home / skills / proffesor-for-testing / agentic-qe / v3-integration-deep

This skill helps you reduce code duplication by integrating claude-flow as a specialized extension of agentic-flow@alpha, boosting performance and parity.

npx playbooks add skill proffesor-for-testing/agentic-qe --skill v3-integration-deep

Review the files below or copy the command above to add this skill to your agents.

Files (1)
SKILL.md
7.1 KB
---
name: "V3 Deep Integration"
description: "Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation."
---

# V3 Deep Integration

## What This Skill Does

Transforms claude-flow from parallel implementation to specialized extension of agentic-flow@alpha, eliminating massive code duplication while achieving performance improvements and feature parity.

## Quick Start

```bash
# Initialize deep integration
Task("Integration architecture", "Design agentic-flow@alpha adapter layer", "v3-integration-architect")

# Feature integration (parallel)
Task("SONA integration", "Integrate 5 SONA learning modes", "v3-integration-architect")
Task("Flash Attention", "Implement 2.49x-7.47x speedup", "v3-integration-architect")
Task("AgentDB coordination", "Setup 150x-12,500x search", "v3-integration-architect")
```

## Code Deduplication Strategy

### Current Overlap → Integration
```
┌─────────────────────────────────────────┐
│  claude-flow          agentic-flow      │
├─────────────────────────────────────────┤
│ SwarmCoordinator  →   Swarm System      │ 80% overlap (eliminate)
│ AgentManager      →   Agent Lifecycle   │ 70% overlap (eliminate)
│ TaskScheduler     →   Task Execution    │ 60% overlap (eliminate)
│ SessionManager    →   Session Mgmt      │ 50% overlap (eliminate)
└─────────────────────────────────────────┘

TARGET: <5,000 lines (vs 15,000+ currently)
```

## agentic-flow@alpha Feature Integration

### SONA Learning Modes
```typescript
class SONAIntegration {
  async initializeMode(mode: SONAMode): Promise<void> {
    switch(mode) {
      case 'real-time':   // ~0.05ms adaptation
      case 'balanced':    // general purpose
      case 'research':    // deep exploration
      case 'edge':        // resource-constrained
      case 'batch':       // high-throughput
    }
    await this.agenticFlow.sona.setMode(mode);
  }
}
```

### Flash Attention Integration
```typescript
class FlashAttentionIntegration {
  async optimizeAttention(): Promise<AttentionResult> {
    return this.agenticFlow.attention.flashAttention({
      speedupTarget: '2.49x-7.47x',
      memoryReduction: '50-75%',
      mechanisms: ['multi-head', 'linear', 'local', 'global']
    });
  }
}
```

### AgentDB Coordination
```typescript
class AgentDBIntegration {
  async setupCrossAgentMemory(): Promise<void> {
    await this.agentdb.enableCrossAgentSharing({
      indexType: 'HNSW',
      speedupTarget: '150x-12500x',
      dimensions: 1536
    });
  }
}
```

### MCP Tools Integration
```typescript
class MCPToolsIntegration {
  async integrateBuiltinTools(): Promise<void> {
    // Leverage 213 pre-built tools
    const tools = await this.agenticFlow.mcp.getAvailableTools();
    await this.registerClaudeFlowSpecificTools(tools);

    // Use 19 hook types
    const hookTypes = await this.agenticFlow.hooks.getTypes();
    await this.configureClaudeFlowHooks(hookTypes);
  }
}
```

## Migration Implementation

### Phase 1: Adapter Layer
```typescript
import { Agent as AgenticFlowAgent } from 'agentic-flow@alpha';

export class ClaudeFlowAgent extends AgenticFlowAgent {
  async handleClaudeFlowTask(task: ClaudeTask): Promise<TaskResult> {
    return this.executeWithSONA(task);
  }

  // Backward compatibility
  async legacyCompatibilityLayer(oldAPI: any): Promise<any> {
    return this.adaptToNewAPI(oldAPI);
  }
}
```

### Phase 2: System Migration
```typescript
class SystemMigration {
  async migrateSwarmCoordination(): Promise<void> {
    // Replace SwarmCoordinator (800+ lines) with agentic-flow Swarm
    const swarmConfig = await this.extractSwarmConfig();
    await this.agenticFlow.swarm.initialize(swarmConfig);
  }

  async migrateAgentManagement(): Promise<void> {
    // Replace AgentManager (1,736+ lines) with agentic-flow lifecycle
    const agents = await this.extractActiveAgents();
    for (const agent of agents) {
      await this.agenticFlow.agent.create(agent);
    }
  }

  async migrateTaskExecution(): Promise<void> {
    // Replace TaskScheduler with agentic-flow task graph
    const tasks = await this.extractTasks();
    await this.agenticFlow.task.executeGraph(this.buildTaskGraph(tasks));
  }
}
```

### Phase 3: Cleanup
```typescript
class CodeCleanup {
  async removeDeprecatedCode(): Promise<void> {
    // Remove massive duplicate implementations
    await this.removeFile('src/core/SwarmCoordinator.ts');    // 800+ lines
    await this.removeFile('src/agents/AgentManager.ts');      // 1,736+ lines
    await this.removeFile('src/task/TaskScheduler.ts');       // 500+ lines

    // Total reduction: 10,000+ → <5,000 lines
  }
}
```

## RL Algorithm Integration

```typescript
class RLIntegration {
  algorithms = [
    'PPO', 'DQN', 'A2C', 'MCTS', 'Q-Learning',
    'SARSA', 'Actor-Critic', 'Decision-Transformer'
  ];

  async optimizeAgentBehavior(): Promise<void> {
    for (const algorithm of this.algorithms) {
      await this.agenticFlow.rl.train(algorithm, {
        episodes: 1000,
        rewardFunction: this.claudeFlowRewardFunction
      });
    }
  }
}
```

## Performance Integration

### Flash Attention Targets
```typescript
const attentionBenchmark = {
  baseline: 'current attention mechanism',
  target: '2.49x-7.47x improvement',
  memoryReduction: '50-75%',
  implementation: 'agentic-flow@alpha Flash Attention'
};
```

### AgentDB Search Performance
```typescript
const searchBenchmark = {
  baseline: 'linear search in current systems',
  target: '150x-12,500x via HNSW indexing',
  implementation: 'agentic-flow@alpha AgentDB'
};
```

## Backward Compatibility

### Gradual Migration
```typescript
class BackwardCompatibility {
  // Phase 1: Dual operation
  async enableDualOperation(): Promise<void> {
    this.oldSystem.continue();
    this.newSystem.initialize();
    this.syncState(this.oldSystem, this.newSystem);
  }

  // Phase 2: Feature-by-feature migration
  async migrateGradually(): Promise<void> {
    const features = this.getAllFeatures();
    for (const feature of features) {
      await this.migrateFeature(feature);
      await this.validateFeatureParity(feature);
    }
  }

  // Phase 3: Complete transition
  async completeTransition(): Promise<void> {
    await this.validateFullParity();
    await this.deprecateOldSystem();
  }
}
```

## Success Metrics

- **Code Reduction**: <5,000 lines orchestration (vs 15,000+)
- **Performance**: 2.49x-7.47x Flash Attention speedup
- **Search**: 150x-12,500x AgentDB improvement
- **Memory**: 50-75% usage reduction
- **Feature Parity**: 100% v2 functionality maintained
- **SONA**: <0.05ms adaptation time
- **Integration**: All 213 MCP tools + 19 hook types available

## Related V3 Skills

- `v3-memory-unification` - Memory system integration
- `v3-performance-optimization` - Performance target validation
- `v3-swarm-coordination` - Swarm system migration
- `v3-security-overhaul` - Secure integration patterns

Overview

This skill implements a deep integration of claude-flow as a specialized extension of agentic-flow@alpha, consolidating overlapping systems and eliminating massive duplication. It targets a reduction from 15,000+ duplicated lines to under 5,000 while preserving feature parity and improving performance across attention, search, and tooling. The result is a leaner, easier-to-maintain codebase that leverages agentic-flow building blocks for swarm, lifecycle, task execution, and tooling.

How this skill works

The integration introduces an adapter layer that maps claude-flow concepts to agentic-flow primitives, then migrates subsystems in phases: adapter, system migration, and cleanup. Key integrations include SONA learning modes, Flash Attention, AgentDB HNSW indexing, MCP tools and hooks, and RL training pipelines. Backward compatibility is preserved via dual operation and a feature-by-feature migration path until full transition and deprecation of legacy code.

When to use it

  • When you need to remove large-scale duplicate implementations across two agent frameworks.
  • When aiming for significant inference and memory performance gains (Flash Attention targets).
  • When consolidating multi-agent coordination and lifecycle management under a single foundation.
  • When enabling cross-agent memory and ultra-fast vector search with HNSW indexing.
  • When migrating gradually to a unified agent platform while preserving production stability.

Best practices

  • Start with an adapter layer to encapsulate legacy APIs and enable incremental migration.
  • Run dual operation during migration to sync state and validate parity feature-by-feature.
  • Benchmark Flash Attention and AgentDB performance before and after migration to verify targets.
  • Register and configure MCP tools and hooks early to preserve tool parity for downstream agents.
  • Remove deprecated code only after automated validation confirms behavioral parity.

Example use cases

  • Replace a bespoke SwarmCoordinator and AgentManager with agentic-flow Swarm and lifecycle to cut maintenance overhead.
  • Integrate Flash Attention to achieve 2.49x–7.47x attention speedups and reduce memory by 50–75%.
  • Enable AgentDB cross-agent sharing with HNSW to accelerate vector search by 150x–12,500x.
  • Expose 213 built-in tools and 19 hook types to claude-flow agents through a unified MCP integration.
  • Train agents with multiple RL algorithms (PPO, DQN, A2C, Decision-Transformer) to optimize behavior under the new platform.

FAQ

Will this break existing claude-flow behavior?

No. The migration uses an adapter and phased dual operation to preserve backward compatibility and validate parity before deprecating legacy code.

What are the primary performance wins to expect?

Expect Flash Attention speedups of 2.49x–7.47x, 50–75% memory reduction, and AgentDB HNSW search improvements of 150x–12,500x in targeted scenarios.