Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Deep agentic-flow@alpha integration implementing ADR-001. Eliminates 10,000+ duplicate lines by building claude-flow as specialized extension rather than parallel implementation.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Transforms claude-flow from parallel implementation to specialized extension of agentic-flow@alpha, eliminating massive code duplication while achieving performance improvements and feature parity.
# 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)
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);
}
}
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']
});
}
}
class AgentDBIntegration {
async setupCrossAgentMemory(): Promise<void> {
await this.agentdb.enableCrossAgentSharing({
indexType: 'HNSW',
speedupTarget: '150x-12500x',
dimensions: 1536
});
}
}
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);
}
}
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);
}
}
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));
}
}
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
}
}
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
});
}
}
}
const attentionBenchmark = {
baseline: 'current attention mechanism',
target: '2.49x-7.47x improvement',
memoryReduction: '50-75%',
implementation: 'agentic-flow@alpha Flash Attention'
};
const searchBenchmark = {
baseline: 'linear search in current systems',
target: '150x-12,500x via HNSW indexing',
implementation: 'agentic-flow@alpha AgentDB'
};
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();
}
}
v3-memory-unification - Memory system integrationv3-performance-optimization - Performance target validationv3-swarm-coordination - Swarm system migrationv3-security-overhaul - Secure integration patterns还没有评价。欢迎分享你的使用感受。
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
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Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
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Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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