agent-v3-integration-architect
- Repo stars 54,444
- Author updated Live
- Author repo ruflo
- Domain
- AI
- Compatible agents
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- Trust score
- 88 / 100 · community maintained
- Author / version / license
- @ruvnet · no license declared
- Token usage
- Lean
- Setup complexity
- Guided setup
- External API key
- Not required
- Operating systems
- macOS · Linux · Windows
- Runtime requirements
- No special requirements
- Permissions
-
- Read-only
- Write / modify
- Shell exec
- Network behavior
- Local-only
- Install commands
- 26 variants
Profile is derived at build time from SKILL.md and install vectors. Subject to drift from author intent.
Heads up: 未限定 allowed-tools,默认拥有全部工具权限。
---
name: agent-v3-integration-architect
description: Agent skill for v3-integration-architect - invoke with $agent-v3-integration-architect name: v3-…
category: ai
runtime: no special runtime
---
# agent-v3-integration-architect output preview
## PART A: Task fit
- Use case: 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 ….
- Inputs: target material, constraints, expected output, and acceptance criteria.
- Evidence boundary: follow “Core Mission: ADR-001 Implementation / Integration Strategy / Current Duplication Analysis” and do not present inference as author intent.
## PART B: Execution result
- **01** The card summarizes the use case; runtime output centers on “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 …”.
- **02** When the source has headings, the agent prioritizes “Core Mission: ADR-001 Implementation / Integration Strategy / Current Duplication Analysis” so the result follows the author’s structure.
- **03** Typical output includes task judgment, concrete steps, required commands or file edits, validation, and follow-up options.
- **04** Risk context follows the fingerprint: read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
## Running Rules
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding scope.
- Return the result, validation criteria, and next iteration options. The source does not require a stable slash command. After installation, invoke the skill by name and describe the task.
Name target files or source material, expected output, forbidden changes, and whether network or shell access is allowed. Permission fingerprint: read files, write/modify files, run shell commands.
Start with a small task and check whether the result follows “Core Mission: ADR-001 Implementation / Integration Strategy / Current Duplication Analysis”. Inspect diffs, logs, previews, or tests before expanding scope.
Confirm the final output includes a concrete result, evidence, and next action. If it stays generic, tighten inputs, boundaries, and acceptance criteria.
---
name: agent-v3-integration-architect
description: Agent skill for v3-integration-architect - invoke with $agent-v3-integration-architect name: v3-…
category: ai
source: ruvnet/ruflo
---
# agent-v3-integration-architect
## When to use
- Agent skill for v3-integration-architect - invoke with $agent-v3-integration-architect name: v3-integration-architect…
- Use it when the task has clear inputs, repeatable steps, and validation criteria.
## What to provide
- Target material, scope, expected result, and forbidden changes.
- Whether network, commands, file writes, or external services are allowed.
## Execution rules
- Organize steps around “Core Mission: ADR-001 Implementation / Integration Strategy / Current Duplication Analysis” and keep inference separate from source facts.
- read files, write/modify files, run shell commands; mostly runs locally; usually needs no extra API key.
- Validate with a small sample before expanding the task.
## Output requirements
- Return the deliverable, key evidence, validation method, and next action.
- Mark missing information as unknown; do not invent commands, platforms, or dependencies. The author source anchors workflow facts; repository files anchor sources and commands; Fluxly only adds fit, limitations, and quality judgment.
skill "agent-v3-integration-architect" {
input -> user goal + target files + boundaries + acceptance criteria
context -> Core Mission: ADR-001 Implementation / Integration Strategy / Current Duplication Analysis
rules -> SKILL.md triggers / order / output contract
runtime -> no special runtime | read files, write/modify files, run shell commands | mostly runs locally
guardrails -> usually needs no extra API key + small-sample validation + diff/log review
output -> copyable result + checklist + next iteration
} 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
// 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 → 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)
// 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 → <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
| 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 |
Decide Fit First
Design Intent
How To Use It
Boundaries And Review