Skyla Documentation

Overview

Skyla is an AI platform that implements a context-aware epistemic gate system with 2-model consensus architecture. The platform combines symbolic reasoning, advanced mathematical model selection, and cryptographic verification to create transparent, auditable AI behavior.

Unlike traditional AI systems that use duplicate models, Skyla implements true architectural diversity by comparing Claude Haiku (efficiency-focused) against Claude Sonnet (quality-focused), providing authentic consensus measurement and quality-based selection.

Multi-Model Consensus System

Status: Currently Implemented - This is the core innovation of Skyla's current implementation.

True Architectural Diversity

Unlike systems that use duplicate models, Skyla implements genuine consensus measurement between two different Claude architectures with distinct strengths and optimizations.

Advanced Selection Algorithm

The system evaluates responses using sophisticated metrics including adaptive length scoring, nuance detection, efficiency indicators, semantic richness, and structural coherence.

Context-Aware Processing

The system provides context-aware architectural preferences, giving efficiency-focused models advantages for simple queries and quality-focused models advantages for complex analytical tasks.

Dynamic Token Allocation

Response length is dynamically adjusted based on input complexity, ensuring optimal resource usage while maintaining response quality.

Integrity Measurement

The system measures genuine divergence between architectures across multiple dimensions including length variance, sentiment analysis, topic consistency, and tone evaluation.

Identity Vector System

The Identity Vector System defines the mathematical foundation of Skyla's state management. This 4-dimensional vector space enables precise, deterministic state transitions while maintaining cryptographic verifiability.

Vector Dimensions

• Cognitive: Analytical thinking and reasoning capabilities
• Emotional: Sentiment and affective response patterns
• Adaptive: Flexibility and learning capability
• Coherence: Consistency and structural integrity

State Transition Mechanics

The system uses three-layer processing to ensure deterministic, mathematically consistent state changes:

• Symbolic Rules: Direct pattern matching for specific commands
• Semantic Patterns: Regex-based categorization with precise mathematical adjustments
• Hash Fallback: Deterministic micro-adjustments for unknown inputs

Mode Transitions

The system determines operational modes based on the dominant dimension values, transitioning between analytical, creative, adaptive, and coherent modes as needed. All transitions generate cryptographic proofs for verification.

Key Features

Context-Aware Epistemic Gate

Revolutionary context analysis system that prevents unnecessary clarification requests by analyzing conversation history and resolving ambiguous references automatically.

Session Memory Management

Maintains conversational context across interactions with smart context extraction that identifies recent topics, problems, and technologies mentioned in conversations.

Mathematical Quality Assessment

Sophisticated 5-factor quality scoring system that evaluates optimal response length, nuance detection, efficiency indicators, semantic richness, and structural coherence.

Query Complexity Analysis

Automatic categorization of input complexity with dynamic weighting that favors nuance for complex queries and efficiency for simple interactions.

API Interface

Primary Endpoint

Skyla provides a working API endpoint for multi-model consensus processing with context-aware epistemic analysis.

Response Format

API responses include the selected model response, integrity measurements, consensus strength, divergence metrics, and contextual analysis. The system provides clear feedback about why specific models were selected and how confident the system is in the response quality.

Identity Vector Context

The server uses the identity vector for contextual awareness only - all vector modifications happen client-side, ensuring the system maintains transparency while providing contextual understanding.

FAQ

What's actually implemented vs conceptual?

Currently Working: True 2-model consensus system with Claude Haiku vs Sonnet, advanced quality scoring, architectural selection, context memory, and integrity measurement. Conceptual: Full cryptographic verification, multi-agent systems / epistemic telemetry coordination.

How does the consensus system work?

Skyla sends each query to both Claude Haiku and Sonnet simultaneously, measures their response divergence across 4 dimensions, then selects the best response using a 5-factor quality scoring algorithm that considers query complexity and architectural strengths.

What makes this different from other AI systems?

Most AI systems use single models or duplicate models for redundancy. Skyla uses genuinely different architectures (efficiency vs quality focused) and measures real consensus between them, providing transparency into AI decision-making that doesn't exist elsewhere.

How does the context-aware epistemic gate work?

Instead of asking for clarification every time there's any ambiguity, Skyla analyzes conversation history to resolve pronouns and contextual references. It only requests clarification when there's genuine ambiguity that can't be resolved from context.

Is this ready for production use?

The core consensus system, identity vector management, and context analysis are fully functional and ready for use. Full cryptographic verification and multi-agent coordination features are still in development.