Agent skill

Agent V3 Integration Architect

by ruvnet in ruvnet/ruflo

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

MITAuto-check passedAgent Workflows

Install Agent V3 Integration Architect

skills CLI
$ npx skills add ruvnet/ruflo --skill agent-v3-integration-architect -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ruvnet/ruflo agent-v3-integration-architect --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/agent-v3-integration-architect .claude/skills/agent-v3-integration-architect && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
agent-v3-integration-architect
GitHub stars
74k
Used in
2 other repos
Token cost
~2.7k tokens
SKILL.md length
401 words
Files
1
Skills in repo
264
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 3 steps: Foundation Adapter (Week 7) → Core Migration (Week 8-9) → Optimization (Week 10)
  • Agent Workflows work in your project
  • SKILL.md covers Core Mission: ADR-001…, Integration Strategy, agentic-flow@alpha Feature… and Migration Implementation Plan, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Agent V3 Integration Architect is an agent skill from ruvnet/ruflo. Agent skill for v3-integration-architect - invoke with $agent-v3-integration-architect

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows. The repository describes itself as: 🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory…. The licence is MIT.

When your agent uses it

  • Agent Workflows work in your project

Example prompts

  • “/agent-v3-integration-architect”

Requirements

  • Node.js

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Foundation Adapter (Week 7)
  2. Core Migration (Week 8-9)
  3. Optimization (Week 10)

What it can do on your machine

Read from SKILL.md and the folder at commit 58e0ae7. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agent V3 Integration Architect loads about 2.7k tokens when it runs. Until then it costs about 29 tokens; SKILL.md has 401 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~29
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ruvnet/ruflo at commit 58e0ae7, republished under its MIT licence (© ruvnet). 401 words, ~2,657 tokens.

Download SKILL.mdSave it as .claude/skills/agent-v3-integration-architect/SKILL.md (or your agent's skills folder).
name
agent-v3-integration-architect
description
Agent skill for v3-integration-architect - invoke with $agent-v3-integration-architect

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 "🔗 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 "📊 Current duplicate functionality:"
echo "  • SwarmCoordinator vs Swarm System (80% overlap)"
echo "  • AgentManager vs Agent Lifecycle (70% overlap)"
echo "  • TaskScheduler vs Task Execution (60% overlap)"
echo "  • SessionManager vs Session Mgmt (50% overlap)"

# Check integration points
ls -la services$agentic-flow-hooks/ 2>$dev$null | wc -l | xargs echo "🔧 Current hook integrations:"

post_execution: | echo "🔗 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

🔗 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
┌─────────────────────────────────────────┐
│         FUNCTIONALITY OVERLAP           │
├─────────────────────────────────────────┤
│  claude-flow          agentic-flow      │
├─────────────────────────────────────────┤
│ SwarmCoordinator  →   Swarm System      │ 80% overlap
│ AgentManager      →   Agent Lifecycle   │ 70% overlap
│ TaskScheduler     →   Task Execution    │ 60% overlap
│ SessionManager    →   Session Mgmt      │ 50% overlap
└─────────────────────────────────────────┘

TARGET: <5,000 lines orchestration (vs 15,000+ currently)
Integration Architecture
typescript
// 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
typescript
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
typescript
// 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
typescript
// 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
typescript
// 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
typescript
// 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)
typescript
// Create compatibility layer
class AgenticFlowAdapter {
  constructor(private agenticFlow: AgenticFlowCore) {}

  // Migrate SwarmCoordinator → Swarm System
  async migrateSwarmCoordination(): Promise<void> {
    const swarmConfig = await this.extractSwarmConfig();
    await this.agenticFlow.swarm.initialize(swarmConfig);
    // Deprecate old SwarmCoordinator (800+ lines)
  }

  // Migrate AgentManager → 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)
typescript
// 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)
typescript
// 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 → <5,000 lines
  }
}

Performance Integration Targets

Flash Attention Optimization
typescript
// 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
typescript
// 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
typescript
// 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
typescript
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

© ruvnet, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/agent-v3-integration-architect of ruvnet/ruflo.

Open the folder on GitHubat commit 58e0ae7

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in ruvnet/ruflo, which our catalogue first saw on October 7, 2026.

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Agent V3 Integration Architect this skillruvnet/ruflo74k2 repos~2.7kAutomated safety check: PassMIT
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Hook Development for Claude Code Pluginsanthropics/claude-plugins-official38k10 repos~4.1kAutomated safety check: NotesApache-2.0
Using Superpowersfarm-fe/farm5.6k35 repos~1.4kAutomated safety check: PassMIT
Executing Plans Inlineobra/superpowers297k2 repos~5.1kAutomated safety check: PassMIT
Skill CreatorAzure/azqr79589 repos~8.2kAutomated safety check: PassApache-2.0

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Categories

Questions about Agent V3 Integration Architect

What does Agent V3 Integration Architect do?

Agent skill for v3-integration-architect - invoke with $agent-v3-integration-architect. Agent V3 Integration Architect is an agent skill from ruvnet/ruflo.

When should I use Agent V3 Integration Architect?

Agent V3 Integration Architect fits situations like: agent Workflows work in your project.

How do I install Agent V3 Integration Architect in Claude Code?

Run `npx skills add ruvnet/ruflo --skill agent-v3-integration-architect -a claude-code`. Or copy the skill folder (.agents/skills/agent-v3-integration-architect in ruvnet/ruflo) into .claude/skills/agent-v3-integration-architect in your project. Claude Code loads it when a task matches its description.

How do I install Agent V3 Integration Architect in Codex?

Run `npx skills add ruvnet/ruflo --skill agent-v3-integration-architect -a codex`. Or copy the skill folder (.agents/skills/agent-v3-integration-architect in ruvnet/ruflo) into .agents/skills/agent-v3-integration-architect in your project. Codex loads it when a task matches its description.

Can I use Agent V3 Integration Architect in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ruvnet/ruflo --skill agent-v3-integration-architect -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-v3-integration-architect, .gemini/skills/agent-v3-integration-architect, .github/skills/agent-v3-integration-architect and .opencode/skills/agent-v3-integration-architect in your project.

What does Agent V3 Integration Architect need to run?

SKILL.md names no scripts, command-line tools or credentials: Agent V3 Integration Architect is instructions for the agent only. Our summary lists: Node.js.

Does Agent V3 Integration Architect access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Agent V3 Integration Architect safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Agent V3 Integration Architect use?

Agent V3 Integration Architect is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agent V3 Integration Architect use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agent V3 Integration Architect?

Skills that share tags, products or a category with Agent V3 Integration Architect: MCP Server Builder (anthropics/skills, 180k stars), Hook Development for Claude Code Plugins (anthropics/claude-plugins-official, 38k stars), Using Superpowers (farm-fe/farm, 5.6k stars) and Executing Plans Inline (obra/superpowers, 297k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agent V3 Integration Architect?

ruvnet (a GitHub user) maintains it in ruvnet/ruflo, which has 74,159 GitHub stars. The repository holds 264 skills in this directory. The repository was last updated on October 9, 2026.

Source: ruvnet/ruflo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.