context-crystallizer

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

context-crystallizer

★ 16 Open GitHub ↗

Transform your large repository into crystallized, AI-consumable knowledge. Enable AI agents to work with enterprise-scale projects efficiently.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Context Crystallizer 💎

**AI Context Engineering for Large Repositories**

Transform massive repositories into crystallized, AI-consumable knowledge through systematic analysis and optimization.

## What is Crystallization?

Just as pressure transforms carbon into diamonds, Context Crystallizer applies systematic AI analysis to transform raw repositories into **crystallized knowledge** - structured, searchable, and optimized for AI consumption. Each file is analyzed to extract its purpose, key APIs, patterns, and relationships, creating a knowledge base that AI can efficiently search and understand.

## Inspiration

Context Crystallizer was inspired by [AI Distiller (aid)](https://github.com/janreges/ai-distiller), which pioneered the concept of intelligently extracting essential information from large codebases for AI consumption. While AI Distiller focuses on extracting public APIs and type information using tree-sitter parsers, Context Crystallizer takes a complementary approach by having AI agents generate comprehensive crystallized contexts about functionality, patterns, and relationships.

## The Problem 🔥

AI agents hit context length limits when working with large repositories. A typical enterprise repository has 10,000+ files, but LLMs can only process a fraction at once. This forces AI to work blindly or make assumptions about unfamiliar code.

## The Solution ✨

Context Crystallizer creates a **searchable crystallized context base** of AI-optimized knowledge:

- 🔍 **Search by functionality**: "find authentication middleware" 
- ⚡ **Token-efficient**: 5:1 compression ratio (source:crystallized context)
- 🤖 **AI-optimized format**: Structured specifically for LLM consumption
- 📊 **Smart assembly**: Combines multiple contexts within token limits
- 💎 **Crystallized knowledge**: Preserves essential information in optimized form

## How It Works

**Simple 3-step crystallization process:**

1. **Initialize**: Scan repository and prepare for crystallization
   - Automatically respects `.gitignore` patterns
   - Skips common build directories (node_modules, dist, build, .git)
   - Filters out binary files and very large files (>1MB)
2. **Crystallize**: AI analyzes each file to extract meaningful knowledge  
3. **Search**: Find relevant crystallized contexts for any task

## Quick Demo

**Developer using Claude Code with a large documentation repository:**

> **Developer**: "I need to understand how authentication works in this massive project"
>
> **Claude**: "I'll crystallize this repository first to build a searchable knowledge base, then find all authentication-related information."
>
> *Claude crystallizes the repository - scanning and analyzing each file*
>
> **Claude**: "Crystallization complete! I found 5 files with authentication logic. The main JWT middleware handles token validation with Redis session caching. Here's how it works..."
>
> *Claude provides comprehensive explanation using crystallized contexts*
>
> **Developer**: "What files depend on the authentication system?"
>
> **Claude**: "Let me search for related crystallized contexts..."
>
> *Claude uses find_related_crystallized_contexts() to discover dependencies*

## Installation & Setup

### Quick Start (5 minutes)

```bash
# Install globally via NPM
npm install -g context-crystallizer

# Navigate to your project
cd /path/to/your/project

# Start the MCP server
context-crystallizer
```

### Claude Desktop Integration

Add to your Claude Desktop configuration (`~/claude_desktop_config.json`):

```json
{
  "mcpServers": {
    "context-crystallizer": {
      "command": "npx",
      "args": ["context-crystallizer"],
      "cwd": "/path/to/your/project"
    }
  }
}
```

## Context Crystallizer Tools

Context Crystallizer provides dual access to crystallization functionality: AI agents can use MCP tools for conversation-driven analysis, while developers can use CLI commands for direct control.

### AI Agent Usage (MCP Tools)

AI agents interact with Context Crystallizer through MCP (Model Context Protocol) for conversation-driven crystallization and knowledge search.

| Tool | Purpose | AI Agent Conversation Example |
|------|---------|-------------------------------|
| **get_crystallization_guidance** | Get comprehensive analysis guidance | **Developer**: "How should I analyze these files?" **Claude**: "Let me get the crystallization guidance..." *Claude calls get_crystallization_guidance* **Claude**: "Here's the complete analysis methodology with templates, quality standards, and AI-specific guidance for creating crystallized contexts." |
| **init_crystallization** | Initialize repository crystallization | **Developer**: "Set up this React project for crystallization" **Claude**: "I'll initialize crystallization for your React project" *Claude calls init_crystallization* **Claude**: "✓ Queued 247 files for crystallization (automatically respecting .gitignore). Ready to start analyzing!" |
| **get_next_file_to_crystallize** | Get next file for AI analysis | **Claude**: "Let me get the next file to analyze..." *Claude calls get_next_file_to_crystallize* **Claude**: "Analyzing src/components/Auth.tsx - this appears to be authentication UI logic..." |
| **store_crystallized_context** | Save AI-generated knowledge | **Claude**: "I've analyzed the authentication component. Storing crystallized context..." *Claude calls store_crystallized_context* **Claude**: "✓ Crystallized context stored. Progress: 45/247 files" |
| **get_crystallization_progress** | Monitor crystallization status | **Developer**: "How's the crystallization going?" **Claude**: "Let me check progress..." *Claude calls get_crystallization_progress* **Claude**: "Progress: 45/247 files (18% complete), ~2 hours remaining" |
| **search_crystallized_contexts** | Find relevant knowledge by functionality | **Developer**: "How does authentication work in this app?" **Claude**: "Let me search the crystallized contexts..." *Claude calls search_crystallized_contexts with query="authentication"* **Claude**: "Found 5 auth-related files: JWT middleware, login component, auth context..." |
| **get_crystallized_bundle** | Combine multiple contexts | **Developer**: "Show me how the payment system works" **Claude**: "I'll bundle all payment-related contexts..." *Claude calls get_crystallized_bundle* **Claude**: "The payment flow involves 4 components: PaymentForm, Stripe integration, order processing..." |
| **find_related_crystallized_contexts** | Discover code relationships | **Developer**: "What depends on this Auth.tsx file?" **Claude**: "Let me find related contexts..." *Claude calls find_related_crystallized_contexts* **Claude**: "Found 3 related files: LoginPage uses Auth.tsx, ProtectedRoute depends on it..." |
| **search_by_complexity** | Find contexts by difficulty level | **Developer**: "Show me simple files to understand first" **Claude**: "Finding low-complexity files..." *Claude calls search_by_complexity with complexity="low"* **Claude**: "Here are 8 simple config files and utility functions to start with..." |
| **validate_crystallization_quality** | Assess context quality | **Developer**: "Is the crystallization quality good?" **Claude**: "Let me validate the crystallization quality..." *Claude calls validate_crystallization_quality* **Claude**: "Quality report: 89% completeness, 92% AI readability. Suggestions: Add more error handling patterns for 3 files" |
| **update_crystallized_contexts** | Refresh contexts for changes | **Developer**: "Update crystallization after my changes" **Claude**: "Detecting changed files and updating contexts..." *Claude calls update_crystallized_contexts* **Claude**: "Updated 3 changed files, removed 1 deleted file. Crystallization is current!" |

### CLI Usage (Developer Commands)

Developers can use direct CLI commands for precise control over crystallization operations.

| Command | Purpose | Example | Parameters |
|---------|---------|---------|-----------|
| **guidance** | Get comprehensive analysis guidance | `context-crystallizer guidance` | `--repo-path <path>` (optional) |
| **init** | Initialize repository crystallization | `context-crystallizer init ./my-repo` | `<repo-path>` (required) `--exclude <patterns...>` (optional, adds to .gitignore & defaults) |
| **progress** | Check crystallization progress | `context-crystallizer progress` | `--json` (optional) |
| **search** | Search crystallized contexts | `context-crystallizer search "authentication"` | `<query>` (required) `--max-tokens <number>` `--category <type>` `--json` |
| **bundle** | Bundle multiple contexts | `context-crystallizer bundle src/auth src/api` | `<files...>` (required) `--max-tokens <number>` `--json` |
| **related** | Find related contexts | `context-crystallizer related src/auth.ts` | `<file-path>` (required) `--max-results <number>` `--json` |
| **validate** | Validate crystallization quality | `context-crystallizer validate [file]` | `[file-path]` (optional) `--report` `--json` |
| **update** | Update changed contexts | `context-crystallizer update` | `--force` `--include-unchanged` `--cleanup-deleted` `--check-only` `--report` `--json` |
| **mcp** | Start MCP server | `context-crystallizer mcp` | None |

### Usage Patterns

**🔄 Initial Setup & Crystallization**
```
Developer → Start: context-crystallizer
Developer → Configure: Claude Desktop with MCP
Developer → Request: "Crystallize this repository"
Claude → Calls: init_crystallization, get_next_file_to_crystallize, store_crystallized_context
Claude → Reports: Progress and completion
```

**🔍 Daily Development Workflow**  
```
Developer → Ask: "How does feature X work?"
Claude → Calls: search_crystallized_contexts
Claude → Explains: Using found crystallized knowledge

Developer → Ask: "What will this change affect?"
Claude → Calls: find_related_crystallized_contexts  
Claude → Warns: About potential impacts
```

**🔧 Maintenance & Updates**
```
Developer → Notification: "I changed some files"
Claude → Calls: update_crystallized_contexts
Claude → Reports: "Updated 3 contexts, all current"

Developer → Question: "Is crystallization still good quality?"
Claude → Calls: validate_crystallization_quality
Claude → Reports: Quality metrics and suggestions
```

## CLI & Developer Usage

### Starting the MCP Server

```bash
# Install globally
npm install -g context-crystallizer

# Navigate to your project directory
cd /path/to/your/project

# Start the MCP server (required for AI agent integration)
context-crystallizer
```

The server will start and display:
```
Context Crystallizer MCP server running... Ready to transform repositories into crystallized knowledge!
```

### Integration Options

**1. Claude Desktop Integration**

Add to `~/claude_desktop_config.json`:
```json
{
  "mcpServers": {
    "context-crystallizer": {
      "command": "npx", 
      "args": ["context-crystallizer"],
      "cwd": "/path/to/your/project"
    }
  }
}
```

**2. MCP-Compatible Clients**

Any MCP-compatible client can connect to the server and use the 11 crystallization tools. The server implements the standard MCP protocol for tool discovery and execution.

**3. Direct Development**

For development and testing:
```bash
# Development with hot reload
npm run dev

# Production build
npm run build
npm start

# TypeScript development
npm run dev:mcp
```

### Developer Notes

- **Server Lifecycle**: The MCP server must be running for AI agent integration
- **Project Context**: Always start the server from your project root directory  
- **Persistent Storage**: Crystallized contexts are saved in `.context-crystallizer/` directory
- **File Watching**: Use `update_crystallized_contexts` tool after making code changes
- **Quality Monitoring**: Regular quality validation ensures accurate crystallized knowledge

## Contributing

We welcome contributions! Focus on AI workflow improvements:

- 🐛 **Bug Reports**: Use our issue templates
- 💡 **Feature Requests**: Enhance AI context engineering capabilities  
- 🔧 **Pull Requests**: Include crystallization quality validation tests
- 💬 **Discussions**: Share AI integration patterns

See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed guidelines.

## License

MIT License - see [LICENSE.md](LICENSE.md) for details.

---

**Transform your large repository into crystallized, AI-consumable knowledge. Enable AI agents to work with enterprise-scale projects efficiently.**

⭐ **Star this repo** if Context Crystallizer helps your AI workflows!

context-crystallizer

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README