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Skills/ruvnet/ruflo/v3-deep-integration
v3-deep-integration logo

v3-deep-integration

ruvnet/ruflo
0 installs60K stars
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Installation

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

Summary

Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.

SKILL.md

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

# 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

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

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

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

MCP Tools Integration

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

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

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

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

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

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

AgentDB Search Performance

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

Backward Compatibility

Gradual Migration

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

Score

0–100
76/ 100

Grade

B

Popularity28/30

59,500 GitHub stars on the source repo — top-tier adoption. Install counts are not tracked for this skill.

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 — 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.

V3 Deep Integration skill score badge previewScore badge

Markdown

[![V3 Deep Integration skill](https://www.claudemarket.ai/skills/ruvnet/ruflo/v3-deep-integration/badges/score.svg)](https://www.claudemarket.ai/skills/ruvnet/ruflo/v3-deep-integration)

HTML

<a href="https://www.claudemarket.ai/skills/ruvnet/ruflo/v3-deep-integration"><img src="https://www.claudemarket.ai/skills/ruvnet/ruflo/v3-deep-integration/badges/score.svg" alt="V3 Deep Integration skill"/></a>

V3 Deep Integration FAQ

How do I install the V3 Deep Integration skill?

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

What does the V3 Deep Integration skill do?

Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the V3 Deep Integration skill free?

Yes. V3 Deep Integration 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 V3 Deep Integration work with Claude Code and OpenClaw?

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

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