Content Core

Content Core is a powerful, AI-powered content extraction and processing platform that transforms any source into clean, structured content. Extract text from websites, transcribe videos, process documents, and generate AI summaries—all through a unified interface with multiple integration options.

🚀 What You Can Do

Extract content from anywhere:

• 📄 Documents - PDF, Word, PowerPoint, Excel, Markdown, HTML, EPUB
• 🎥 Media - Videos (MP4, AVI, MOV) with automatic transcription
• 🎵 Audio - MP3, WAV, M4A with speech-to-text conversion
• 🌐 Web - Any URL with intelligent content extraction
• 🖼️ Images - JPG, PNG, TIFF with OCR text recognition
• 📦 Archives - ZIP, TAR, GZ with content analysis

Process with AI:

• ✨ Clean & format extracted content automatically
• 📝 Generate summaries with customizable styles (bullet points, executive summary, etc.)
• 🎯 Context-aware processing - explain to a child, technical summary, action items
• 🔄 Smart engine selection - automatically chooses the best extraction method

🛠️ Multiple Ways to Use

🖥️ Command Line (Zero Install)

# Extract content from any source
uvx --from "content-core" ccore https://example.com
uvx --from "content-core" ccore document.pdf

# Generate AI summaries  
uvx --from "content-core" csum video.mp4 --context "bullet points"

🤖 Claude Desktop Integration

One-click setup with Model Context Protocol (MCP) - extract content directly in Claude conversations.

🔍 Raycast Extension

Smart auto-detection commands:

• Extract Content - Full interface with format options
• Summarize Content - 9 summary styles available
• Quick Extract - Instant clipboard extraction

🖱️ macOS Right-Click Integration

Right-click any file in Finder → Services → Extract or Summarize content instantly.

🐍 Python Library

import content_core as cc

# Extract from any source
result = await cc.extract("https://example.com/article")
summary = await cc.summarize_content(result, context="explain to a child")

⚡ Key Features

• 🎯 Intelligent Auto-Detection: Automatically selects the best extraction method based on content type and available services
• 🔧 Smart Engine Selection:
  • URLs: Firecrawl → Jina → Crawl4AI (optional) → BeautifulSoup fallback chain
  • Documents: Docling → Enhanced PyMuPDF → Simple extraction fallback
  • Media: OpenAI Whisper transcription
  • Images: OCR with multiple engine support
• 📊 Enhanced PDF Processing: Advanced PyMuPDF engine with quality flags, table detection, and optional OCR for mathematical formulas
• 🌍 Multiple Integrations: CLI, Python library, MCP server, Raycast extension, macOS Services
• ⚡ Zero-Install Options: Use uvx for instant access without installation
• 🧠 AI-Powered Processing: LLM integration for content cleaning and summarization
• 🔄 Asynchronous: Built with asyncio for efficient processing
• 🐍 Pure Python Implementation: No system dependencies required - simplified installation across all platforms

Getting Started

Installation

Install Content Core using pip - no system dependencies required!

# Basic installation (PyMuPDF + BeautifulSoup/Jina extraction)
pip install content-core

# With enhanced document processing (adds Docling)
pip install content-core[docling]

# With local browser-based URL extraction (adds Crawl4AI)
# Note: Requires Playwright browsers (~300MB). Run:
pip install content-core[crawl4ai]
python -m playwright install --with-deps

# Full installation (with all optional features)
pip install content-core[docling,crawl4ai]

Note: The core installation uses pure Python implementations and doesn't require system libraries like libmagic, ensuring consistent, hassle-free installation across Windows, macOS, and Linux. Optional features like Crawl4AI (browser automation) may require additional system dependencies.

Alternatively, if you’re developing locally:

# Clone the repository
git clone https://github.com/lfnovo/content-core
cd content-core

# Install with uv
uv sync

Command-Line Interface

Content Core provides three CLI commands for extracting, cleaning, and summarizing content: ccore, cclean, and csum. These commands support input from text, URLs, files, or piped data (e.g., via cat file | command).

Zero-install usage with uvx:

# Extract content
uvx --from "content-core" ccore https://example.com

# Clean content  
uvx --from "content-core" cclean "messy content"

# Summarize content
uvx --from "content-core" csum "long text" --context "bullet points"

ccore - Extract Content

Extracts content from text, URLs, or files, with optional formatting. Usage:

ccore [-f|--format xml|json|text] [-d|--debug] [content]

Options:

• -f, --format: Output format (xml, json, or text). Default: text.
• -d, --debug: Enable debug logging.
• content: Input content (text, URL, or file path). If omitted, reads from stdin.

Examples:

# Extract from a URL as text
ccore https://example.com

# Extract from a file as JSON
ccore -f json document.pdf

# Extract from piped text as XML
echo "Sample text" | ccore --format xml

cclean - Clean Content

Cleans content by removing unnecessary formatting, spaces, or artifacts. Accepts text, JSON, XML input, URLs, or file paths. Usage:

cclean [-d|--debug] [content]

Options:

• -d, --debug: Enable debug logging.
• content: Input content to clean (text, URL, file path, JSON, or XML). If omitted, reads from stdin.

Examples:

# Clean a text string
cclean "  messy   text   "

# Clean piped JSON
echo '{"content": "  messy   text   "}' | cclean

# Clean content from a URL
cclean https://example.com

# Clean a file’s content
cclean document.txt

csum - Summarize Content

Summarizes content with an optional context to guide the summary style. Accepts text, JSON, XML input, URLs, or file paths.

Usage:

csum [--context "context text"] [-d|--debug] [content]

Options:

• --context: Context for summarization (e.g., "explain to a child"). Default: none.
• -d, --debug: Enable debug logging.
• content: Input content to summarize (text, URL, file path, JSON, or XML). If omitted, reads from stdin.

Examples:

# Summarize text
csum "AI is transforming industries."

# Summarize with context
csum --context "in bullet points" "AI is transforming industries."

# Summarize piped content
cat article.txt | csum --context "one sentence"

# Summarize content from URL
csum https://example.com

# Summarize a file's content
csum document.txt

Quick Start

You can quickly integrate content-core into your Python projects to extract, clean, and summarize content from various sources.

import content_core as cc

# Extract content from a URL, file, or text
result = await cc.extract("https://example.com/article")

# Clean messy content
cleaned_text = await cc.clean("...messy text with [brackets] and extra spaces...")

# Summarize content with optional context
summary = await cc.summarize_content("long article text", context="explain to a child")

# Extract audio with custom speech-to-text model
from content_core.common import ProcessSourceInput
result = await cc.extract(ProcessSourceInput(
    file_path="interview.mp3",
    audio_provider="openai",
    audio_model="whisper-1"
))

Documentation

For more information on how to use the Content Core library, including details on AI model configuration and customization, refer to our Usage Documentation.

MCP Server Integration

Content Core includes a Model Context Protocol (MCP) server that enables seamless integration with Claude Desktop and other MCP-compatible applications. The MCP server exposes Content Core's powerful extraction capabilities through a standardized protocol.

<a href="https://glama.ai/mcp/servers/@lfnovo/content-core"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@lfnovo/content-core/badge" /> </a>

Quick Setup with Claude Desktop

# Install Content Core (MCP server included)
pip install content-core

# Or use directly with uvx (no installation required)
uvx --from "content-core" content-core-mcp

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "content-core": {
      "command": "uvx",
      "args": [
        "--from",
        "content-core",
        "content-core-mcp"
      ]
    }
  }
}

For detailed setup instructions, configuration options, and usage examples, see our MCP Documentation.

Enhanced PDF Processing

Content Core features an optimized PyMuPDF extraction engine with significant improvements for scientific documents and complex PDFs.

Key Improvements

• 🔬 Mathematical Formula Extraction: E

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