sparc-methodology

SPARC Methodology - Comprehensive Development Framework

Overview

SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) is a systematic development methodology integrated with Claude Flow's multi-agent orchestration capabilities. It provides 17 specialized modes for comprehensive software development, from initial research through deployment and monitoring.

Table of Contents

1. [Core Philosophy](#core-philosophy)
2. [Development Phases](#development-phases)
3. [Available Modes](#available-modes)
4. [Activation Methods](#activation-methods)
5. [Orchestration Patterns](#orchestration-patterns)
6. [TDD Workflows](#tdd-workflows)
7. [Best Practices](#best-practices)
8. [Integration Examples](#integration-examples)
9. [Common Workflows](#common-workflows)

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 {
  mo