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Get Started Free →Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.
.claude/skills/ruvnet-v3-deep-integration/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-03 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 46% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 54% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 51% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 176% | 0% |
Transforms claude-flow from parallel implementation to specialized extension of agentic-flow@alpha, eliminating massive code duplication while achieving performance improvements and feature parity.
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")
┌─────────────────────────────────────────┐
│ 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)typescriptclass 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); } }
typescriptclass FlashAttentionIntegration { async optimizeAttention(): Promise<AttentionResult> { return this.agenticFlow.attention.flashAttention({ speedupTarget: '2.49x-7.47x', memoryReduction: '50-75%', mechanisms: ['multi-head', 'linear', 'local', 'global'] }); } }
typescriptclass AgentDBIntegration { async setupCrossAgentMemory(): Promise<void> { await this.agentdb.enableCrossAgentSharing({ indexType: 'HNSW', speedupTarget: '150x-12500x', dimensions: 1536 }); } }
typescriptclass 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); } }
typescriptimport { 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); } }
typescriptclass 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)); } }
typescriptclass 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 } }
typescriptclass 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 }); } } }
typescriptconst attentionBenchmark = { baseline: 'current attention mechanism', target: '2.49x-7.47x improvement', memoryReduction: '50-75%', implementation: 'agentic-flow@alpha Flash Attention' };
typescriptconst searchBenchmark = { baseline: 'linear search in current systems', target: '150x-12,500x via HNSW indexing', implementation: 'agentic-flow@alpha AgentDB' };
typescriptclass 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(); } }
v3-memory-unification - Memory system integrationv3-performance-optimization - Performance target validationv3-swarm-coordination - Swarm system migrationv3-security-overhaul - Secure integration patterns| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 17,705 | 21,067 | +19% | 1 | 1 | 0% | 3,058 | 6,770 | +121% | 0 | 0 | — |
case-02 | fail→fail | 22,819 | 18,433 | -19% | 1 | 1 | 0% | 4,913 | 6,258 | +27% | 0 | 0 | — |
case-03 | fail→pass | 28,198 | 18,642 | -34% | 1 | 1 | 0% | 5,611 | 5,998 | +7% | 0 | 0 | — |
case-04 | pass→pass | 16,877 | 12,136 | -28% | 1 | 1 | 0% | 2,716 | 3,884 | +43% | 0 | 0 | — |
case-05 | pass→pass | 19,648 | 17,053 | -13% | 1 | 1 | 0% | 3,736 | 5,459 | +46% | 0 | 0 | — |
case-06 | pass→pass | 13,789 | 8,616 | -38% | 1 | 1 | 0% | 2,460 | 3,669 | +49% | 0 | 0 | — |
case-07 | fail→pass | 14,539 | 6,726 | -54% | 1 | 1 | 0% | 2,214 | 3,230 | +46% | 0 | 0 | — |
case-08 | fail→pass | 15,842 | 12,042 | -24% | 1 | 1 | 0% | 2,755 | 4,253 | +54% | 0 | 0 | — |
case-09 | pass→pass | 11,624 | 10,975 | -6% | 1 | 1 | 0% | 1,946 | 3,914 | +101% | 0 | 0 | — |
case-10 | fail→pass | 16,859 | 13,144 | -22% | 1 | 1 | 0% | 2,853 | 4,296 | +51% | 0 | 0 | — |
case-11 | pass→pass | 14,763 | 12,726 | -14% | 1 | 1 | 0% | 2,384 | 4,245 | +78% | 0 | 0 | — |
case-12 | pass→pass | 12,580 | 9,208 | -27% | 1 | 1 | 0% | 2,082 | 3,609 | +73% | 0 | 0 | — |
case-13 | pass→pass | 13,824 | 7,863 | -43% | 1 | 1 | 0% | 2,391 | 3,259 | +36% | 0 | 0 | — |
case-14 | fail→pass | 12,620 | 1,777 | -86% | 1 | 1 | 0% | 803 | 2,214 | +176% | 0 | 0 | — |
case-20 | fail→pass | 18,884 | 15,667 | -17% | 1 | 1 | 0% | 3,100 | 5,551 | +79% | 0 | 0 | — |
case-15 | fail→pass | 14,278 | 2,661 | -81% | 1 | 1 | 0% | 2,409 | 2,391 | -1% | 0 | 0 | — |
case-16 | pass→pass | 11,344 | 2,951 | -74% | 1 | 1 | 0% | 1,751 | 2,484 | +42% | 0 | 0 | — |
case-17 | fail→fail | 7,117 | 3,114 | -56% | 1 | 1 | 0% | 1,178 | 2,493 | +112% | 0 | 0 | — |
case-18 | pass→pass | 11,821 | 9,563 | -19% | 1 | 1 | 0% | 2,003 | 3,697 | +85% | 0 | 0 | — |
case-19 | fail→pass | 9,782 | 3,777 | -61% | 1 | 1 | 0% | 1,620 | 2,573 | +59% | 0 | 0 | — |
case-21 | pass→pass | 18,311 | 22,308 | +22% | 1 | 1 | 0% | 3,201 | 5,943 | +86% | 0 | 0 | — |
case-22 | pass→pass | 20,310 | 16,005 | -21% | 1 | 1 | 0% | 4,491 | 5,621 | +25% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 21 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +36 percentage points is the difference between those two pass rates over the 21 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.