk

AI Enhancement Workflow

# Test AI integration
cip ai-enhance --test-only

# Generate meaningful metadata (replaces generic descriptions)
cip ai-metadata --force --path ./my-repository

# Create navigation instructions for AI agents
cip generate-instructions --validate

# Validate quality and compliance
cip validate --format json

📚 Documentation

📖 Getting Started

• Quick Start Guide - Overview of documentation structure
• Technical Documentation - Implementation details and API reference
• Integration Guide - Add CIP to existing projects

🏛️ Architecture & Design

• Architecture Documentation - Protocol specifications and design
• GPT Integration - AI integration patterns and prompts
• Case Studies - Real-world implementation examples

🚦 Planning & Roadmap

• Development Roadmap - Planned features and timeline
• TODO List - Current development priorities
• Known Limitations - Current constraints and workarounds

🏗️ Project Structure

cip-core/
├── .cip/                 # 🎯 CIP protocol configuration
│   ├── meta.yaml         #    📋 Repository metadata
│   ├── core.yaml         #    🗺️ Directory index
│   └── instructions_v2.0.yaml # 📖 AI agent instructions
├── cip_core/              # 🐍 Core Python package
│   ├── schemas/           #    📋 YAML schema validation
│   ├── validators/        #    ✅ Compliance checking
│   ├── automation/        #    🤖 AI-enhanced metadata generation
│   ├── navigation/        #    🗺️ Cross-repository linking
│   ├── instructions/      #    📖 AI instruction generation
│   ├── ollama_local/      #    🤖 Local AI integration
│   ├── vm/               #    ☁️ Cloud VM service integration
│   ├── cli/              #    🛠️ Command-line interface
│   └── utils/            #    🔧 Common utilities
├── docs/                 # 📚 Comprehensive documentation
│   ├── architecture/     #    🏛️ Protocol specifications
│   ├── technical/        #    🔧 Implementation guides
│   ├── case_studies/     #    📝 Real-world examples
│   ├── gpt/             #    🤖 AI integration guides
│   └── limitations/      #    ⚠️ Known constraints
├── roadmap/             # 🗺️ Development planning
├── todo/                # 📋 Current priorities
├── templates/           # 📄 Configuration templates
└── tests/              # 🧪 Test suite

🛠️ CLI Commands

Note: The cip command requires installation via pip install -e . first. Alternatively, use python -m cip_core.cli.main to run commands directly.

Core Operations

cip init                    # Initialize repository with CIP metadata
cip validate               # Check repository compliance
cip bootstrap              # Complete CIP setup automation

AI-Enhanced Features

cip ai-metadata           # Generate intelligent descriptions
cip ai-enhance            # Full AI enhancement workflow
cip generate-instructions # Create AI guidance files

Repository Navigation

cip resolve               # Resolve repo:// URLs to content
cip list-repos           # List ecosystem repositories
cip validate-links       # Check cross-repository links

Development Tools

cip generate-metadata    # Rule-based metadata generation
cip vm trigger           # Cloud AI analysis (planned)
cip vm status           # Check analysis job status

🤝 Contributing

This repository is currently in private development. Once stabilized, we'll welcome community contributions following the Dawn Field Theory governance model.

📄 License

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

Why MIT? CIP-Core is designed as infrastructure for the broader scientific computing ecosystem. The MIT license ensures maximum compatibility and adoption across academic, commercial, and open source projects.

---

Part of the Dawn Field Theory ecosystem