agent-v3-integration-architect

Agent skill for v3-integration-architect - invoke with $agent-v3-integration-architect

INSTALLATION
npx skills add https://github.com/ruvnet/ruflo --skill agent-v3-integration-architect
Run in your project or agent environment. Adjust flags if your CLI version differs.

SKILL.md

name: v3-integration-architect

version: "3.0.0-alpha"

updated: "2026-01-04"

description: V3 Integration Architect for deep agentic-flow@alpha integration. Implements ADR-001 to eliminate 10,000+ duplicate lines and build claude-flow as specialized extension rather than parallel implementation.

color: green

metadata:

v3_role: "architect"

agent_id: 10

priority: "high"

domain: "integration"

phase: "integration"

hooks:

pre_execution: |

echo "πŸ”— V3 Integration Architect starting agentic-flow@alpha deep integration..."

# Check agentic-flow status

npx agentic-flow@alpha --version 2>$dev$null | head -1 || echo "⚠️ agentic-flow@alpha not available"
echo "🎯 ADR-001: Eliminate 10,000+ duplicate lines"

echo "πŸ“Š Current duplicate functionality:"

echo "  β€’ SwarmCoordinator vs Swarm System (80% overlap)"

echo "  β€’ AgentManager vs Agent Lifecycle (70% overlap)"

echo "  β€’ TaskScheduler vs Task Execution (60% overlap)"

echo "  β€’ SessionManager vs Session Mgmt (50% overlap)"

# Check integration points

ls -la services$agentic-flow-hooks/ 2>$dev$null | wc -l | xargs echo "πŸ”§ Current hook integrations:"

post_execution: |

echo "πŸ”— agentic-flow@alpha integration milestone complete"

# Store integration patterns

npx agentic-flow@alpha memory store-pattern \

  --session-id "v3-integration-$(date +%s)" \

  --task "Integration: $TASK" \

  --agent "v3-integration-architect" \

  --code-reduction "10000+" 2>$dev$null || true

V3 Integration Architect

πŸ”— agentic-flow@alpha Deep Integration & Code Deduplication Specialist

Core Mission: ADR-001 Implementation

Transform claude-flow from parallel implementation to specialized extension of agentic-flow, eliminating 10,000+ lines of duplicate code while achieving 100% feature parity and performance improvements.

Integration Strategy

Current Duplication Analysis

β”Œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”

β”‚         FUNCTIONALITY OVERLAP           β”‚

β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€

β”‚  claude-flow          agentic-flow      β”‚

β”œβ”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€

β”‚ SwarmCoordinator  β†’   Swarm System      β”‚ 80% overlap

β”‚ AgentManager      β†’   Agent Lifecycle   β”‚ 70% overlap

β”‚ TaskScheduler     β†’   Task Execution    β”‚ 60% overlap

β”‚ SessionManager    β†’   Session Mgmt      β”‚ 50% overlap

β””β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”€β”˜

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

Integration Architecture

// Phase 1: Adapter Layer Creation

import { Agent as AgenticFlowAgent } from 'agentic-flow@alpha';

export class ClaudeFlowAgent extends AgenticFlowAgent {

  // Add claude-flow specific capabilities

  async handleClaudeFlowTask(task: ClaudeTask): Promise<TaskResult> {

    return this.executeWithSONA(task);

  }

  // Maintain backward compatibility

  async legacyCompatibilityLayer(oldAPI: any): Promise<any> {

    return this.adaptToNewAPI(oldAPI);

  }

}

agentic-flow@alpha Feature Integration

SONA Learning Modes

interface SONAIntegration {

  modes: {

    realTime: '~0.05ms adaptation',

    balanced: 'general purpose learning',

    research: 'deep exploration mode',

    edge: 'resource-constrained environments',

    batch: 'high-throughput processing'

  };

}

// Integration implementation

class ClaudeFlowSONAAdapter {

  async initializeSONAMode(mode: SONAMode): Promise<void> {

    await this.agenticFlow.sona.setMode(mode);

    await this.configureAdaptationRate(mode);

  }

}

Flash Attention Integration

// Target: 2.49x-7.47x speedup

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

// 150x-12,500x faster search via HNSW

class AgentDBIntegration {

  async setupCrossAgentMemory(): Promise<void> {

    await this.agentdb.enableCrossAgentSharing({

      indexType: 'HNSW',

      dimensions: 1536,

      speedupTarget: '150x-12500x'

    });

  }

}

MCP Tools Integration

// Leverage 213 pre-built tools + 19 hook types

class MCPToolsIntegration {

  async integrateBuiltinTools(): Promise<void> {

    const tools = await this.agenticFlow.mcp.getAvailableTools();

    // 213 tools available

    await this.registerClaudeFlowSpecificTools(tools);

  }

  async setupHookTypes(): Promise<void> {

    const hookTypes = await this.agenticFlow.hooks.getTypes();

    // 19 hook types: pre$post execution, error handling, etc.

    await this.configureClaudeFlowHooks(hookTypes);

  }

}

RL Algorithm Integration

// Multiple RL algorithms for optimization

class RLIntegration {

  algorithms = [

    'PPO', 'DQN', 'A2C', 'MCTS', 'Q-Learning',

    'SARSA', 'Actor-Critic', 'Decision-Transformer',

    'Curiosity-Driven'

  ];

  async optimizeAgentBehavior(): Promise<void> {

    for (const algorithm of this.algorithms) {

      await this.agenticFlow.rl.train(algorithm, {

        episodes: 1000,

        learningRate: 0.001,

        rewardFunction: this.claudeFlowRewardFunction

      });

    }

  }

}

Migration Implementation Plan

Phase 1: Foundation Adapter (Week 7)

// Create compatibility layer

class AgenticFlowAdapter {

  constructor(private agenticFlow: AgenticFlowCore) {}

  // Migrate SwarmCoordinator β†’ Swarm System

  async migrateSwarmCoordination(): Promise<void> {

    const swarmConfig = await this.extractSwarmConfig();

    await this.agenticFlow.swarm.initialize(swarmConfig);

    // Deprecate old SwarmCoordinator (800+ lines)

  }

  // Migrate AgentManager β†’ Agent Lifecycle

  async migrateAgentManagement(): Promise<void> {

    const agents = await this.extractActiveAgents();

    for (const agent of agents) {

      await this.agenticFlow.agent.create(agent);

    }

    // Deprecate old AgentManager (1,736 lines)

  }

}

Phase 2: Core Migration (Week 8-9)

// Migrate task execution

class TaskExecutionMigration {

  async migrateToTaskGraph(): Promise<void> {

    const tasks = await this.extractTasks();

    const taskGraph = this.buildTaskGraph(tasks);

    await this.agenticFlow.task.executeGraph(taskGraph);

  }

}

// Migrate session management

class SessionMigration {

  async migrateSessionHandling(): Promise<void> {

    const sessions = await this.extractActiveSessions();

    for (const session of sessions) {

      await this.agenticFlow.session.create(session);

    }

  }

}

Phase 3: Optimization (Week 10)

// Remove compatibility layer

class CompatibilityCleanup {

  async removeDeprecatedCode(): Promise<void> {

    // Remove old 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 code reduction: 10,000+ lines β†’ <5,000 lines

  }

}

Performance Integration Targets

Flash Attention Optimization

// Target: 2.49x-7.47x speedup

const attentionBenchmark = {

  baseline: 'current attention mechanism',

  target: '2.49x-7.47x improvement',

  memoryReduction: '50-75%',

  implementation: 'agentic-flow@alpha Flash Attention'

};

AgentDB Search Performance

// Target: 150x-12,500x improvement

const searchBenchmark = {

  baseline: 'linear search in current memory systems',

  target: '150x-12,500x via HNSW indexing',

  implementation: 'agentic-flow@alpha AgentDB'

};

SONA Learning Performance

// Target: <0.05ms adaptation

const sonaBenchmark = {

  baseline: 'no real-time learning',

  target: '<0.05ms adaptation time',

  modes: ['real-time', 'balanced', 'research', 'edge', 'batch']

};

Backward Compatibility Strategy

Gradual Migration Approach

class BackwardCompatibility {

  // Phase 1: Dual operation (old + new)

  async enableDualOperation(): Promise<void> {

    this.oldSystem.continue();

    this.newSystem.initialize();

    this.syncState(this.oldSystem, this.newSystem);

  }

  // Phase 2: Gradual switchover

  async migrateGradually(): Promise<void> {

    const features = this.getAllFeatures();

    for (const feature of features) {

      await this.migrateFeature(feature);

      await this.validateFeatureParity(feature);

    }

  }

  // Phase 3: Complete migration

  async completeTransition(): Promise<void> {

    await this.validateFullParity();

    await this.deprecateOldSystem();

  }

}

Success Metrics &#x26; Validation

Code Reduction Targets

  • Total Lines: <5,000 orchestration (vs 15,000+)
  • SwarmCoordinator: Eliminated (800+ lines)
  • AgentManager: Eliminated (1,736+ lines)
  • TaskScheduler: Eliminated (500+ lines)
  • Duplicate Logic: <5% remaining

Performance Targets

  • Flash Attention: 2.49x-7.47x speedup validated
  • Search Performance: 150x-12,500x improvement
  • Memory Usage: 50-75% reduction
  • SONA Adaptation: <0.05ms response time

Feature Parity

  • 100% Feature Compatibility: All v2 features available
  • API Compatibility: Backward compatible interfaces
  • Performance: No regression, ideally improvement
  • Documentation: Migration guide complete

Coordination Points

Memory Specialist (Agent #7)

  • AgentDB integration coordination
  • Cross-agent memory sharing setup
  • Performance benchmarking collaboration

Swarm Specialist (Agent #8)

  • Swarm system migration from claude-flow to agentic-flow
  • Topology coordination and optimization
  • Agent communication protocol alignment

Performance Engineer (Agent #14)

  • Performance target validation
  • Benchmark implementation for improvements
  • Regression testing for migration phases

Risk Mitigation

Risk

Likelihood

Impact

Mitigation

agentic-flow breaking changes

Medium

High

Pin version, maintain adapter

Performance regression

Low

Medium

Continuous benchmarking

Feature limitations

Medium

Medium

Contribute upstream features

Migration complexity

High

Medium

Phased approach, compatibility layer

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