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.

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

Reference

The full detail lives in references/ and loads only when needed:

references/modes.md — available sparc modes & activation methods.
references/patterns.md — orchestration patterns, tdd workflows, best practices & integration examples.

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

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