Three Stage Architecture - skenai/WILL GitHub Wiki

version: 2.0.0 date: 2025-03-04 type: architecture-doc status: public tags: [william, three, stage, architecture] related: [] changelog:

version: 2.0.0 date: 2025-03-04 changes:
- "MAJOR: Switch to YAML frontmatter"
- "MAJOR: Enhanced metadata structure" references: []
version: 1.0.0 date: 2025-03-03 changes:
- "MAJOR: Initial documentation" references: []

Three-Stage Architecture

Overview

WILLIAM's SKENAI system implements a three-stage sequencer architecture designed for maximum security, quality control, and efficient deployment. This architecture ensures that all content flows through appropriate validation and quality gates before reaching production.

Stage 1: SKENAI (First Hopper)

Purpose

Primary collection and aggregation layer
Handles "mashy bits" and initial intake
First-stage validation and processing

Key Features

Raw content intake system
Initial processing pipeline
Basic validation checks
Content categorization
Preliminary security scanning

Stage 2: SKENAI-Q (Staging)

Purpose

Intermediate processing and validation
Quality control and content refinement
Proposal staging and review
Security protocol enforcement

Key Components

Proposal validation system (100+ proposals)
Quality metrics assessment
Security protocol checks
Staging environment
Test suite execution

Validation Requirements

Strict proposal format checking
Metadata completeness verification
Content structure validation
Security compliance checks
Quality metrics assessment
Cross-reference validation

Stage 3: SKENAI-R (Release)

Purpose

Final deployment stage
Production-ready content
Official release and distribution
Public access management

Key Features

Production environment
Release management
Version control
Public documentation
Access control

Content Flow

Initial Intake (SKENAI)
- Raw content submission
- Initial processing of "mashy bits"
- Basic validation checks
- Content categorization
Quality Control (SKENAI-Q)
- Detailed proposal validation
- Security protocol enforcement
- Quality metrics assessment
- Staging verification
- Cross-reference checking
Production Release (SKENAI-R)
- Final verification
- Production deployment
- Public distribution
- Access management
- Documentation updates

Security Integration

Progressive security implementation across stages:

Stage 1: Basic security scanning
Stage 2: OMEGA_BLACK protocols
Stage 3: Production security measures

Quality Assurance

Quality checks follow WILLIAM's standards:

Stage 1: Initial validation
Stage 2: Comprehensive quality assessment
Stage 3: Final verification

Benefits

Clear separation of concerns
Structured content flow
Progressive refinement pipeline
Better quality control gates
Organized staging process
Alignment with CI/CD practices

This architecture maintains WILLIAM's unique requirements for content processing and governance while providing a robust framework for system evolution.

Core Architecture

1. Recognition Stage

The first stage focuses on pattern recognition and initial signal processing:

Components

Market signal detection
Pattern identification
Value indicators
Trend analysis

Processing Flow

Signal intake
Pattern matching
Initial validation
Value assessment

2. Quality Stage

The second stage ensures signal quality and pattern validation:

Components

Signal verification
Pattern validation
Quality metrics
Value confirmation

Processing Flow

Deep validation
Pattern refinement
Quality assessment
Value crystallization

3. Production Stage

The final stage optimizes and deploys validated patterns:

Components

Pattern optimization
Signal coordination
Value maximization
Market integration

Processing Flow

Final validation
Resource optimization
Market coordination
Value deployment

Implementation Framework

1. Signal Processing

class SignalProcessor:
    def process(self, market_data):
        """Three-stage signal processing:
        1. Pattern recognition
        2. Quality validation
        3. Market optimization"""
        pass

2. Pattern Validation

class PatternValidator:
    def validate(self, pattern):
        """Pattern validation through:
        1. Signal verification
        2. Quality assessment
        3. Value confirmation"""
        pass

3. Market Integration

class MarketIntegrator:
    def integrate(self, validated_pattern):
        """Market integration through:
        1. Resource optimization
        2. Signal coordination
        3. Value maximization"""
        pass

Quality Control

1. Signal Quality

Accuracy metrics
Reliability measures
Consistency checks
Value indicators

2. Pattern Quality

Formation integrity
Validation scores
Stability measures
Value metrics

3. Market Quality

Integration success
Resource efficiency
Value creation
System stability

System Evolution

1. Recognition Evolution

Enhanced detection
Better processing
Improved validation
Value discovery

2. Quality Evolution

Advanced metrics
Deeper validation
Stronger patterns
Value confirmation

3. Production Evolution

Optimal deployment
Market efficiency
Value maximization
System growth

Future Directions

1. Enhanced Processing

Better signal detection
Improved validation
Advanced optimization
Value creation

2. Quality Improvements

Enhanced metrics
Stronger validation
Pattern stability
Value assurance

3. Market Integration

Seamless deployment
Resource efficiency
Pattern optimization
Value maximization

Integration with NATURAL Framework

Clean repository separation
Natural pipeline flow
Validator protection
Interface standards

Pipeline API Integration

/pipeline/submit - Entry point
/pipeline/validate - Basic checks
/pipeline/analyze - Efficiency (Q.1)
/pipeline/patterns - Recognition (Q.2)
/pipeline/status - State checks
/pipeline/vote - Governance

Integration with Three-Graph Lattice

Technical graph validation
Economic resource optimization
Quality metrics tracking