Structures software development into five phases to improve output quality through multi-agent orchestration.
Install
mkdir -p .claude/skills/sparc-methodology && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/325" && unzip -o skill.zip -d .claude/skills/sparc-methodology && rm skill.zipInstalls to .claude/skills/sparc-methodology
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestrationKey capabilities
- →Decompose complex software tasks into systematic development phases
- →Execute parallel agent coordination for multi-agent workflows
- →Implement test-driven development cycles with automated validation
- →Perform batch file operations and code refactoring
- →Manage persistent cross-session memory for architectural decisions
- →Conduct automated code quality, security, and performance reviews
How it works
The methodology uses 17 specialized modes to orchestrate agents through five distinct phases: Specification, Pseudocode, Architecture, Refinement, and Completion. It utilizes parallel execution and memory integration to maintain consistency and speed across development tasks.
Inputs & outputs
When to use sparc-methodology
- →Planning a new software project
- →Ensuring TDD standards during dev
- →Structuring multi-agent coding workflows
About this skill
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
- Core Philosophy
- Development Phases
- Available Modes
- Activation Methods
- Orchestration Patterns
- TDD Workflows
- Best Practices
- Integration Examples
- 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
- Specification Before Code: Define requirements and constraints clearly
- Design Before Implementation: Plan architecture and components
- Tests Before Features: Write failing tests, then make them pass
- Review Everything: Code quality, security, and performance checks
- 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:
- Write failing test (RED)
- Implement minimum code
- Make test pass (GREEN)
- Refactor for quality (REFACTOR)
- 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
Content truncated.
When not to use it
- →Simple scripts that do not require architectural planning or multi-agent orchestration
- →Projects where test-driven development or strict quality standards are not required
Prerequisites
Limitations
- →Requires adherence to specific SPARC modes and workflow structures
- →Performance benefits depend on the ability to parallelize tasks
How it compares
Unlike manual development, this methodology automates the entire lifecycle through predefined agent modes and cross-agent communication, enabling concurrent task processing and persistent knowledge sharing.
Compared to similar skills
sparc-methodology side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| sparc-methodology (this skill) | 8 | 4mo | Review | Intermediate |
| create-plan | 36 | 8mo | Review | Beginner |
| project-planner | 32 | 9mo | Review | Intermediate |
| system-design | 19 | 9mo | No flags | Intermediate |
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Example prompts that trigger this skill in your AI assistant.
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