What is V3 Deep Integration?

It is a Claude Code skill designed to integrate agentic-flow@alpha into your project, replacing parallel code with specialized extensions to improve performance and maintainability.

What are SONA learning modes?

SONA integration provides five specialized learning modes—including real-time, research, and edge—allowing agents to adapt to specific task constraints in as little as 0.05ms.

How much code can this skill help remove?

Based on documented benchmarks, it can eliminate over 10,000 lines of duplicate orchestration code by leveraging shared framework components and the agentic-flow adapter layer.

What performance improvements can I expect?

Users can achieve up to 7.47x speedups via Flash Attention and search performance gains of 150x to 12,500x using AgentDB with HNSW indexing.

Does this skill support backward compatibility?

Yes, it includes a multi-phase migration strategy and dedicated adapter layers to ensure existing functionality remains intact while transitioning to the new architecture.

V3 Deep Agentic Integration

Name: V3 Deep Agentic Integration
Author: ellisapotheosis

byellisapotheosis

Developer Tools

Optimizes Claude-flow architecture by integrating agentic-flow@alpha to eliminate code duplication and boost performance.

About

This skill facilitates the deep integration of the agentic-flow@alpha framework into Project Nyra, following ADR-001. It replaces redundant parallel implementations with specialized extensions, reducing the codebase by over 10,000 lines while introducing advanced features like SONA learning modes, Flash Attention optimization, and HNSW-indexed AgentDB search. It is an essential tool for developers refactoring complex agent systems into high-performance architectures while maintaining full backward compatibility and feature parity.

Key Features

Code deduplication reducing 10,000+ lines of redundant implementations
0 GitHub stars
Flash Attention implementation for up to 7.47x processing speedup
SONA Learning Mode integration with five distinct adaptive states
Automated migration patterns for Swarm and Agent management systems
High-performance AgentDB coordination with HNSW indexing for rapid search

Use Cases

Migrating multi-agent swarm configurations to the agentic-flow@alpha standard
Implementing high-speed vector search and attention mechanisms in Claude-based agents
Refactoring legacy agentic systems to reduce technical debt and maintenance overhead

About

Key Features

Code deduplication reducing 10,000+ lines of redundant implementations
0 GitHub stars
Flash Attention implementation for up to 7.47x processing speedup
SONA Learning Mode integration with five distinct adaptive states
Automated migration patterns for Swarm and Agent management systems
High-performance AgentDB coordination with HNSW indexing for rapid search

Use Cases

Migrating multi-agent swarm configurations to the agentic-flow@alpha standard
Implementing high-speed vector search and attention mechanisms in Claude-based agents
Refactoring legacy agentic systems to reduce technical debt and maintenance overhead