majiayu000 SPARC Development Framework
Behavioral Classification
Type: Guided Decision
This skill guides you through development methodology decisions and then executes based on your choices.
Decision Points:
- Which SPARC mode(s) to use for your task?
- Swarm topology (hierarchical, mesh, ring, star)?
- Parallel or sequential execution?
- Test coverage target?
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.
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
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
Quick Start
MCP Tools (Preferred)
// Initialize swarm
mcp__claude-flow__swarm_init { topology: "hierarchical", maxAgents: 8 }
// Execute a mode
mcp__claude-flow__sparc_mode {
mode: "coder",
task_description: "implement user authentication with JWT",
options: { test_driven: true }
}
CLI (Terminal)
# List all modes
npx claude-flow sparc modes
# Run specific mode
npx claude-flow sparc run <mode> "task description"
# TDD workflow
npx claude-flow sparc tdd "feature description"
# Full pipeline
npx claude-flow sparc pipeline "task description"
Sub-Documentation
For detailed information, see the following files:
| Document | Contents |
|---|---|
| Development Phases | 5 SPARC phases from Specification to Completion |
| Available Modes | All 17 specialized modes with usage examples |
| Orchestration Patterns | Swarm topologies, coordination patterns |
| Workflows | TDD workflows, common workflows, advanced features |
Available Modes (17 total)
Core Orchestration
| Mode | Description |
|---|---|
orchestrator |
Multi-agent task orchestration |
swarm-coordinator |
Swarm management for multi-agent workflows |
workflow-manager |
Process automation and workflow orchestration |
batch-executor |
Parallel task execution for high-throughput |
Development
| Mode | Description |
|---|---|
coder |
Autonomous code generation |
architect |
System design with Memory coordination |
tdd |
Test-driven development |
reviewer |
Code review and quality analysis |
Analysis & Research
| Mode | Description |
|---|---|
researcher |
Deep research with parallel web searches |
analyzer |
Code and data analysis |
optimizer |
Performance optimization |
Creative & Support
| Mode | Description |
|---|---|
designer |
UI/UX design with accessibility focus |
innovator |
Creative problem-solving |
documenter |
Documentation generation |
debugger |
Systematic debugging |
tester |
Comprehensive testing beyond TDD |
memory-manager |
Knowledge management |
Activation Methods
Method 1: MCP Tools (Preferred)
mcp__claude-flow__sparc_mode {
mode: "<mode-name>",
task_description: "<task description>",
options: { /* mode-specific options */ }
}
Method 2: NPX CLI
npx claude-flow sparc run <mode> "task description"
Method 3: Local Installation
./claude-flow sparc run <mode> "task description"
Quick Reference
Most Common Commands
npx claude-flow sparc modes # List modes
npx claude-flow sparc run <mode> "task" # Run specific mode
npx claude-flow sparc tdd "feature" # TDD workflow
npx claude-flow sparc pipeline "task" # Full pipeline
npx claude-flow sparc batch <modes> "task" # Batch execution
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: "..." }
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
Remember: SPARC = Specification, Pseudocode, Architecture, Refinement, Completion
Version: 3.0.0 Last Updated: 2025-01-24