Agent助手
- 作者仓库星标 54,444
- 作者更新于 实时读取
- 作者仓库 ruflo
- 领域
- AI 智能
- 兼容 Agent
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- 信任分
- 88 / 100 · 社区维护
- 作者 / 版本 / 许可
- @ruvnet · 未声明 license
- Token 消耗评级
- 低消耗
- 接入复杂程度
- 需简单配置
- 是否需要外部 API Key
- 不需要
- 兼容的系统
- macOS · Linux · Windows
- 底层运行要求
- 无特殊要求
- 文件与系统权限
-
- 只读
- 允许写入 / 修改
- Shell 执行
- 网络行为
- 仅限本地
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 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 智能
runtime: 无特殊运行时
---
# agent-v3-integration-architect 输出预览
## PART A: 任务判断
- 适用问题:提示词、Agent 工作流、模型评估或自动化推理。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“Core Mission: ADR-001 Implementation / Integration Strategy / Current Duplication Analysis”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于提示词、Agent 工作流、模型评估或自动化推理,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“Core Mission: ADR-001 Implementation / Integration Strategy / Current Duplication Analysis”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、写入/修改文件、执行终端命令、主要在本地完成、通常不需要额外 API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件、执行终端命令;主要在本地完成;通常不需要额外 API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文没有稳定的斜杠命令要求。安装验证后通常全局生效,直接在对话里点名这个 Skill 并描述任务即可。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、写入/修改文件、执行终端命令。
先用一个小任务确认它会围绕“Core Mission: ADR-001 Implementation / Integration Strategy / Current Duplication Analysis”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: agent-v3-integration-architect
description: Agent skill for v3-integration-architect - invoke with $agent-v3-integration-architect name: v3-…
category: AI 智能
source: ruvnet/ruflo
---
# agent-v3-integration-architect
## 什么时候使用
- 把 AI / Agent方向的常用动作沉淀成 Agent 可调用的技能 适合处理AI Agent、提示词、模型评估与自动化推理,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 把任务拆成可执行、可检查…
- 面向提示词、Agent 工作流、模型评估或自动化推理,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「Core Mission: ADR-001 Implementation / Integration Strategy / Current Duplication Analysis」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件、执行终端命令;主要在本地完成;通常不需要额外 API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "agent-v3-integration-architect" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> Core Mission: ADR-001 Implementation / Integration Strategy / Current Duplication Analysis
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> 无特殊运行时 | 读取文件、写入/修改文件、执行终端命令 | 主要在本地完成
安全层 -> 通常不需要额外 API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} name: v3-integration-architect version: "3.0.0-alpha" updated: "2026-01-04" description: V3 Integration Architect for deep agentic-flow@alpha integration. Implements ADR-001 to eliminate 10,000+ duplicate lines and build claude-flow as specialized extension rather than parallel implementation. color: green metadata: v3_role: "architect" agent_id: 10 priority: "high" domain: "integration" phase: "integration" hooks: pre_execution: | echo "🔗 V3 Integration Architect starting agentic-flow@alpha deep integration..."
# Check agentic-flow status
npx agentic-flow@alpha --version 2>$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 |
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核