[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71S3SZ1SMJW2REWM68FXF05":3,"skill-same:01M71S3SZ1SMJW2REWM68FXF05":67},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":41,"linkOnly":42,"markdownContent":43,"markdownBody":44,"frontMatter":45,"refName":46,"commitSha":47,"skillDirectory":48,"forksCount":49,"repositoryHtmlUrl":50,"repositoryHomepageUrl":6,"ownerHtmlUrl":51,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":52,"install":53,"resources":66},"01M71S3SZ1SMJW2REWM68FXF05","V3 CLI Modernization",null,"CLI modernization and hooks system enhancement for claude-flow v3. Implements interactive prompts, command decomposition, enhanced hooks integration, and intelligent workflow automation.","manykarim\u002Frobotframework-agentguard","manykarim","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F61293164?v=4","01M71S15ZAXR15JHKM1N7BHT21","Many Kasiriha","01M71S160CWQXETSCYWXDVNYJ2",1,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmanykarim\u002Frobotframework-agentguard\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fv3-cli-modernization\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,5,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"modernization","v3","cli","command","hooks system","enhancement","for","decomposition","prompts","hooks","interactive","system","flow","Workflow Automation","integration","automation","workflow","2026-11-10 13:02:02",false,"---\nname: \"V3 CLI Modernization\"\ndescription: \"CLI modernization and hooks system enhancement for claude-flow v3. Implements interactive prompts, command decomposition, enhanced hooks integration, and intelligent workflow automation.\"\n---\n\n# V3 CLI Modernization\n\n## What This Skill Does\n\nModernizes claude-flow v3 CLI with interactive prompts, intelligent command decomposition, enhanced hooks integration, performance optimization, and comprehensive workflow automation capabilities.\n\n## Quick Start\n\n```bash\n# Initialize CLI modernization analysis\nTask(\"CLI architecture\", \"Analyze current CLI structure and identify optimization opportunities\", \"cli-hooks-developer\")\n\n# Modernization implementation (parallel)\nTask(\"Command decomposition\", \"Break down large CLI files into focused modules\", \"cli-hooks-developer\")\nTask(\"Interactive prompts\", \"Implement intelligent interactive CLI experience\", \"cli-hooks-developer\")\nTask(\"Hooks enhancement\", \"Deep integrate hooks with CLI lifecycle\", \"cli-hooks-developer\")\n```\n\n## CLI Architecture Modernization\n\n### Current State Analysis\n```\nCurrent CLI Issues:\n├── index.ts: 108KB monolithic file\n├── enterprise.ts: 68KB feature module\n├── Limited interactivity: Basic command parsing\n├── Hooks integration: Basic pre\u002Fpost execution\n└── No intelligent workflows: Manual command chaining\n\nTarget Architecture:\n├── Modular Commands: \u003C500 lines per command\n├── Interactive Prompts: Smart context-aware UX\n├── Enhanced Hooks: Deep lifecycle integration\n├── Workflow Automation: Intelligent command orchestration\n└── Performance: \u003C200ms command response time\n```\n\n### Modular Command Architecture\n```typescript\n\u002F\u002F src\u002Fcli\u002Fcore\u002Fcommand-registry.ts\ninterface CommandModule {\n  name: string;\n  description: string;\n  category: CommandCategory;\n  handler: CommandHandler;\n  middleware: MiddlewareStack;\n  permissions: Permission[];\n  examples: CommandExample[];\n}\n\nexport class ModularCommandRegistry {\n  private commands = new Map\u003Cstring, CommandModule>();\n  private categories = new Map\u003CCommandCategory, CommandModule[]>();\n  private aliases = new Map\u003Cstring, string>();\n\n  registerCommand(command: CommandModule): void {\n    this.commands.set(command.name, command);\n\n    \u002F\u002F Register in category index\n    if (!this.categories.has(command.category)) {\n      this.categories.set(command.category, []);\n    }\n    this.categories.get(command.category)!.push(command);\n  }\n\n  async executeCommand(name: string, args: string[]): Promise\u003CCommandResult> {\n    const command = this.resolveCommand(name);\n    if (!command) {\n      throw new CommandNotFoundError(name, this.getSuggestions(name));\n    }\n\n    \u002F\u002F Execute middleware stack\n    const context = await this.buildExecutionContext(command, args);\n    const result = await command.middleware.execute(context);\n\n    return result;\n  }\n\n  private resolveCommand(name: string): CommandModule | undefined {\n    \u002F\u002F Try exact match first\n    if (this.commands.has(name)) {\n      return this.commands.get(name);\n    }\n\n    \u002F\u002F Try alias\n    const aliasTarget = this.aliases.get(name);\n    if (aliasTarget) {\n      return this.commands.get(aliasTarget);\n    }\n\n    \u002F\u002F Try fuzzy match\n    return this.findFuzzyMatch(name);\n  }\n}\n```\n\n## Command Decomposition Strategy\n\n### Swarm Commands Module\n```typescript\n\u002F\u002F src\u002Fcli\u002Fcommands\u002Fswarm\u002Fswarm.command.ts\n@Command({\n  name: 'swarm',\n  description: 'Swarm coordination and management',\n  category: 'orchestration'\n})\nexport class SwarmCommand {\n  constructor(\n    private swarmCoordinator: UnifiedSwarmCoordinator,\n    private promptService: InteractivePromptService\n  ) {}\n\n  @SubCommand('init')\n  @Option('--topology', 'Swarm topology (mesh|hierarchical|adaptive)', 'hierarchical')\n  @Option('--agents', 'Number of agents to spawn', 5)\n  @Option('--interactive', 'Interactive agent configuration', false)\n  async init(\n    @Arg('projectName') projectName: string,\n    options: SwarmInitOptions\n  ): Promise\u003CCommandResult> {\n\n    if (options.interactive) {\n      return this.interactiveSwarmInit(projectName);\n    }\n\n    return this.quickSwarmInit(projectName, options);\n  }\n\n  private async interactiveSwarmInit(projectName: string): Promise\u003CCommandResult> {\n    console.log(`🚀 Initializing Swarm for ${projectName}`);\n\n    \u002F\u002F Interactive topology selection\n    const topology = await this.promptService.select({\n      message: 'Select swarm topology:',\n      choices: [\n        { name: 'Hierarchical (Queen-led coordination)', value: 'hierarchical' },\n        { name: 'Mesh (Peer-to-peer collaboration)', value: 'mesh' },\n        { name: 'Adaptive (Dynamic topology switching)', value: 'adaptive' }\n      ]\n    });\n\n    \u002F\u002F Agent configuration\n    const agents = await this.promptAgentConfiguration();\n\n    \u002F\u002F Initialize with configuration\n    const swarm = await this.swarmCoordinator.initialize({\n      name: projectName,\n      topology,\n      agents,\n      hooks: {\n        onAgentSpawn: this.handleAgentSpawn.bind(this),\n        onTaskComplete: this.handleTaskComplete.bind(this),\n        onSwarmComplete: this.handleSwarmComplete.bind(this)\n      }\n    });\n\n    return CommandResult.success({\n      message: `✅ Swarm ${projectName} initialized with ${agents.length} agents`,\n      data: { swarmId: swarm.id, topology, agentCount: agents.length }\n    });\n  }\n\n  @SubCommand('status')\n  async status(): Promise\u003CCommandResult> {\n    const swarms = await this.swarmCoordinator.listActiveSwarms();\n\n    if (swarms.length === 0) {\n      return CommandResult.info('No active swarms found');\n    }\n\n    \u002F\u002F Interactive swarm selection if multiple\n    const selectedSwarm = swarms.length === 1\n      ? swarms[0]\n      : await this.promptService.select({\n          message: 'Select swarm to inspect:',\n          choices: swarms.map(s => ({\n            name: `${s.name} (${s.agents.length} agents, ${s.topology})`,\n            value: s\n          }))\n        });\n\n    return this.displaySwarmStatus(selectedSwarm);\n  }\n}\n```\n\n### Learning Commands Module\n```typescript\n\u002F\u002F src\u002Fcli\u002Fcommands\u002Flearning\u002Flearning.command.ts\n@Command({\n  name: 'learning',\n  description: 'Learning system management and optimization',\n  category: 'intelligence'\n})\nexport class LearningCommand {\n  constructor(\n    private learningService: IntegratedLearningService,\n    private promptService: InteractivePromptService\n  ) {}\n\n  @SubCommand('start')\n  @Option('--algorithm', 'RL algorithm to use', 'auto')\n  @Option('--tier', 'Learning tier (basic|standard|advanced)', 'standard')\n  async start(options: LearningStartOptions): Promise\u003CCommandResult> {\n    \u002F\u002F Auto-detect optimal algorithm if not specified\n    if (options.algorithm === 'auto') {\n      const taskContext = await this.analyzeCurrentContext();\n      options.algorithm = this.learningService.selectOptimalAlgorithm(taskContext);\n\n      console.log(`🧠 Auto-selected ${options.algorithm} algorithm based on context`);\n    }\n\n    const session = await this.learningService.startSession({\n      algorithm: options.algorithm,\n      tier: options.tier,\n      userId: await this.getCurrentUser()\n    });\n\n    return CommandResult.success({\n      message: `🚀 Learning session started with ${options.algorithm}`,\n      data: { sessionId: session.id, algorithm: options.algorithm, tier: options.tier }\n    });\n  }\n\n  @SubCommand('feedback')\n  @Arg('reward', 'Reward value (0-1)', 'number')\n  async feedback(\n    @Arg('reward') reward: number,\n    @Option('--context', 'Additional context for learning')\n    context?: string\n  ): Promise\u003CCommandResult> {\n    const activeSession = await this.learningService.getActiveSession();\n    if (!activeSession) {\n      return CommandResult.error('No active learning session found. Start one with `learning start`');\n    }\n\n    await this.learningService.submitFeedback({\n      sessionId: activeSession.id,\n      reward,\n      context,\n      timestamp: new Date()\n    });\n\n    return CommandResult.success({\n      message: `📊 Feedback recorded (reward: ${reward})`,\n      data: { reward, sessionId: activeSession.id }\n    });\n  }\n\n  @SubCommand('metrics')\n  async metrics(): Promise\u003CCommandResult> {\n    const metrics = await this.learningService.getMetrics();\n\n    \u002F\u002F Interactive metrics display\n    await this.displayInteractiveMetrics(metrics);\n\n    return CommandResult.success('Metrics displayed');\n  }\n}\n```\n\n## Interactive Prompt System\n\n### Advanced Prompt Service\n```typescript\n\u002F\u002F src\u002Fcli\u002Fservices\u002Finteractive-prompt.service.ts\ninterface PromptOptions {\n  message: string;\n  type: 'select' | 'multiselect' | 'input' | 'confirm' | 'progress';\n  choices?: PromptChoice[];\n  default?: any;\n  validate?: (input: any) => boolean | string;\n  transform?: (input: any) => any;\n}\n\nexport class InteractivePromptService {\n  private inquirer: any; \u002F\u002F Dynamic import for tree-shaking\n\n  async select\u003CT>(options: SelectPromptOptions\u003CT>): Promise\u003CT> {\n    const { default: inquirer } = await import('inquirer');\n\n    const result = await inquirer.prompt([{\n      type: 'list',\n      name: 'selection',\n      message: options.message,\n      choices: options.choices,\n      default: options.default\n    }]);\n\n    return result.selection;\n  }\n\n  async multiSelect\u003CT>(options: MultiSelectPromptOptions\u003CT>): Promise\u003CT[]> {\n    const { default: inquirer } = await import('inquirer');\n\n    const result = await inquirer.prompt([{\n      type: 'checkbox',\n      name: 'selections',\n      message: options.message,\n      choices: options.choices,\n      validate: (input: T[]) => {\n        if (options.minSelections && input.length \u003C options.minSelections) {\n          return `Please select at least ${options.minSelections} options`;\n        }\n        if (options.maxSelections && input.length > options.maxSelections) {\n          return `Please select at most ${options.maxSelections} options`;\n        }\n        return true;\n      }\n    }]);\n\n    return result.selections;\n  }\n\n  async input(options: InputPromptOptions): Promise\u003Cstring> {\n    const { default: inquirer } = await import('inquirer');\n\n    const result = await inquirer.prompt([{\n      type: 'input',\n      name: 'input',\n      message: options.message,\n      default: options.default,\n      validate: options.validate,\n      transformer: options.transform\n    }]);\n\n    return result.input;\n  }\n\n  async progressTask\u003CT>(\n    task: ProgressTask\u003CT>,\n    options: ProgressOptions\n  ): Promise\u003CT> {\n    const { default: cliProgress } = await import('cli-progress');\n\n    const progressBar = new cliProgress.SingleBar({\n      format: `${options.title} |{bar}| {percentage}% | {status}`,\n      barCompleteChar: '█',\n      barIncompleteChar: '░',\n      hideCursor: true\n    });\n\n    progressBar.start(100, 0, { status: 'Starting...' });\n\n    try {\n      const result = await task({\n        updateProgress: (percent: number, status?: string) => {\n          progressBar.update(percent, { status: status || 'Processing...' });\n        }\n      });\n\n      progressBar.update(100, { status: 'Complete!' });\n      progressBar.stop();\n\n      return result;\n    } catch (error) {\n      progressBar.stop();\n      throw error;\n    }\n  }\n\n  async confirmWithDetails(\n    message: string,\n    details: ConfirmationDetails\n  ): Promise\u003Cboolean> {\n    console.log('\\n' + chalk.bold(message));\n    console.log(chalk.gray('Details:'));\n\n    for (const [key, value] of Object.entries(details)) {\n      console.log(chalk.gray(`  ${key}: ${value}`));\n    }\n\n    return this.confirm('\\nProceed?');\n  }\n}\n```\n\n## Enhanced Hooks Integration\n\n### Deep CLI Hooks Integration\n```typescript\n\u002F\u002F src\u002Fcli\u002Fhooks\u002Fcli-hooks-manager.ts\ninterface CLIHookEvent {\n  type: 'command_start' | 'command_end' | 'command_error' | 'agent_spawn' | 'task_complete';\n  command: string;\n  args: string[];\n  context: ExecutionContext;\n  timestamp: Date;\n}\n\nexport class CLIHooksManager {\n  private hooks: Map\u003Cstring, HookHandler[]> = new Map();\n  private learningIntegration: LearningHooksIntegration;\n\n  constructor() {\n    this.learningIntegration = new LearningHooksIntegration();\n    this.setupDefaultHooks();\n  }\n\n  private setupDefaultHooks(): void {\n    \u002F\u002F Learning integration hooks\n    this.registerHook('command_start', async (event: CLIHookEvent) => {\n      await this.learningIntegration.recordCommandStart(event);\n    });\n\n    this.registerHook('command_end', async (event: CLIHookEvent) => {\n      await this.learningIntegration.recordCommandSuccess(event);\n    });\n\n    this.registerHook('command_error', async (event: CLIHookEvent) => {\n      await this.learningIntegration.recordCommandError(event);\n    });\n\n    \u002F\u002F Intelligent suggestions\n    this.registerHook('command_start', async (event: CLIHookEvent) => {\n      const suggestions = await this.generateIntelligentSuggestions(event);\n      if (suggestions.length > 0) {\n        this.displaySuggestions(suggestions);\n      }\n    });\n\n    \u002F\u002F Performance monitoring\n    this.registerHook('command_end', async (event: CLIHookEvent) => {\n      await this.recordPerformanceMetrics(event);\n    });\n  }\n\n  async executeHooks(type: string, event: CLIHookEvent): Promise\u003Cvoid> {\n    const handlers = this.hooks.get(type) || [];\n\n    await Promise.all(handlers.map(handler =>\n      this.executeHookSafely(handler, event)\n    ));\n  }\n\n  private async generateIntelligentSuggestions(event: CLIHookEvent): Promise\u003CSuggestion[]> {\n    const context = await this.learningIntegration.getExecutionContext(event);\n    const patterns = await this.learningIntegration.findSimilarPatterns(context);\n\n    return patterns.map(pattern => ({\n      type: 'optimization',\n      message: `Based on similar executions, consider: ${pattern.suggestion}`,\n      confidence: pattern.confidence\n    }));\n  }\n}\n```\n\n### Learning Integration\n```typescript\n\u002F\u002F src\u002Fcli\u002Fhooks\u002Flearning-hooks-integration.ts\nexport class LearningHooksIntegration {\n  constructor(\n    private agenticFlowHooks: AgenticFlowHooksClient,\n    private agentDBLearning: AgentDBLearningClient\n  ) {}\n\n  async recordCommandStart(event: CLIHookEvent): Promise\u003Cvoid> {\n    \u002F\u002F Start trajectory tracking\n    await this.agenticFlowHooks.trajectoryStart({\n      sessionId: event.context.sessionId,\n      command: event.command,\n      args: event.args,\n      context: event.context\n    });\n\n    \u002F\u002F Record experience in AgentDB\n    await this.agentDBLearning.recordExperience({\n      type: 'command_execution',\n      state: this.encodeCommandState(event),\n      action: event.command,\n      timestamp: event.timestamp\n    });\n  }\n\n  async recordCommandSuccess(event: CLIHookEvent): Promise\u003Cvoid> {\n    const executionTime = Date.now() - event.timestamp.getTime();\n    const reward = this.calculateReward(event, executionTime, true);\n\n    \u002F\u002F Complete trajectory\n    await this.agenticFlowHooks.trajectoryEnd({\n      sessionId: event.context.sessionId,\n      success: true,\n      reward,\n      verdict: 'positive'\n    });\n\n    \u002F\u002F Submit feedback to learning system\n    await this.agentDBLearning.submitFeedback({\n      sessionId: event.context.learningSessionId,\n      reward,\n      success: true,\n      latencyMs: executionTime\n    });\n\n    \u002F\u002F Store successful pattern\n    if (reward > 0.8) {\n      await this.agenticFlowHooks.storePattern({\n        pattern: event.command,\n        solution: event.context.result,\n        confidence: reward\n      });\n    }\n  }\n\n  async recordCommandError(event: CLIHookEvent): Promise\u003Cvoid> {\n    const executionTime = Date.now() - event.timestamp.getTime();\n    const reward = this.calculateReward(event, executionTime, false);\n\n    \u002F\u002F Complete trajectory with error\n    await this.agenticFlowHooks.trajectoryEnd({\n      sessionId: event.context.sessionId,\n      success: false,\n      reward,\n      verdict: 'negative',\n      error: event.context.error\n    });\n\n    \u002F\u002F Learn from failure\n    await this.agentDBLearning.submitFeedback({\n      sessionId: event.context.learningSessionId,\n      reward,\n      success: false,\n      latencyMs: executionTime,\n      error: event.context.error\n    });\n  }\n\n  private calculateReward(event: CLIHookEvent, executionTime: number, success: boolean): number {\n    if (!success) return 0;\n\n    \u002F\u002F Base reward for success\n    let reward = 0.5;\n\n    \u002F\u002F Performance bonus (faster execution)\n    const expectedTime = this.getExpectedExecutionTime(event.command);\n    if (executionTime \u003C expectedTime) {\n      reward += 0.3 * (1 - executionTime \u002F expectedTime);\n    }\n\n    \u002F\u002F Complexity bonus\n    const complexity = this.calculateCommandComplexity(event);\n    reward += complexity * 0.2;\n\n    return Math.min(reward, 1.0);\n  }\n}\n```\n\n## Intelligent Workflow Automation\n\n### Workflow Orchestrator\n```typescript\n\u002F\u002F src\u002Fcli\u002Fworkflows\u002Fworkflow-orchestrator.ts\ninterface WorkflowStep {\n  id: string;\n  command: string;\n  args: string[];\n  dependsOn: string[];\n  condition?: WorkflowCondition;\n  retryPolicy?: RetryPolicy;\n}\n\nexport class WorkflowOrchestrator {\n  constructor(\n    private commandRegistry: ModularCommandRegistry,\n    private promptService: InteractivePromptService\n  ) {}\n\n  async executeWorkflow(workflow: Workflow): Promise\u003CWorkflowResult> {\n    const context = new WorkflowExecutionContext(workflow);\n\n    \u002F\u002F Display workflow overview\n    await this.displayWorkflowOverview(workflow);\n\n    const confirmed = await this.promptService.confirm(\n      'Execute this workflow?'\n    );\n\n    if (!confirmed) {\n      return WorkflowResult.cancelled();\n    }\n\n    \u002F\u002F Execute steps\n    return this.promptService.progressTask(\n      async ({ updateProgress }) => {\n        const steps = this.sortStepsByDependencies(workflow.steps);\n\n        for (let i = 0; i \u003C steps.length; i++) {\n          const step = steps[i];\n          updateProgress((i \u002F steps.length) * 100, `Executing ${step.command}`);\n\n          await this.executeStep(step, context);\n        }\n\n        return WorkflowResult.success(context.getResults());\n      },\n      { title: `Workflow: ${workflow.name}` }\n    );\n  }\n\n  async generateWorkflowFromIntent(intent: string): Promise\u003CWorkflow> {\n    \u002F\u002F Use learning system to generate workflow\n    const patterns = await this.findWorkflowPatterns(intent);\n\n    if (patterns.length === 0) {\n      throw new Error('Could not generate workflow for intent');\n    }\n\n    \u002F\u002F Select best pattern or let user choose\n    const selectedPattern = patterns.length === 1\n      ? patterns[0]\n      : await this.promptService.select({\n          message: 'Select workflow template:',\n          choices: patterns.map(p => ({\n            name: `${p.name} (${p.confidence}% match)`,\n            value: p\n          }))\n        });\n\n    return this.customizeWorkflow(selectedPattern, intent);\n  }\n\n  private async executeStep(step: WorkflowStep, context: WorkflowExecutionContext): Promise\u003Cvoid> {\n    \u002F\u002F Check conditions\n    if (step.condition && !this.evaluateCondition(step.condition, context)) {\n      context.skipStep(step.id, 'Condition not met');\n      return;\n    }\n\n    \u002F\u002F Check dependencies\n    const missingDeps = step.dependsOn.filter(dep => !context.isStepCompleted(dep));\n    if (missingDeps.length > 0) {\n      throw new WorkflowError(`Step ${step.id} has unmet dependencies: ${missingDeps.join(', ')}`);\n    }\n\n    \u002F\u002F Execute with retry policy\n    const retryPolicy = step.retryPolicy || { maxAttempts: 1 };\n    let lastError: Error | null = null;\n\n    for (let attempt = 1; attempt \u003C= retryPolicy.maxAttempts; attempt++) {\n      try {\n        const result = await this.commandRegistry.executeCommand(step.command, step.args);\n        context.completeStep(step.id, result);\n        return;\n      } catch (error) {\n        lastError = error as Error;\n\n        if (attempt \u003C retryPolicy.maxAttempts) {\n          await this.delay(retryPolicy.backoffMs || 1000);\n        }\n      }\n    }\n\n    throw new WorkflowError(`Step ${step.id} failed after ${retryPolicy.maxAttempts} attempts: ${lastError?.message}`);\n  }\n}\n```\n\n## Performance Optimization\n\n### Command Performance Monitoring\n```typescript\n\u002F\u002F src\u002Fcli\u002Fperformance\u002Fcommand-performance.ts\nexport class CommandPerformanceMonitor {\n  private metrics = new Map\u003Cstring, CommandMetrics>();\n\n  async measureCommand\u003CT>(\n    commandName: string,\n    executor: () => Promise\u003CT>\n  ): Promise\u003CT> {\n    const start = performance.now();\n    const memBefore = process.memoryUsage();\n\n    try {\n      const result = await executor();\n      const end = performance.now();\n      const memAfter = process.memoryUsage();\n\n      this.recordMetrics(commandName, {\n        executionTime: end - start,\n        memoryDelta: memAfter.heapUsed - memBefore.heapUsed,\n        success: true\n      });\n\n      return result;\n    } catch (error) {\n      const end = performance.now();\n\n      this.recordMetrics(commandName, {\n        executionTime: end - start,\n        memoryDelta: 0,\n        success: false,\n        error: error as Error\n      });\n\n      throw error;\n    }\n  }\n\n  private recordMetrics(command: string, measurement: PerformanceMeasurement): void {\n    if (!this.metrics.has(command)) {\n      this.metrics.set(command, new CommandMetrics(command));\n    }\n\n    const metrics = this.metrics.get(command)!;\n    metrics.addMeasurement(measurement);\n\n    \u002F\u002F Alert if performance degrades\n    if (metrics.getP95ExecutionTime() > 5000) { \u002F\u002F 5 seconds\n      console.warn(`⚠️  Command '${command}' is performing slowly (P95: ${metrics.getP95ExecutionTime()}ms)`);\n    }\n  }\n\n  getCommandReport(command: string): PerformanceReport {\n    const metrics = this.metrics.get(command);\n    if (!metrics) {\n      throw new Error(`No metrics found for command: ${command}`);\n    }\n\n    return {\n      command,\n      totalExecutions: metrics.getTotalExecutions(),\n      successRate: metrics.getSuccessRate(),\n      avgExecutionTime: metrics.getAverageExecutionTime(),\n      p95ExecutionTime: metrics.getP95ExecutionTime(),\n      avgMemoryUsage: metrics.getAverageMemoryUsage(),\n      recommendations: this.generateRecommendations(metrics)\n    };\n  }\n}\n```\n\n## Smart Auto-completion\n\n### Intelligent Command Completion\n```typescript\n\u002F\u002F src\u002Fcli\u002Fcompletion\u002Fintelligent-completion.ts\nexport class IntelligentCompletion {\n  constructor(\n    private learningService: LearningService,\n    private commandRegistry: ModularCommandRegistry\n  ) {}\n\n  async generateCompletions(\n    partial: string,\n    context: CompletionContext\n  ): Promise\u003CCompletion[]> {\n    const completions: Completion[] = [];\n\n    \u002F\u002F 1. Exact command matches\n    const exactMatches = this.commandRegistry.findCommandsByPrefix(partial);\n    completions.push(...exactMatches.map(cmd => ({\n      value: cmd.name,\n      description: cmd.description,\n      type: 'command',\n      confidence: 1.0\n    })));\n\n    \u002F\u002F 2. Learning-based suggestions\n    const learnedSuggestions = await this.learningService.suggestCommands(\n      partial,\n      context\n    );\n    completions.push(...learnedSuggestions);\n\n    \u002F\u002F 3. Context-aware suggestions\n    const contextualSuggestions = await this.generateContextualSuggestions(\n      partial,\n      context\n    );\n    completions.push(...contextualSuggestions);\n\n    \u002F\u002F Sort by confidence and relevance\n    return completions\n      .sort((a, b) => b.confidence - a.confidence)\n      .slice(0, 10); \u002F\u002F Top 10 suggestions\n  }\n\n  private async generateContextualSuggestions(\n    partial: string,\n    context: CompletionContext\n  ): Promise\u003CCompletion[]> {\n    const suggestions: Completion[] = [];\n\n    \u002F\u002F If in git repository, suggest git-related commands\n    if (context.isGitRepository) {\n      if (partial.startsWith('git')) {\n        suggestions.push({\n          value: 'git commit',\n          description: 'Create git commit with generated message',\n          type: 'workflow',\n          confidence: 0.8\n        });\n      }\n    }\n\n    \u002F\u002F If package.json exists, suggest npm commands\n    if (context.hasPackageJson) {\n      if (partial.startsWith('npm') || partial.startsWith('swarm')) {\n        suggestions.push({\n          value: 'swarm init',\n          description: 'Initialize swarm for this project',\n          type: 'workflow',\n          confidence: 0.9\n        });\n      }\n    }\n\n    return suggestions;\n  }\n}\n```\n\n## Success Metrics\n\n### CLI Performance Targets\n- [ ] **Command Response**: \u003C200ms average command execution time\n- [ ] **File Decomposition**: index.ts (108KB) → \u003C10KB per command module\n- [ ] **Interactive UX**: Smart prompts with context awareness\n- [ ] **Hook Integration**: Deep lifecycle integration with learning\n- [ ] **Workflow Automation**: Intelligent multi-step command orchestration\n- [ ] **Auto-completion**: >90% accuracy for command suggestions\n\n### User Experience Improvements\n```typescript\nconst cliImprovements = {\n  before: {\n    commandResponse: '~500ms',\n    interactivity: 'Basic command parsing',\n    workflows: 'Manual command chaining',\n    suggestions: 'Static help text'\n  },\n\n  after: {\n    commandResponse: '\u003C200ms with caching',\n    interactivity: 'Smart context-aware prompts',\n    workflows: 'Automated multi-step execution',\n    suggestions: 'Learning-based intelligent completion'\n  }\n};\n```\n\n## Related V3 Skills\n\n- `v3-core-implementation` - Core domain integration\n- `v3-memory-unification` - Memory-backed command caching\n- `v3-swarm-coordination` - CLI swarm management integration\n- `v3-performance-optimization` - CLI performance monitoring\n\n## Usage Examples\n\n### Complete CLI Modernization\n```bash\n# Full CLI modernization implementation\nTask(\"CLI modernization implementation\",\n     \"Implement modular commands, interactive prompts, and intelligent workflows\",\n     \"cli-hooks-developer\")\n```\n\n### Interactive Command Enhancement\n```bash\n# Enhanced interactive commands\nclaude-flow swarm init --interactive\nclaude-flow learning start --guided\nclaude-flow workflow create --from-intent \"setup new project\"\n```","# V3 CLI Modernization\n\n## What This Skill Does\n\nModernizes claude-flow v3 CLI with interactive prompts, intelligent command decomposition, enhanced hooks integration, performance optimization, and comprehensive workflow automation capabilities.\n\n## Quick Start\n\n```bash\n# Initialize CLI modernization analysis\nTask(\"CLI architecture\", \"Analyze current CLI structure and identify optimization opportunities\", \"cli-hooks-developer\")\n\n# Modernization implementation (parallel)\nTask(\"Command decomposition\", \"Break down large CLI files into focused modules\", \"cli-hooks-developer\")\nTask(\"Interactive prompts\", \"Implement intelligent interactive CLI experience\", \"cli-hooks-developer\")\nTask(\"Hooks enhancement\", \"Deep integrate hooks with CLI lifecycle\", \"cli-hooks-developer\")\n```\n\n## CLI Architecture Modernization\n\n### Current State Analysis\n```\nCurrent CLI Issues:\n├── index.ts: 108KB monolithic file\n├── enterprise.ts: 68KB feature module\n├── Limited interactivity: Basic command parsing\n├── Hooks integration: Basic pre\u002Fpost execution\n└── No intelligent workflows: Manual command chaining\n\nTarget Architecture:\n├── Modular Commands: \u003C500 lines per command\n├── Interactive Prompts: Smart context-aware UX\n├── Enhanced Hooks: Deep lifecycle integration\n├── Workflow Automation: Intelligent command orchestration\n└── Performance: \u003C200ms command response time\n```\n\n### Modular Command Architecture\n```typescript\n\u002F\u002F src\u002Fcli\u002Fcore\u002Fcommand-registry.ts\ninterface CommandModule {\n  name: string;\n  description: string;\n  category: CommandCategory;\n  handler: CommandHandler;\n  middleware: MiddlewareStack;\n  permissions: Permission[];\n  examples: CommandExample[];\n}\n\nexport class ModularCommandRegistry {\n  private commands = new Map\u003Cstring, CommandModule>();\n  private categories = new Map\u003CCommandCategory, CommandModule[]>();\n  private aliases = new Map\u003Cstring, string>();\n\n  registerCommand(command: CommandModule): void {\n    this.commands.set(command.name, command);\n\n    \u002F\u002F Register in category index\n    if (!this.categories.has(command.category)) {\n      this.categories.set(command.category, []);\n    }\n    this.categories.get(command.category)!.push(command);\n  }\n\n  async executeCommand(name: string, args: string[]): Promise\u003CCommandResult> {\n    const command = this.resolveCommand(name);\n    if (!command) {\n      throw new CommandNotFoundError(name, this.getSuggestions(name));\n    }\n\n    \u002F\u002F Execute middleware stack\n    const context = await this.buildExecutionContext(command, args);\n    const result = await command.middleware.execute(context);\n\n    return result;\n  }\n\n  private resolveCommand(name: string): CommandModule | undefined {\n    \u002F\u002F Try exact match first\n    if (this.commands.has(name)) {\n      return this.commands.get(name);\n    }\n\n    \u002F\u002F Try alias\n    const aliasTarget = this.aliases.get(name);\n    if (aliasTarget) {\n      return this.commands.get(aliasTarget);\n    }\n\n    \u002F\u002F Try fuzzy match\n    return this.findFuzzyMatch(name);\n  }\n}\n```\n\n## Command Decomposition Strategy\n\n### Swarm Commands Module\n```typescript\n\u002F\u002F src\u002Fcli\u002Fcommands\u002Fswarm\u002Fswarm.command.ts\n@Command({\n  name: 'swarm',\n  description: 'Swarm coordination and management',\n  category: 'orchestration'\n})\nexport class SwarmCommand {\n  constructor(\n    private swarmCoordinator: UnifiedSwarmCoordinator,\n    private promptService: InteractivePromptService\n  ) {}\n\n  @SubCommand('init')\n  @Option('--topology', 'Swarm topology (mesh|hierarchical|adaptive)', 'hierarchical')\n  @Option('--agents', 'Number of agents to spawn', 5)\n  @Option('--interactive', 'Interactive agent configuration', false)\n  async init(\n    @Arg('projectName') projectName: string,\n    options: SwarmInitOptions\n  ): Promise\u003CCommandResult> {\n\n    if (options.interactive) {\n      return this.interactiveSwarmInit(projectName);\n    }\n\n    return this.quickSwarmInit(projectName, options);\n  }\n\n  private async interactiveSwarmInit(projectName: string): Promise\u003CCommandResult> {\n    console.log(`🚀 Initializing Swarm for ${projectName}`);\n\n    \u002F\u002F Interactive topology selection\n    const topology = await this.promptService.select({\n      message: 'Select swarm topology:',\n      choices: [\n        { name: 'Hierarchical (Queen-led coordination)', value: 'hierarchical' },\n        { name: 'Mesh (Peer-to-peer collaboration)', value: 'mesh' },\n        { name: 'Adaptive (Dynamic topology switching)', value: 'adaptive' }\n      ]\n    });\n\n    \u002F\u002F Agent configuration\n    const agents = await this.promptAgentConfiguration();\n\n    \u002F\u002F Initialize with configuration\n    const swarm = await this.swarmCoordinator.initialize({\n      name: projectName,\n      topology,\n      agents,\n      hooks: {\n        onAgentSpawn: this.handleAgentSpawn.bind(this),\n        onTaskComplete: this.handleTaskComplete.bind(this),\n        onSwarmComplete: this.handleSwarmComplete.bind(this)\n      }\n    });\n\n    return CommandResult.success({\n      message: `✅ Swarm ${projectName} initialized with ${agents.length} agents`,\n      data: { swarmId: swarm.id, topology, agentCount: agents.length }\n    });\n  }\n\n  @SubCommand('status')\n  async status(): Promise\u003CCommandResult> {\n    const swarms = await this.swarmCoordinator.listActiveSwarms();\n\n    if (swarms.length === 0) {\n      return CommandResult.info('No active swarms found');\n    }\n\n    \u002F\u002F Interactive swarm selection if multiple\n    const selectedSwarm = swarms.length === 1\n      ? swarms[0]\n      : await this.promptService.select({\n          message: 'Select swarm to inspect:',\n          choices: swarms.map(s => ({\n            name: `${s.name} (${s.agents.length} agents, ${s.topology})`,\n            value: s\n          }))\n        });\n\n    return this.displaySwarmStatus(selectedSwarm);\n  }\n}\n```\n\n### Learning Commands Module\n```typescript\n\u002F\u002F src\u002Fcli\u002Fcommands\u002Flearning\u002Flearning.command.ts\n@Command({\n  name: 'learning',\n  description: 'Learning system management and optimization',\n  category: 'intelligence'\n})\nexport class LearningCommand {\n  constructor(\n    private learningService: IntegratedLearningService,\n    private promptService: InteractivePromptService\n  ) {}\n\n  @SubCommand('start')\n  @Option('--algorithm', 'RL algorithm to use', 'auto')\n  @Option('--tier', 'Learning tier (basic|standard|advanced)', 'standard')\n  async start(options: LearningStartOptions): Promise\u003CCommandResult> {\n    \u002F\u002F Auto-detect optimal algorithm if not specified\n    if (options.algorithm === 'auto') {\n      const taskContext = await this.analyzeCurrentContext();\n      options.algorithm = this.learningService.selectOptimalAlgorithm(taskContext);\n\n      console.log(`🧠 Auto-selected ${options.algorithm} algorithm based on context`);\n    }\n\n    const session = await this.learningService.startSession({\n      algorithm: options.algorithm,\n      tier: options.tier,\n      userId: await this.getCurrentUser()\n    });\n\n    return CommandResult.success({\n      message: `🚀 Learning session started with ${options.algorithm}`,\n      data: { sessionId: session.id, algorithm: options.algorithm, tier: options.tier }\n    });\n  }\n\n  @SubCommand('feedback')\n  @Arg('reward', 'Reward value (0-1)', 'number')\n  async feedback(\n    @Arg('reward') reward: number,\n    @Option('--context', 'Additional context for learning')\n    context?: string\n  ): Promise\u003CCommandResult> {\n    const activeSession = await this.learningService.getActiveSession();\n    if (!activeSession) {\n      return CommandResult.error('No active learning session found. Start one with `learning start`');\n    }\n\n    await this.learningService.submitFeedback({\n      sessionId: activeSession.id,\n      reward,\n      context,\n      timestamp: new Date()\n    });\n\n    return CommandResult.success({\n      message: `📊 Feedback recorded (reward: ${reward})`,\n      data: { reward, sessionId: activeSession.id }\n    });\n  }\n\n  @SubCommand('metrics')\n  async metrics(): Promise\u003CCommandResult> {\n    const metrics = await this.learningService.getMetrics();\n\n    \u002F\u002F Interactive metrics display\n    await this.displayInteractiveMetrics(metrics);\n\n    return CommandResult.success('Metrics displayed');\n  }\n}\n```\n\n## Interactive Prompt System\n\n### Advanced Prompt Service\n```typescript\n\u002F\u002F src\u002Fcli\u002Fservices\u002Finteractive-prompt.service.ts\ninterface PromptOptions {\n  message: string;\n  type: 'select' | 'multiselect' | 'input' | 'confirm' | 'progress';\n  choices?: PromptChoice[];\n  default?: any;\n  validate?: (input: any) => boolean | string;\n  transform?: (input: any) => any;\n}\n\nexport class InteractivePromptService {\n  private inquirer: any; \u002F\u002F Dynamic import for tree-shaking\n\n  async select\u003CT>(options: SelectPromptOptions\u003CT>): Promise\u003CT> {\n    const { default: inquirer } = await import('inquirer');\n\n    const result = await inquirer.prompt([{\n      type: 'list',\n      name: 'selection',\n      message: options.message,\n      choices: options.choices,\n      default: options.default\n    }]);\n\n    return result.selection;\n  }\n\n  async multiSelect\u003CT>(options: MultiSelectPromptOptions\u003CT>): Promise\u003CT[]> {\n    const { default: inquirer } = await import('inquirer');\n\n    const result = await inquirer.prompt([{\n      type: 'checkbox',\n      name: 'selections',\n      message: options.message,\n      choices: options.choices,\n      validate: (input: T[]) => {\n        if (options.minSelections && input.length \u003C options.minSelections) {\n          return `Please select at least ${options.minSelections} options`;\n        }\n        if (options.maxSelections && input.length > options.maxSelections) {\n          return `Please select at most ${options.maxSelections} options`;\n        }\n        return true;\n      }\n    }]);\n\n    return result.selections;\n  }\n\n  async input(options: InputPromptOptions): Promise\u003Cstring> {\n    const { default: inquirer } = await import('inquirer');\n\n    const result = await inquirer.prompt([{\n      type: 'input',\n      name: 'input',\n      message: options.message,\n      default: options.default,\n      validate: options.validate,\n      transformer: options.transform\n    }]);\n\n    return result.input;\n  }\n\n  async progressTask\u003CT>(\n    task: ProgressTask\u003CT>,\n    options: ProgressOptions\n  ): Promise\u003CT> {\n    const { default: cliProgress } = await import('cli-progress');\n\n    const progressBar = new cliProgress.SingleBar({\n      format: `${options.title} |{bar}| {percentage}% | {status}`,\n      barCompleteChar: '█',\n      barIncompleteChar: '░',\n      hideCursor: true\n    });\n\n    progressBar.start(100, 0, { status: 'Starting...' });\n\n    try {\n      const result = await task({\n        updateProgress: (percent: number, status?: string) => {\n          progressBar.update(percent, { status: status || 'Processing...' });\n        }\n      });\n\n      progressBar.update(100, { status: 'Complete!' });\n      progressBar.stop();\n\n      return result;\n    } catch (error) {\n      progressBar.stop();\n      throw error;\n    }\n  }\n\n  async confirmWithDetails(\n    message: string,\n    details: ConfirmationDetails\n  ): Promise\u003Cboolean> {\n    console.log('\\n' + chalk.bold(message));\n    console.log(chalk.gray('Details:'));\n\n    for (const [key, value] of Object.entries(details)) {\n      console.log(chalk.gray(`  ${key}: ${value}`));\n    }\n\n    return this.confirm('\\nProceed?');\n  }\n}\n```\n\n## Enhanced Hooks Integration\n\n### Deep CLI Hooks Integration\n```typescript\n\u002F\u002F src\u002Fcli\u002Fhooks\u002Fcli-hooks-manager.ts\ninterface CLIHookEvent {\n  type: 'command_start' | 'command_end' | 'command_error' | 'agent_spawn' | 'task_complete';\n  command: string;\n  args: string[];\n  context: ExecutionContext;\n  timestamp: Date;\n}\n\nexport class CLIHooksManager {\n  private hooks: Map\u003Cstring, HookHandler[]> = new Map();\n  private learningIntegration: LearningHooksIntegration;\n\n  constructor() {\n    this.learningIntegration = new LearningHooksIntegration();\n    this.setupDefaultHooks();\n  }\n\n  private setupDefaultHooks(): void {\n    \u002F\u002F Learning integration hooks\n    this.registerHook('command_start', async (event: CLIHookEvent) => {\n      await this.learningIntegration.recordCommandStart(event);\n    });\n\n    this.registerHook('command_end', async (event: CLIHookEvent) => {\n      await this.learningIntegration.recordCommandSuccess(event);\n    });\n\n    this.registerHook('command_error', async (event: CLIHookEvent) => {\n      await this.learningIntegration.recordCommandError(event);\n    });\n\n    \u002F\u002F Intelligent suggestions\n    this.registerHook('command_start', async (event: CLIHookEvent) => {\n      const suggestions = await this.generateIntelligentSuggestions(event);\n      if (suggestions.length > 0) {\n        this.displaySuggestions(suggestions);\n      }\n    });\n\n    \u002F\u002F Performance monitoring\n    this.registerHook('command_end', async (event: CLIHookEvent) => {\n      await this.recordPerformanceMetrics(event);\n    });\n  }\n\n  async executeHooks(type: string, event: CLIHookEvent): Promise\u003Cvoid> {\n    const handlers = this.hooks.get(type) || [];\n\n    await Promise.all(handlers.map(handler =>\n      this.executeHookSafely(handler, event)\n    ));\n  }\n\n  private async generateIntelligentSuggestions(event: CLIHookEvent): Promise\u003CSuggestion[]> {\n    const context = await this.learningIntegration.getExecutionContext(event);\n    const patterns = await this.learningIntegration.findSimilarPatterns(context);\n\n    return patterns.map(pattern => ({\n      type: 'optimization',\n      message: `Based on similar executions, consider: ${pattern.suggestion}`,\n      confidence: pattern.confidence\n    }));\n  }\n}\n```\n\n### Learning Integration\n```typescript\n\u002F\u002F src\u002Fcli\u002Fhooks\u002Flearning-hooks-integration.ts\nexport class LearningHooksIntegration {\n  constructor(\n    private agenticFlowHooks: AgenticFlowHooksClient,\n    private agentDBLearning: AgentDBLearningClient\n  ) {}\n\n  async recordCommandStart(event: CLIHookEvent): Promise\u003Cvoid> {\n    \u002F\u002F Start trajectory tracking\n    await this.agenticFlowHooks.trajectoryStart({\n      sessionId: event.context.sessionId,\n      command: event.command,\n      args: event.args,\n      context: event.context\n    });\n\n    \u002F\u002F Record experience in AgentDB\n    await this.agentDBLearning.recordExperience({\n      type: 'command_execution',\n      state: this.encodeCommandState(event),\n      action: event.command,\n      timestamp: event.timestamp\n    });\n  }\n\n  async recordCommandSuccess(event: CLIHookEvent): Promise\u003Cvoid> {\n    const executionTime = Date.now() - event.timestamp.getTime();\n    const reward = this.calculateReward(event, executionTime, true);\n\n    \u002F\u002F Complete trajectory\n    await this.agenticFlowHooks.trajectoryEnd({\n      sessionId: event.context.sessionId,\n      success: true,\n      reward,\n      verdict: 'positive'\n    });\n\n    \u002F\u002F Submit feedback to learning system\n    await this.agentDBLearning.submitFeedback({\n      sessionId: event.context.learningSessionId,\n      reward,\n      success: true,\n      latencyMs: executionTime\n    });\n\n    \u002F\u002F Store successful pattern\n    if (reward > 0.8) {\n      await this.agenticFlowHooks.storePattern({\n        pattern: event.command,\n        solution: event.context.result,\n        confidence: reward\n      });\n    }\n  }\n\n  async recordCommandError(event: CLIHookEvent): Promise\u003Cvoid> {\n    const executionTime = Date.now() - event.timestamp.getTime();\n    const reward = this.calculateReward(event, executionTime, false);\n\n    \u002F\u002F Complete trajectory with error\n    await this.agenticFlowHooks.trajectoryEnd({\n      sessionId: event.context.sessionId,\n      success: false,\n      reward,\n      verdict: 'negative',\n      error: event.context.error\n    });\n\n    \u002F\u002F Learn from failure\n    await this.agentDBLearning.submitFeedback({\n      sessionId: event.context.learningSessionId,\n      reward,\n      success: false,\n      latencyMs: executionTime,\n      error: event.context.error\n    });\n  }\n\n  private calculateReward(event: CLIHookEvent, executionTime: number, success: boolean): number {\n    if (!success) return 0;\n\n    \u002F\u002F Base reward for success\n    let reward = 0.5;\n\n    \u002F\u002F Performance bonus (faster execution)\n    const expectedTime = this.getExpectedExecutionTime(event.command);\n    if (executionTime \u003C expectedTime) {\n      reward += 0.3 * (1 - executionTime \u002F expectedTime);\n    }\n\n    \u002F\u002F Complexity bonus\n    const complexity = this.calculateCommandComplexity(event);\n    reward += complexity * 0.2;\n\n    return Math.min(reward, 1.0);\n  }\n}\n```\n\n## Intelligent Workflow Automation\n\n### Workflow Orchestrator\n```typescript\n\u002F\u002F src\u002Fcli\u002Fworkflows\u002Fworkflow-orchestrator.ts\ninterface WorkflowStep {\n  id: string;\n  command: string;\n  args: string[];\n  dependsOn: string[];\n  condition?: WorkflowCondition;\n  retryPolicy?: RetryPolicy;\n}\n\nexport class WorkflowOrchestrator {\n  constructor(\n    private commandRegistry: ModularCommandRegistry,\n    private promptService: InteractivePromptService\n  ) {}\n\n  async executeWorkflow(workflow: Workflow): Promise\u003CWorkflowResult> {\n    const context = new WorkflowExecutionContext(workflow);\n\n    \u002F\u002F Display workflow overview\n    await this.displayWorkflowOverview(workflow);\n\n    const confirmed = await this.promptService.confirm(\n      'Execute this workflow?'\n    );\n\n    if (!confirmed) {\n      return WorkflowResult.cancelled();\n    }\n\n    \u002F\u002F Execute steps\n    return this.promptService.progressTask(\n      async ({ updateProgress }) => {\n        const steps = this.sortStepsByDependencies(workflow.steps);\n\n        for (let i = 0; i \u003C steps.length; i++) {\n          const step = steps[i];\n          updateProgress((i \u002F steps.length) * 100, `Executing ${step.command}`);\n\n          await this.executeStep(step, context);\n        }\n\n        return WorkflowResult.success(context.getResults());\n      },\n      { title: `Workflow: ${workflow.name}` }\n    );\n  }\n\n  async generateWorkflowFromIntent(intent: string): Promise\u003CWorkflow> {\n    \u002F\u002F Use learning system to generate workflow\n    const patterns = await this.findWorkflowPatterns(intent);\n\n    if (patterns.length === 0) {\n      throw new Error('Could not generate workflow for intent');\n    }\n\n    \u002F\u002F Select best pattern or let user choose\n    const selectedPattern = patterns.length === 1\n      ? patterns[0]\n      : await this.promptService.select({\n          message: 'Select workflow template:',\n          choices: patterns.map(p => ({\n            name: `${p.name} (${p.confidence}% match)`,\n            value: p\n          }))\n        });\n\n    return this.customizeWorkflow(selectedPattern, intent);\n  }\n\n  private async executeStep(step: WorkflowStep, context: WorkflowExecutionContext): Promise\u003Cvoid> {\n    \u002F\u002F Check conditions\n    if (step.condition && !this.evaluateCondition(step.condition, context)) {\n      context.skipStep(step.id, 'Condition not met');\n      return;\n    }\n\n    \u002F\u002F Check dependencies\n    const missingDeps = step.dependsOn.filter(dep => !context.isStepCompleted(dep));\n    if (missingDeps.length > 0) {\n      throw new WorkflowError(`Step ${step.id} has unmet dependencies: ${missingDeps.join(', ')}`);\n    }\n\n    \u002F\u002F Execute with retry policy\n    const retryPolicy = step.retryPolicy || { maxAttempts: 1 };\n    let lastError: Error | null = null;\n\n    for (let attempt = 1; attempt \u003C= retryPolicy.maxAttempts; attempt++) {\n      try {\n        const result = await this.commandRegistry.executeCommand(step.command, step.args);\n        context.completeStep(step.id, result);\n        return;\n      } catch (error) {\n        lastError = error as Error;\n\n        if (attempt \u003C retryPolicy.maxAttempts) {\n          await this.delay(retryPolicy.backoffMs || 1000);\n        }\n      }\n    }\n\n    throw new WorkflowError(`Step ${step.id} failed after ${retryPolicy.maxAttempts} attempts: ${lastError?.message}`);\n  }\n}\n```\n\n## Performance Optimization\n\n### Command Performance Monitoring\n```typescript\n\u002F\u002F src\u002Fcli\u002Fperformance\u002Fcommand-performance.ts\nexport class CommandPerformanceMonitor {\n  private metrics = new Map\u003Cstring, CommandMetrics>();\n\n  async measureCommand\u003CT>(\n    commandName: string,\n    executor: () => Promise\u003CT>\n  ): Promise\u003CT> {\n    const start = performance.now();\n    const memBefore = process.memoryUsage();\n\n    try {\n      const result = await executor();\n      const end = performance.now();\n      const memAfter = process.memoryUsage();\n\n      this.recordMetrics(commandName, {\n        executionTime: end - start,\n        memoryDelta: memAfter.heapUsed - memBefore.heapUsed,\n        success: true\n      });\n\n      return result;\n    } catch (error) {\n      const end = performance.now();\n\n      this.recordMetrics(commandName, {\n        executionTime: end - start,\n        memoryDelta: 0,\n        success: false,\n        error: error as Error\n      });\n\n      throw error;\n    }\n  }\n\n  private recordMetrics(command: string, measurement: PerformanceMeasurement): void {\n    if (!this.metrics.has(command)) {\n      this.metrics.set(command, new CommandMetrics(command));\n    }\n\n    const metrics = this.metrics.get(command)!;\n    metrics.addMeasurement(measurement);\n\n    \u002F\u002F Alert if performance degrades\n    if (metrics.getP95ExecutionTime() > 5000) { \u002F\u002F 5 seconds\n      console.warn(`⚠️  Command '${command}' is performing slowly (P95: ${metrics.getP95ExecutionTime()}ms)`);\n    }\n  }\n\n  getCommandReport(command: string): PerformanceReport {\n    const metrics = this.metrics.get(command);\n    if (!metrics) {\n      throw new Error(`No metrics found for command: ${command}`);\n    }\n\n    return {\n      command,\n      totalExecutions: metrics.getTotalExecutions(),\n      successRate: metrics.getSuccessRate(),\n      avgExecutionTime: metrics.getAverageExecutionTime(),\n      p95ExecutionTime: metrics.getP95ExecutionTime(),\n      avgMemoryUsage: metrics.getAverageMemoryUsage(),\n      recommendations: this.generateRecommendations(metrics)\n    };\n  }\n}\n```\n\n## Smart Auto-completion\n\n### Intelligent Command Completion\n```typescript\n\u002F\u002F src\u002Fcli\u002Fcompletion\u002Fintelligent-completion.ts\nexport class IntelligentCompletion {\n  constructor(\n    private learningService: LearningService,\n    private commandRegistry: ModularCommandRegistry\n  ) {}\n\n  async generateCompletions(\n    partial: string,\n    context: CompletionContext\n  ): Promise\u003CCompletion[]> {\n    const completions: Completion[] = [];\n\n    \u002F\u002F 1. Exact command matches\n    const exactMatches = this.commandRegistry.findCommandsByPrefix(partial);\n    completions.push(...exactMatches.map(cmd => ({\n      value: cmd.name,\n      description: cmd.description,\n      type: 'command',\n      confidence: 1.0\n    })));\n\n    \u002F\u002F 2. Learning-based suggestions\n    const learnedSuggestions = await this.learningService.suggestCommands(\n      partial,\n      context\n    );\n    completions.push(...learnedSuggestions);\n\n    \u002F\u002F 3. Context-aware suggestions\n    const contextualSuggestions = await this.generateContextualSuggestions(\n      partial,\n      context\n    );\n    completions.push(...contextualSuggestions);\n\n    \u002F\u002F Sort by confidence and relevance\n    return completions\n      .sort((a, b) => b.confidence - a.confidence)\n      .slice(0, 10); \u002F\u002F Top 10 suggestions\n  }\n\n  private async generateContextualSuggestions(\n    partial: string,\n    context: CompletionContext\n  ): Promise\u003CCompletion[]> {\n    const suggestions: Completion[] = [];\n\n    \u002F\u002F If in git repository, suggest git-related commands\n    if (context.isGitRepository) {\n      if (partial.startsWith('git')) {\n        suggestions.push({\n          value: 'git commit',\n          description: 'Create git commit with generated message',\n          type: 'workflow',\n          confidence: 0.8\n        });\n      }\n    }\n\n    \u002F\u002F If package.json exists, suggest npm commands\n    if (context.hasPackageJson) {\n      if (partial.startsWith('npm') || partial.startsWith('swarm')) {\n        suggestions.push({\n          value: 'swarm init',\n          description: 'Initialize swarm for this project',\n          type: 'workflow',\n          confidence: 0.9\n        });\n      }\n    }\n\n    return suggestions;\n  }\n}\n```\n\n## Success Metrics\n\n### CLI Performance Targets\n- [ ] **Command Response**: \u003C200ms average command execution time\n- [ ] **File Decomposition**: index.ts (108KB) → \u003C10KB per command module\n- [ ] **Interactive UX**: Smart prompts with context awareness\n- [ ] **Hook Integration**: Deep lifecycle integration with learning\n- [ ] **Workflow Automation**: Intelligent multi-step command orchestration\n- [ ] **Auto-completion**: >90% accuracy for command suggestions\n\n### User Experience Improvements\n```typescript\nconst cliImprovements = {\n  before: {\n    commandResponse: '~500ms',\n    interactivity: 'Basic command parsing',\n    workflows: 'Manual command chaining',\n    suggestions: 'Static help text'\n  },\n\n  after: {\n    commandResponse: '\u003C200ms with caching',\n    interactivity: 'Smart context-aware prompts',\n    workflows: 'Automated multi-step execution',\n    suggestions: 'Learning-based intelligent completion'\n  }\n};\n```\n\n## Related V3 Skills\n\n- `v3-core-implementation` - Core domain integration\n- `v3-memory-unification` - Memory-backed command caching\n- `v3-swarm-coordination` - CLI swarm management integration\n- `v3-performance-optimization` - CLI performance monitoring\n\n## Usage Examples\n\n### Complete CLI Modernization\n```bash\n# Full CLI modernization implementation\nTask(\"CLI modernization implementation\",\n     \"Implement modular commands, interactive prompts, and intelligent workflows\",\n     \"cli-hooks-developer\")\n```\n\n### Interactive Command Enhancement\n```bash\n# Enhanced interactive commands\nclaude-flow swarm init --interactive\nclaude-flow learning start 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