WILL System - skenai/WILL GitHub Wiki

version: 2.0.0 date: 2025-03-04 type: system-doc status: public tags: [william, will, system] 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: []

WILL System Overview

WILL v2.0.0 implements a clean, three-tier architecture following the Market Coordination Protocol (MCP) standard.

Core Components

1. Proposal Validation Framework

Key Features:
- Track validation (GFORCE)
- Level assessment
- Security classification
- Quality metrics
- Risk evaluation

2. XP Integration

Mechanics:
- Track multipliers
- Level progression
- Quality-based rewards
- Early allocation bonus (1.5x)
- Weekly decay (2%)

3. Security Levels

Classifications:
- PUBLIC: General improvements
- CLASSIFIED: Sensitive changes
- BROKEN_ARROW: Critical updates

4. Parallel Systems Architecture

Core Systems:
  GEN (Genesis):
    - Base protocol implementation
    - Core value mechanics
    - Foundational patterns
    - Stability assurance
  
  EVO (Evolution):
    - Protocol evolution
    - Dynamic adaptation
    - Pattern emergence
    - Natural selection

Integration:
  - Dual token economics
  - Cross-system validation
  - Natural flow mechanics
  - Quality-driven evolution

Technical Implementation

1. Validation Pipeline

interface ProposalData {
  track: string;
  level: string;
  sequence: string;
  name: string;
  title: string;
  content: string;
  status: 'DRAFT';
  priority: 'MEDIUM' | 'HIGH' | 'CRITICAL' | 'LOW';
  securityLevel: 'PUBLIC' | 'CLASSIFIED' | 'BROKEN_ARROW';
  qualityMetrics?: {
    structure: number;
    content: number;
    impact: number;
    innovation: number;
  };
}

2. Quality Assessment

Metrics:
  Structure: 15%
  Content: 15%
  Impact: 35%
  Innovation: 35%

Requirements:
  - Clear objectives
  - Defined scope
  - Timeline
  - Risk assessment

3. Integration Points

Systems:
  - XP Management
  - Token System
  - Pattern Recognition
  - GitHub API
  - Edge Runtime

4. AI Integration

Core Components:
  Pattern Recognition:
    - Flow analysis
    - Quality assessment
    - Natural emergence detection
    - Adaptation triggers

  Dynamic Optimization:
    - Route efficiency
    - Value distribution
    - Quality maintenance
    - System evolution

Integration Points:
  - Quality validation gates
  - Pattern-based routing
  - Natural flow detection
  - System adaptation

5. Quality Framework

Validation Gates:
  Technical:
    - Code quality
    - System integration
    - Performance metrics
    - Security standards

  Economic:
    - Value stability
    - Flow efficiency
    - Market dynamics
    - Risk assessment

  Evolution:
    - Pattern emergence
    - System adaptation
    - Natural selection
    - Growth metrics

User Interaction

1. Proposal Creation

  • Guided interview process
  • Template-based structure
  • Real-time validation
  • Automatic track detection

2. Review Process

  • Quality metrics assessment
  • Security level verification
  • Dependency analysis
  • Impact evaluation

3. Deployment Checks

Validation Areas:
  - Infrastructure requirements
  - Security implications
  - CI/CD pipeline
  - Resource allocation

Best Practices

1. Proposal Development

  • Use templates
  • Follow GFORCE framework
  • Include measurable objectives
  • Document dependencies

2. Quality Assurance

  • Run pre-validation checks
  • Address all requirements
  • Test integrations
  • Review security implications

3. Community Engagement

  • Share drafts early
  • Gather feedback
  • Iterate based on input
  • Document changes

Related Documentation

Recent Updates

February 2025

  1. Enhanced validation system documentation
  2. Added non-technical user guide
  3. Updated XP decay mechanics
  4. Improved proposal templates

Future Development

Planned Improvements

  1. Automated quality assessment
  2. Enhanced security validation
  3. Integrated testing framework
  4. Expanded templates library

Community Requests

  1. Simplified validation process
  2. More example proposals
  3. Better error messages
  4. Interactive tutorials

Public-Facing Documentation

WILL System

Overview

WILLIAM (Wise Intelligent Learning Interface Advancing Market) represents a breakthrough in market intelligence, combining natural pattern recognition with advanced resource optimization to create a self-evolving market ecosystem.

Core Components

1. Market Intelligence
  • Pattern recognition
  • Signal processing
  • Value discovery
  • Resource optimization
2. Natural Evolution
  • Pattern formation
  • Value emergence
  • System adaptation
  • Market growth
3. Resource Management
  • Dynamic allocation
  • Efficiency optimization
  • Value maximization
  • System stability

Implementation Framework

1. Market Analysis
class MarketAnalyzer:
    def analyze(self, market_data):
        """Market analysis through:
        1. Pattern recognition
        2. Signal processing
        3. Value discovery"""
        pass
2. Resource Optimization
class ResourceOptimizer:
    def optimize(self, resources):
        """Resource optimization through:
        1. Dynamic allocation
        2. Efficiency maximization
        3. Value creation"""
        pass
3. System Evolution
class SystemEvolution:
    def evolve(self, state):
        """System evolution through:
        1. Pattern adaptation
        2. Resource optimization
        3. Value growth"""
        pass

Quality Framework

1. Market Quality
  • Signal accuracy
  • Pattern reliability
  • Value validation
  • Resource efficiency
2. System Quality
  • Evolution metrics
  • Adaptation success
  • Growth indicators
  • Stability measures
3. Value Quality
  • Creation efficiency
  • Market validation
  • Resource optimization
  • System benefit

Natural Growth

1. Pattern Development
  • Enhanced detection
  • Better processing
  • Improved validation
  • Value discovery
2. Resource Evolution
  • Dynamic allocation
  • Efficiency improvement
  • Value optimization
  • System stability
3. Market Adaptation
  • Signal refinement
  • Pattern evolution
  • Value creation
  • Natural growth

Future Directions

1. Enhanced Intelligence
  • Better pattern recognition
  • Improved signal processing
  • Advanced optimization
  • Value discovery
2. Market Integration
  • Seamless coordination
  • Resource efficiency
  • Pattern validation
  • Value maximization
3. System Growth
  • Natural evolution
  • Pattern development
  • Value creation
  • Sustainable scaling

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