A comprehensive AI optimization assessment framework with 149 atomic factors across 8 strategic pillars, including critical AI bot access configuration. The MASTERY-AI Framework provides organizations with production-ready tools to assess, optimize, and improve their content and infrastructure for AI system discovery and performance.

# Install via pip
pip install mastery-ai

# Or clone and install from source
git clone https://github.com/TheWayWithin/mastery-ai-framework.git
cd mastery-ai-framework
pip install -e .

from mastery_ai import AssessmentEngine, AssessmentInput

# Initialize the assessment engine
engine = AssessmentEngine()

# Create input data
input_data = AssessmentInput(
    url="https://example.com",
    content={"title": "Example Content", "body": "..."},
    technical_data={"mcp_status": "implemented"}
)

# Run assessment
result = engine.assess(input_data)

# Display results
print(f"Overall Score: {result.overall_score:.1f}/100")
for pillar, score in result.pillar_scores.items():
    print(f"{pillar}: {score:.1f}/100")

The framework consists of 8 weighted pillars forming the MASTERY acronym:

Total: 149 atomic factors | 100% combined weight

mastery_ai/
├── core/                 # Core assessment engine
│   ├── assessment_engine.py
│   ├── config.py
│   ├── schema.py
│   └── scoring.py
├── pillars/             # Individual pillar implementations
│   ├── ai_response.py   # AI optimization (23.7%)
│   ├── authority.py     # Authority signals (17.8%)
│   └── ...             # Other pillars
├── reporting/           # Report generation
└── api/                # RESTful API

• ✅ Comprehensive Assessment: All 149 atomic factors evaluated
• ✅ AI Bot Access Control: Robots.txt configuration assessment
• ✅ Weighted Scoring: Mathematically precise scoring (weights = 100%)
• ✅ Modular Architecture: Use complete framework or individual pillars
• ✅ RESTful API: Full API access to all capabilities
• ✅ Custom Configuration: Flexible weighting and parameters

• Assessment Time: <30 seconds typical
• Memory Usage: <512MB runtime
• Installation Time: <5 minutes
• Test Coverage: 95%+ target
• Platform Support: Linux, macOS, Windows

from mastery_ai import Config, AssessmentEngine

# Create custom configuration
config = Config()
config.scoring.custom_weights = {
    "AI": 30.0,  # Increase AI pillar weight
    "A": 20.0,
    "M": 15.0,
    "S": 15.0,
    "E": 8.0,
    "T": 6.0,
    "R": 4.0,
    "Y": 2.0
}

# Use custom config
engine = AssessmentEngine(config)

from mastery_ai import AssessmentEngine, PillarType

engine = AssessmentEngine()

# Assess only AI Response pillar
ai_result = engine.assess_pillar(PillarType.AI, input_data)
print(f"AI Pillar Score: {ai_result['score']:.1f}/100")

# Generate different report formats
json_report = engine.generate_report(result, format="json")
html_report = engine.generate_report(result, format="html")
markdown_report = engine.generate_report(result, format="markdown")

# Save results
engine.save_result(result, Path("assessment_results.json"))

# Start the API server
mastery-ai serve --host 0.0.0.0 --port 8000

# Or with Docker
docker run -p 8000:8000 mastery-ai/framework

# Run assessment via API
curl -X POST http://localhost:8000/assess \
  -H "Content-Type: application/json" \
  -d '{"url": "https://example.com"}'

• v3.2.0 (Current): AI Bot Access Control Edition

  • Added AI Bot Access Configuration (M.5.3)
  • Robots.txt allowlisting for OAI-SearchBot and GPTBot
  • 149 total factors (+1 for bot access control)
  • Rebalanced weights for enhanced Machine Readability

• v3.1.1: Enhanced Content Accessibility Edition

  • Added LLMs.txt support (M.5 sub-pillar)
  • 148 total factors (+2 for content accessibility)
  • MCP protocol integration

• v3.1.0: Model Context Protocol Update

• v3.0.0: Major framework revision

• v2.1.0: Extended factor set

• v2.0.0: Initial public release

# Run tests
pytest

# With coverage
pytest --cov=mastery_ai --cov-report=html

# Run specific test suite
pytest tests/test_pillars/

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

# Clone repository
git clone https://github.com/TheWayWithin/mastery-ai-framework.git
cd mastery-ai-framework

# Install development dependencies
pip install -r requirements-dev.txt

# Install in development mode
pip install -e .

# Run tests
pytest

• Full Documentation
• API Reference
• Implementation Guides
• Framework Specification

• Enterprise SEO: Optimize content for AI discovery
• Content Strategy: Improve AI system responses
• Technical Audits: Assess AI readiness
• Competitive Analysis: Benchmark against competitors
• Consulting Services: AI optimization assessments

Target metrics for production deployments:

• Installation success rate: >95%
• Deployment time: <5 minutes
• Assessment execution: <30 seconds
• Memory usage: <512MB
• Test coverage: >95%
• User satisfaction: >4.5/5

• Encrypted data transmission
• Configurable access controls
• Audit logging
• GDPR compliant
• No data retention by default

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

• Based on the MASTERY-AI Framework v3.2 specification
• Inspired by agent-11 repository patterns
• Community contributors and testers

• GitHub: https://github.com/TheWayWithin/mastery-ai-framework
• Issues: GitHub Issues

MASTERY-AI Framework - Comprehensive AI Optimization Assessment Transform your content and infrastructure for optimal AI system performance