Qe Sparc Methodology

Core Philosophy

SPARC methodology emphasizes:

• Systematic Approach: Structured phases from specification to completion
• Test-Driven Development: Tests written before implementation
• Parallel Execution: Concurrent agent coordination for 2.8-4.4x speed improvements
• Memory Integration: Persistent knowledge sharing across agents and sessions
• Quality First: Comprehensive reviews, testing, and validation
• Modular Design: Clean separation of concerns with clear interfaces

Key Principles

1. Specification Before Code: Define requirements and constraints clearly
2. Design Before Implementation: Plan architecture and components
3. Tests Before Features: Write failing tests, then make them pass
4. Review Everything: Code quality, security, and performance checks
5. Document Continuously: Maintain current documentation throughout

Development Phases

Phase 1: Specification

Goal: Define requirements, constraints, and success criteria

• Requirements analysis
• User story mapping
• Constraint identification
• Success metrics definition
• Pseudocode planning

Key Modes: researcher, analyzer, memory-manager

Phase 2: Architecture

Goal: Design system structure and component interfaces

• System architecture design
• Component interface definition
• Database schema planning
• API contract specification
• Infrastructure planning

Key Modes: architect, designer, orchestrator

Phase 3: Refinement (TDD Implementation)

Goal: Implement features with test-first approach

• Write failing tests
• Implement minimum viable code
• Make tests pass
• Refactor for quality
• Iterate until complete

Key Modes: tdd, coder, tester

Phase 4: Review

Goal: Ensure code quality, security, and performance

• Code quality assessment
• Security vulnerability scanning
• Performance profiling
• Best practices validation
• Documentation review

Key Modes: reviewer, optimizer, debugger

Phase 5: Completion

Goal: Integration, deployment, and monitoring

• System integration
• Deployment automation
• Monitoring setup
• Documentation finalization
• Knowledge capture

Key Modes: workflow-manager, documenter, memory-manager

Available Modes

Core Orchestration Modes

orchestrator

Multi-agent task orchestration with TodoWrite/Task/Memory coordination.

Capabilities:

• Task decomposition into manageable units
• Agent coordination and resource allocation
• Progress tracking and result synthesis
• Adaptive strategy selection
• Cross-agent communication

Usage:

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

swarm-coordinator

Specialized swarm management for complex multi-agent workflows.

Capabilities:

• Topology optimization (mesh, hierarchical, ring, star)
• Agent lifecycle management
• Dynamic scaling based on workload
• Fault tolerance and recovery
• Performance monitoring

workflow-manager

Process automation and workflow orchestration.

Capabilities:

• Workflow definition and execution
• Event-driven triggers
• Sequential and parallel pipelines
• State management
• Error handling and retry logic

batch-executor

Parallel task execution for high-throughput operations.

Capabilities:

• Concurrent file operations
• Batch processing optimization
• Resource pooling
• Load balancing
• Progress aggregation

Development Modes

coder

Autonomous code generation with batch file operations.

Capabilities:

• Feature implementation
• Code refactoring
• Bug fixes and patches
• API development
• Algorithm implementation

Quality Standards:

• ES2022+ standards
• TypeScript type safety
• Comprehensive error handling
• Performance optimization
• Security best practices

Usage:

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

architect

System design with Memory-based coordination.

Capabilities:

• Microservices architecture
• Event-driven design
• Domain-driven design (DDD)
• Hexagonal architecture
• CQRS and Event Sourcing

Memory Integration:

• Store architectural decisions
• Share component specifications
• Maintain design consistency
• Track architectural evolution

Design Patterns:

• Layered architecture
• Microservices patterns
• Event-driven patterns
• Domain modeling
• Infrastructure as Code

Usage:

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

tdd

Test-driven development with comprehensive testing.

Capabilities:

• Test-first development
• Red-green-refactor cycle
• Test suite design
• Coverage optimization (target: 90%+)
• Continuous testing

TDD Workflow:

1. Write failing test (RED)
2. Implement minimum code
3. Make test pass (GREEN)
4. Refactor for quality (REFACTOR)
5. Repeat cycle

Testing Strategies:

• Unit testing (Jest, Mocha, Vitest)
• Integration testing
• End-to-end testing (Playwright, Cypress)
• Performance testing
• Security testing

Usage:

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"
  }
}

reviewer

Code review using batch file analysis.

Capabilities:

• Code quality assessment
• Security vulnerability detection
• Performance analysis
• Best practices validation
• Documentation review

Review Criteria:

• Code correctness and logic
• Design pattern adherence
• Comprehensive error handling
• Test coverage adequacy
• Maintainability and readability
• Security vulnerabilities
• Performance bottlenecks

Batch Analysis:

• Parallel file review
• Pattern detection
• Dependency checking
• Consistency validation
• Automated reporting

Usage:

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
  }
}

Analysis and Research Modes

researcher

Deep research with parallel WebSearch/WebFetch and Memory coordination.

Capabilities:

• Comprehensive information gathering
• Source credibility evaluation
• Trend analysis and forecasting
• Competitive research
• Technology assessment

Research Methods:

• Parallel web searches
• Academic paper analysis
• Industry report synthesis
• Expert opinion gathering
• Statistical data compilation

Memory Integration:

• Store research findings with citations
• Build knowledge graphs
• Track information sources
• Cross-reference insights
• Maintain research history

Usage:

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

analyzer

Code and data analysis with pattern recognition.

Capabilities:

• Static code analysis
• Dependency analysis
• Performance profiling
• Security scanning
• Data pattern recognition

optimizer

Performance optimization and bottleneck resolution.

Capabilities:

• Algorithm optimization
• Database query tuning
• Caching strategy design
• Bundle size reduction
• Memory leak detection

Creative and Support Modes

designer

UI/UX design with accessibility focus.

Capabilities:

• Interface design
• User experience optimization
• Accessibility compliance (WCAG 2.1)
• Design system creation
• Responsive layout design

innovator

Creative problem-solving and novel solutions.

Capabilities:

• Brainstorming and ideation
• Alternative approach generation
• Technology evaluation
• Proof of concept development
• Innovation feasibility analysis

documenter

Comprehensive documentation generation.

Capabilities:

• API documentation (OpenAPI/Swagger)
• Architecture diagrams
• User guides and tutorials
• Code comments and JSDoc
• README and changelog maintenance

debugger

Systematic debugging and issue resolution.

Capabilities:

• Bug reproduction
• Root cause analysis
• Fix implementation
• Regression prevention
• Debug logging optimization

tester

Comprehensive testing beyond TDD.

Capabilities:

• Test suite expansion
• Edge case identification
• Performance testing
• Load testing
• Chaos engineering

memory-manager

Knowledge management and context preservation.

Capabilities:

• Cross-session memory persistence
• Knowledge graph construction
• Context restoration
• Learning pattern extraction
• Decision tracking

Activation Methods

Method 1: MCP Tools (Preferred in Claude Code)

Best for: Integrated Claude Code workflows with full orchestration capabilities

// 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
}

Method 2: NPX CLI (Fallback)

Best for: Terminal usage or when MCP tools unavailable

# Execute specific mode
npx claude-flow sparc run <mode> "task description"

# Use alpha features
npx claude-flow@alpha sparc run <mode> "task description"

# List all available modes
npx claude-flow sparc modes

# Get help for specific mode
npx claude-flow sparc help <mode>

# Run with options
npx claude-flow sparc run <mode> "task" --parallel --monitor

# Execute TDD workflow
npx claude-flow sparc tdd "feature description"

# Batch execution
npx claude-flow sparc batch <mode1,mode2,mode3> "task"

# Pipeline execution
npx claude-flow sparc pipeline "task description"

Method 3: Local Installation

Best for: Projects with local claude-flow installation

# If claude-flow is installed locally
./claude-flow sparc run <mode> "task description"

Orchestration Patterns

Pattern 1: Hierarchical Coordination

Best for: Complex projects with clear delegation hierarchy

// 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" }

Pattern 2: Mesh Coordination

Best for: Collaborative tasks requiring peer-to-peer communication

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

Pattern 3: Sequential Pipeline

Best for: Ordered workflow execution (spec → design → code → test → review)

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"]
}

Pattern 4: Parallel Execution

Best for: Independent tasks that can run concurrently

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

Pattern 5: Adaptive Strategy

Best for: Dynamic workloads with changing requirements

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

TDD Workflows

Complete TDD Workflow

// 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-Green-Refactor Cycle

// 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 }
}

Best Practices

1. Memory Integration

Always use Memory for cross-agent coordination:

// 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"
}

2. Parallel Operations

Batch all related operations in single message:

// ✅ 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: [...] }

3. Hook Integration

Every SPARC mode should use hooks:

# Before work
npx claude-flow@alpha hooks pre-task --description "implement auth"

# During work
npx claude-flow@alpha hooks post-edit --file "auth.js"

# After work
npx claude-flow@alpha hooks post-task --task-id "task-123"

4. Test Coverage

Maintain minimum 90% coverage:

• Unit tests for all functions
• Integration tests for APIs
• E2E tests for critical flows
• Edge case coverage
• Error path testing

5. Documentation

Document as you build:

• API documentation (OpenAPI)
• Architecture decision records (ADR)
• Code comments for complex logic
• README with setup instructions
• Changelog for version tracking

6. File Organization

Never save to root folder:

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

Integration Examples

Example 1: Full-Stack Development

[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"}
  ]
}

Example 2: Research-Driven Innovation

// 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"
}

Example 3: Legacy Code Refactoring

// 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"
}

Common Workflows

Workflow 1: Feature Development

# Step 1: Research and planning
npx claude-flow sparc run researcher "authentication patterns"

# Step 2: Architecture design
npx claude-flow sparc run architect "design auth system"

# Step 3: TDD implementation
npx claude-flow sparc tdd "user authentication feature"

# Step 4: Code review
npx claude-flow sparc run reviewer "review auth implementation"

# Step 5: Documentation
npx claude-flow sparc run documenter "document auth API"

Workflow 2: Bug Investigation

# Step 1: Analyze issue
npx claude-flow sparc run analyzer "investigate bug #456"

# Step 2: Debug systematically
npx claude-flow sparc run debugger "fix memory leak in service X"

# Step 3: Create tests
npx claude-flow sparc run tester "regression tests for bug #456"

# Step 4: Review fix
npx claude-flow sparc run reviewer "validate bug fix"

Workflow 3: Performance Optimization

# Step 1: Profile performance
npx claude-flow sparc run analyzer "profile API response times"

# Step 2: Identify bottlenecks
npx claude-flow sparc run optimizer "optimize database queries"

# Step 3: Implement improvements
npx claude-flow sparc run coder "implement caching layer"

# Step 4: Benchmark results
npx claude-flow sparc run tester "performance benchmarks"

Workflow 4: Complete Pipeline

# Execute full development pipeline
npx claude-flow sparc pipeline "e-commerce checkout feature"

# This automatically runs:
# 1. researcher - Gather requirements
# 2. architect - Design system
# 3. coder - Implement features
# 4. tdd - Create comprehensive tests
# 5. reviewer - Code quality review
# 6. optimizer - Performance tuning
# 7. documenter - Documentation

Advanced Features

Neural Pattern Training

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

Cross-Session Memory

// 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"
}

GitHub Integration

// 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"
}

Performance Monitoring

// 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"
}

Performance Benefits

Proven Results:

• 84.8% SWE-Bench solve rate
• 32.3% token reduction through optimizations
• 2.8-4.4x speed improvement with parallel execution
• 27+ neural models for pattern learning
• 90%+ test coverage standard

Support and Resources

• Documentation: https://github.com/ruvnet/claude-flow
• Issues: https://github.com/ruvnet/claude-flow/issues
• NPM Package: https://www.npmjs.com/package/claude-flow
• Community: Discord server (link in repository)

Quick Reference

Most Common Commands

# List modes
npx claude-flow sparc modes

# Run specific mode
npx claude-flow sparc run <mode> "task"

# TDD workflow
npx claude-flow sparc tdd "feature"

# Full pipeline
npx claude-flow sparc pipeline "task"

# Batch execution
npx claude-flow sparc batch <modes> "task"

Most Common MCP Calls

// 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: "..." }

Remember: SPARC = Systematic, Parallel, Agile, Refined, Complete