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Skills/ruvnet/ruflo/agent-v3-integration-architect
agent-v3-integration-architect logo

agent-v3-integration-architect

ruvnet/ruflo
713 installs60K stars
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Installation

npx skills add https://github.com/ruvnet/ruflo --skill agent-v3-integration-architect

Summary

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

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 & 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

RiskLikelihoodImpactMitigation
agentic-flow breaking changesMediumHighPin version, maintain adapter
Performance regressionLowMediumContinuous benchmarking
Feature limitationsMediumMediumContribute upstream features
Migration complexityHighMediumPhased approach, compatibility layer

Score

0–100
65/ 100

Grade

C

Popularity17/30

713 installs β€” growing adoption. Source repo has 59,502 GitHub stars.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness β€” computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Agent V3 Integration Architect skill score badge previewScore badge

Markdown

[![Agent V3 Integration Architect skill](https://www.remoteopenclaw.com/skills/ruvnet/ruflo/agent-v3-integration-architect/badges/score.svg)](https://www.remoteopenclaw.com/skills/ruvnet/ruflo/agent-v3-integration-architect)

HTML

<a href="https://www.remoteopenclaw.com/skills/ruvnet/ruflo/agent-v3-integration-architect"><img src="https://www.remoteopenclaw.com/skills/ruvnet/ruflo/agent-v3-integration-architect/badges/score.svg" alt="Agent V3 Integration Architect skill"/></a>

Agent V3 Integration Architect FAQ

How do I install the Agent V3 Integration Architect skill?

Run β€œnpx skills add https://github.com/ruvnet/ruflo --skill agent-v3-integration-architect” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run β€” no restart or extra configuration needed.

What does the Agent V3 Integration Architect skill do?

Agent skill for v3-integration-architect - invoke with $agent-v3-integration-architect The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Agent V3 Integration Architect skill free?

Yes. Agent V3 Integration Architect is a free, open-source skill published from ruvnet/ruflo. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Agent V3 Integration Architect work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Agent V3 Integration Architect works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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