⚡Introducing Claude-SPARC: Automated Development System For Claude Code

I’ve found my new obsession, and it’s Claude Code. After digging in, I figured out how to completely automate it for low cost long-horizon development.

I’m talking fully autonomous research, testing, git commits, pull requests, file editing, code reviews, and even deployments. It runs end-to-end with zero manual input, handling everything from initial research to final delivery. Once set up, it becomes a self-steering dev loop that just keeps building and improving whatever you point it at.

The best part is because it's using Claude Max, it super cheap to run, it doesn't require constant API requests. Unlimited use for $100

What is it?

Claude SPARC is my new multi-threaded, autonomous coding system that blends Claude Code CLI with the SPARC methodology: Specification, Pseudocode, Architecture, Refinement, and Completion.

It operates fully unattended, leveraging --dangerously-skip-permissions, MCPs, and recursive task loops. It's how I built a working Wi-Fi-based human pose estimation system, literally just by pointing this system at the problem and letting it run.

I’ve had this running continuously for over 40 hours without a single crash. It spins up SPARC tasks in parallel, using tools like Bash, Grep, Edit, and WebFetch and various MCPs coordinated across subtasks. Each prompt is autonomous. It runs tests, fixes bugs, commits code, and even handles context pivots.

If you’re on Claude’s MAX plan, you’ll likely hit no real limits. Opus eventually rate-limits or times out, but Claude Max handles long flows reliably. I haven’t had to restart it once.

A basic build pipeline starts like this:

From there, it hands off to pseudocode, scaffolding, and refines the codebase. The design mirrors the same boomerang-style flow I built for Roo Code, only cheaper and Claude-native.

I'll be sharing a few example projects shortly. But in short: Claude SPARC can build almost anything you aim it at. Worth a look.

Features

• Comprehensive Research Phase: Automated web research using parallel batch operations

• Full SPARC Methodology: Complete implementation of all 5 SPARC phases

• TDD London School: Test-driven development with mocks and behavior testing

• Parallel Orchestration: Concurrent development tracks and batch operations

• Quality Assurance: Automated linting, testing, and security validation

• Detailed Commit History: Structured commit messages for each development phase

Download it here: (claude-code.sh)

https://gist.github.com/ruvnet/e8bb444c6149e6e060a785d1a693a194

Basic Usage

With Arguments

Help

Command Line Options

Core Options

• -h, --help - Show help message and exit

• -v, --verbose - Enable verbose output for detailed logging

• -d, --dry-run - Show what would be executed without running

• -c, --config FILE - Specify MCP configuration file (default: .roo/mcp.json)

Research Options

• --skip-research - Skip the web research phase entirely

• --research-depth LEVEL - Set research depth: basic, standard, comprehensive (default: standard)

Development Options

• --mode MODE - Development mode: full, backend-only, frontend-only, api-only (default: full)

• --skip-tests - Skip test development (not recommended)

• --coverage TARGET - Test coverage target percentage (default: 100)

• --no-parallel - Disable parallel execution

Commit Options

• --commit-freq FREQ - Commit frequency: phase, feature, manual (default: phase)

• --no-commits - Disable automatic commits

Output Options

• --output FORMAT - Output format: text, json, markdown (default: text)

• --quiet - Suppress non-essential output

Examples

Basic Development

Research Configuration

Development Modes

Testing and Quality

Advanced Usage

SPARC Phases Explained

Phase 0: Research & Discovery

• Parallel Web Research: Uses BatchTool and WebFetchTool for comprehensive domain research

• Technology Stack Analysis: Researches best practices and framework comparisons

• Implementation Patterns: Gathers code examples and architectural patterns

• Competitive Analysis: Studies existing solutions and industry trends

Phase 1: Specification

• Requirements Analysis: Extracts functional and non-functional requirements

• User Stories: Defines acceptance criteria and system boundaries

• Technical Constraints: Identifies technology stack and deployment requirements

• Performance Targets: Establishes SLAs and scalability goals

Phase 2: Pseudocode

• High-Level Architecture: Defines major components and data flow

• Algorithm Design: Core business logic and optimization strategies

• Test Strategy: TDD approach with comprehensive test planning

• Error Handling: Recovery strategies and validation algorithms

Phase 3: Architecture

• Component Architecture: Detailed specifications and interface definitions

• Data Architecture: Database design and access patterns

• Infrastructure Architecture: Deployment and CI/CD pipeline design

• Security Architecture: Access controls and compliance requirements

Phase 4: Refinement (TDD Implementation)

• Parallel Development Tracks: Backend, frontend, and integration tracks

• TDD London School: Red-Green-Refactor cycles with behavior testing

• Quality Gates: Automated linting, analysis, and security scans

• Performance Optimization: Benchmarking and critical path optimization

Phase 5: Completion

• System Integration: End-to-end testing and requirement validation

• Documentation: API docs, deployment guides, and runbooks

• Production Readiness: Monitoring, alerting, and security review

• Deployment: Automated deployment with validation

Tool Utilization

Core Tools

• BatchTool: Parallel execution of independent operations

• WebFetchTool: Comprehensive research and documentation gathering

• Bash: Git operations, CI/CD, testing, and deployment

• Edit/Replace: Code implementation and refactoring

• GlobTool/GrepTool: Code analysis and pattern detection

• dispatch_agent: Complex subtask delegation

Quality Assurance Tools

• Linting: ESLint, Prettier, markdownlint

• Testing: Jest, Vitest, Cypress for comprehensive coverage

• Security: Security scans and vulnerability assessments

• Performance: Benchmarking and profiling tools

• Documentation: Automated API documentation generation

Development Standards

Code Quality

• Modularity: Files ≤ 500 lines, functions ≤ 50 lines

• Security: No hardcoded secrets, comprehensive input validation

• Testing: 100% test coverage with TDD London School approach

• Documentation: Self-documenting code with strategic comments

• Performance: Optimized critical paths with benchmarking

Commit Standards

• feat: New features and major functionality

• test: Test implementation and coverage improvements

• fix: Bug fixes and issue resolution

• docs: Documentation updates and improvements

• arch: Architectural changes and design updates

• quality: Code quality improvements and refactoring

• deploy: Deployment and infrastructure changes

Parallel Execution Strategy

Research Phase

Development Phase

Quality Assurance

Success Criteria

• ✅ 100% Test Coverage: All code covered by comprehensive tests

• ✅ Quality Gates Passed: Linting, security, and performance validation

• ✅ Production Deployment: Successful deployment with monitoring

• ✅ Documentation Complete: Comprehensive docs and runbooks

• ✅ Security Validated: Security scans and compliance checks

• ✅ Performance Optimized: Benchmarks meet or exceed targets

Configuration

MCP Configuration

The system uses .roo/mcp.json for MCP server configuration. Ensure your MCP setup includes:

• File system access

• Web search capabilities

• Git integration

• Testing frameworks

Allowed Tools

The script automatically configures the following tools:

Examples

Web Application Development

API Service Development

Data Processing Pipeline

Troubleshooting

Common Issues

1. MCP Configuration: Ensure .roo/mcp.json is properly configured

2. Tool Permissions: Use --dangerously-skip-permissions for development

3. Network Access: Ensure internet connectivity for web research

4. Git Configuration: Ensure git is configured for commits