mcp__claude-flow__sparc_mode {
  mode: "orchestrator",
  task_description: "coordinate feature development",
  options: { parallel: true, monitor: true }
}

mcp__claude-flow__sparc_mode {
  mode: "coder",
  task_description: "implement user authentication with JWT",
  options: {
    test_driven: true,
    parallel_edits: true,
    typescript: true
  }
}

mcp__claude-flow__sparc_mode {
  mode: "architect",
  task_description: "design scalable e-commerce platform",
  options: {
    detailed: true,
    memory_enabled: true,
    patterns: ["microservices", "event-driven"]
  }
}

mcp__claude-flow__sparc_mode {
  mode: "tdd",
  task_description: "shopping cart feature with payment integration",
  options: {
    coverage_target: 90,
    test_framework: "jest",
    e2e_framework: "playwright"
  }
}

mcp__claude-flow__sparc_mode {
  mode: "reviewer",
  task_description: "review authentication module PR #123",
  options: {
    security_check: true,
    performance_check: true,
    test_coverage_check: true
  }
}

mcp__claude-flow__sparc_mode {
  mode: "researcher",
  task_description: "research microservices best practices 2024",
  options: {
    depth: "comprehensive",
    sources: ["academic", "industry", "news"],
    citations: true
  }
}

// Basic mode execution
mcp__claude-flow__sparc_mode {
  mode: "<mode-name>",
  task_description: "<task description>",
  options: {
    // mode-specific options
  }
}

// Initialize swarm for complex tasks
mcp__claude-flow__swarm_init {
  topology: "hierarchical",  // or "mesh", "ring", "star"
  strategy: "auto",           // or "balanced", "specialized", "adaptive"
  maxAgents: 8
}

// Spawn specialized agents
mcp__claude-flow__agent_spawn {
  type: "<agent-type>",
  capabilities: ["<capability1>", "<capability2>"]
}

// Monitor execution
mcp__claude-flow__swarm_monitor {
  swarmId: "current",
  interval: 5000
}

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---

// Initialize hierarchical swarm
mcp__claude-flow__swarm_init {
  topology: "hierarchical",
  maxAgents: 12
}

// Spawn coordinator
mcp__claude-flow__agent_spawn {
  type: "coordinator",
  capabilities: ["planning", "delegation", "monitoring"]
}

// Spawn specialized workers
mcp__claude-flow__agent_spawn { type: "architect" }
mcp__claude-flow__agent_spawn { type: "coder" }
mcp__claude-flow__agent_spawn { type: "tester" }
mcp__claude-flow__agent_spawn { type: "reviewer" }

mcp__claude-flow__swarm_init {
  topology: "mesh",
  strategy: "balanced",
  maxAgents: 6
}

mcp__claude-flow__workflow_create {
  name: "development-pipeline",
  steps: [
    { mode: "researcher", task: "gather requirements" },
    { mode: "architect", task: "design system" },
    { mode: "coder", task: "implement features" },
    { mode: "tdd", task: "create tests" },
    { mode: "reviewer", task: "review code" }
  ],
  triggers: ["on_step_complete"]
}

mcp__claude-flow__task_orchestrate {
  task: "build full-stack application",
  strategy: "parallel",
  dependencies: {
    backend: [],
    frontend: [],
    database: [],
    tests: ["backend", "frontend"]
  }
}

mcp__claude-flow__swarm_init {
  topology: "hierarchical",
  strategy: "adaptive",  // Auto-adjusts based on workload
  maxAgents: 20
}

// Step 1: Initialize TDD swarm
mcp__claude-flow__swarm_init {
  topology: "hierarchical",
  maxAgents: 8
}

// Step 2: Research and planning
mcp__claude-flow__sparc_mode {
  mode: "researcher",
  task_description: "research testing best practices for feature X"
}

// Step 3: Architecture design
mcp__claude-flow__sparc_mode {
  mode: "architect",
  task_description: "design testable architecture for feature X"
}

// Step 4: TDD implementation
mcp__claude-flow__sparc_mode {
  mode: "tdd",
  task_description: "implement feature X with 90% coverage",
  options: {
    coverage_target: 90,
    test_framework: "jest",
    parallel_tests: true
  }
}

// Step 5: Code review
mcp__claude-flow__sparc_mode {
  mode: "reviewer",
  task_description: "review feature X implementation",
  options: {
    test_coverage_check: true,
    security_check: true
  }
}

// Step 6: Optimization
mcp__claude-flow__sparc_mode {
  mode: "optimizer",
  task_description: "optimize feature X performance"
}

// RED: Write failing test
mcp__claude-flow__sparc_mode {
  mode: "tester",
  task_description: "create failing test for shopping cart add item",
  options: { expect_failure: true }
}

// GREEN: Minimal implementation
mcp__claude-flow__sparc_mode {
  mode: "coder",
  task_description: "implement minimal code to pass test",
  options: { minimal: true }
}

// REFACTOR: Improve code quality
mcp__claude-flow__sparc_mode {
  mode: "coder",
  task_description: "refactor shopping cart implementation",
  options: { maintain_tests: true }
}

// Store architectural decisions
mcp__claude-flow__memory_usage {
  action: "store",
  namespace: "architecture",
  key: "api-design-v1",
  value: JSON.stringify(apiDesign),
  ttl: 86400000  // 24 hours
}

// Retrieve in subsequent agents
mcp__claude-flow__memory_usage {
  action: "retrieve",
  namespace: "architecture",
  key: "api-design-v1"
}

// ✅ CORRECT: All operations together
[Single Message]:
  mcp__claude-flow__agent_spawn { type: "researcher" }
  mcp__claude-flow__agent_spawn { type: "coder" }
  mcp__claude-flow__agent_spawn { type: "tester" }
  TodoWrite { todos: [8-10 todos] }

// ❌ WRONG: Multiple messages
Message 1: mcp__claude-flow__agent_spawn { type: "researcher" }
Message 2: mcp__claude-flow__agent_spawn { type: "coder" }
Message 3: TodoWrite { todos: [...] }

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project/
├── src/           # Source code
├── tests/         # Test files
├── docs/          # Documentation
├── config/        # Configuration
├── scripts/       # Utility scripts
└── examples/      # Example code

[Single Message - Parallel Agent Execution]:

// Initialize swarm
mcp__claude-flow__swarm_init {
  topology: "hierarchical",
  maxAgents: 10
}

// Architecture phase
mcp__claude-flow__sparc_mode {
  mode: "architect",
  task_description: "design REST API with authentication",
  options: { memory_enabled: true }
}

// Research phase
mcp__claude-flow__sparc_mode {
  mode: "researcher",
  task_description: "research authentication best practices"
}

// Implementation phase
mcp__claude-flow__sparc_mode {
  mode: "coder",
  task_description: "implement Express API with JWT auth",
  options: { test_driven: true }
}

// Testing phase
mcp__claude-flow__sparc_mode {
  mode: "tdd",
  task_description: "comprehensive API tests",
  options: { coverage_target: 90 }
}

// Review phase
mcp__claude-flow__sparc_mode {
  mode: "reviewer",
  task_description: "security and performance review",
  options: { security_check: true }
}

// Batch todos
TodoWrite {
  todos: [
    {content: "Design API schema", status: "completed"},
    {content: "Research JWT implementation", status: "completed"},
    {content: "Implement authentication", status: "in_progress"},
    {content: "Write API tests", status: "pending"},
    {content: "Security review", status: "pending"},
    {content: "Performance optimization", status: "pending"},
    {content: "API documentation", status: "pending"},
    {content: "Deployment setup", status: "pending"}
  ]
}

// Research phase
mcp__claude-flow__sparc_mode {
  mode: "researcher",
  task_description: "research AI-powered search implementations",
  options: {
    depth: "comprehensive",
    sources: ["academic", "industry"]
  }
}

// Innovation phase
mcp__claude-flow__sparc_mode {
  mode: "innovator",
  task_description: "propose novel search algorithm",
  options: { memory_enabled: true }
}

// Architecture phase
mcp__claude-flow__sparc_mode {
  mode: "architect",
  task_description: "design scalable search system"
}

// Implementation phase
mcp__claude-flow__sparc_mode {
  mode: "coder",
  task_description: "implement search algorithm",
  options: { test_driven: true }
}

// Documentation phase
mcp__claude-flow__sparc_mode {
  mode: "documenter",
  task_description: "document search system architecture and API"
}

// Analysis phase
mcp__claude-flow__sparc_mode {
  mode: "analyzer",
  task_description: "analyze legacy codebase dependencies"
}

// Planning phase
mcp__claude-flow__sparc_mode {
  mode: "orchestrator",
  task_description: "plan incremental refactoring strategy"
}

// Testing phase (create safety net)
mcp__claude-flow__sparc_mode {
  mode: "tester",
  task_description: "create comprehensive test suite for legacy code",
  options: { coverage_target: 80 }
}

// Refactoring phase
mcp__claude-flow__sparc_mode {
  mode: "coder",
  task_description: "refactor module X with modern patterns",
  options: { maintain_tests: true }
}

// Review phase
mcp__claude-flow__sparc_mode {
  mode: "reviewer",
  task_description: "validate refactoring maintains functionality"
}

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---

// Train patterns from successful workflows
mcp__claude-flow__neural_train {
  pattern_type: "coordination",
  training_data: "successful_tdd_workflow.json",
  epochs: 50
}

// Save session state
mcp__claude-flow__memory_persist {
  sessionId: "feature-auth-v1"
}

// Restore in new session
mcp__claude-flow__context_restore {
  snapshotId: "feature-auth-v1"
}

// Analyze repository
mcp__claude-flow__github_repo_analyze {
  repo: "owner/repo",
  analysis_type: "code_quality"
}

// Manage pull requests
mcp__claude-flow__github_pr_manage {
  repo: "owner/repo",
  pr_number: 123,
  action: "review"
}

// Real-time swarm monitoring
mcp__claude-flow__swarm_monitor {
  swarmId: "current",
  interval: 5000
}

// Bottleneck analysis
mcp__claude-flow__bottleneck_analyze {
  component: "api-layer",
  metrics: ["latency", "throughput", "errors"]
}

// Token usage tracking
mcp__claude-flow__token_usage {
  operation: "feature-development",
  timeframe: "24h"
}

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// Initialize swarm
mcp__claude-flow__swarm_init { topology: "hierarchical" }

// Execute mode
mcp__claude-flow__sparc_mode { mode: "coder", task_description: "..." }

// Monitor progress
mcp__claude-flow__swarm_monitor { interval: 5000 }

// Store in memory
mcp__claude-flow__memory_usage { action: "store", key: "...", value: "..." }