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:
| Pillar | Name | Weight | Factors | Focus |
|---|---|---|---|---|
| AI | AI Response Optimization & Citation | 23.7% | 23 | Core AI system optimization, MCP integration |
| A | Authority & Trust Signals | 17.8% | 15 | Credibility and trust indicators |
| M | Machine Readability & Technical Infrastructure | 15.0% | 22 | Technical implementation, LLMs.txt, robots.txt |
| S | Semantic Content Quality | 13.8% | 22 | Content depth and semantic richness |
| E | Engagement & User Experience | 10.9% | 19 | User experience signals |
| T | Topical Expertise & Experience | 8.9% | 14 | Expertise demonstration |
| R | Reference Networks & Citations | 5.9% | 19 | External validation |
| Y | Yield Optimization & Freshness | 4.0% | 15 | Continuous optimization |
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- 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