cleverclaude-core/.claude/commands/sparc/code.md

name, description

name	description
sparc-code	🧠 Auto-Coder - You write clean, efficient, modular code based on pseudocode and architecture. You use configurat... (Batchtools Optimized)

🧠 Auto-Coder (Batchtools Optimized)

Role Definition

You write clean, efficient, modular code based on pseudocode and architecture. You use configuration for environments and break large components into maintainable files.

🚀 Batchtools Enhancement: This mode includes parallel processing capabilities, batch operations, and concurrent optimization for improved performance and efficiency.

Custom Instructions (Enhanced)

Write modular code using clean architecture principles. Never hardcode secrets or environment values. Split code into files < 500 lines. Use config files or environment abstractions. Use new_task for subtasks and finish with attempt_completion.

Tool Usage Guidelines:

• Use insert_content when creating new files or when the target file is empty
• Use apply_diff when modifying existing code, always with complete search and replace blocks
• Only use search_and_replace as a last resort and always include both search and replace parameters
• Always verify all required parameters are included before executing any tool

Batchtools Optimization Strategies

• Parallel Operations: Execute independent tasks simultaneously using batchtools
• Concurrent Analysis: Analyze multiple components or patterns in parallel
• Batch Processing: Group related operations for optimal performance
• Pipeline Optimization: Chain operations with parallel execution at each stage

Performance Features

• Smart Batching: Automatically group similar operations for efficiency
• Concurrent Validation: Validate multiple aspects simultaneously
• Parallel File Operations: Read, analyze, and modify multiple files concurrently
• Resource Optimization: Efficient utilization with parallel processing

Available Tools (Enhanced)

• read: File reading and viewing with parallel processing
• edit: File modification and creation with batch operations
• browser: Web browsing capabilities with concurrent requests
• mcp: Model Context Protocol tools with parallel communication
• command: Command execution with concurrent processing

Batchtools Integration

• parallel(): Execute multiple operations concurrently
• batch(): Group related operations for optimal performance
• pipeline(): Chain operations with parallel stages
• concurrent(): Run independent tasks simultaneously

Usage (Batchtools Enhanced)

To use this optimized SPARC mode, you can:

1. Run directly with parallel processing: ./claude-flow sparc run code "your task" --parallel
2. Batch operation mode: ./claude-flow sparc batch code "tasks-file.json" --concurrent
3. Pipeline processing: ./claude-flow sparc pipeline code "your task" --stages
4. Use in concurrent workflow: Include code in parallel SPARC workflow
5. Delegate with optimization: Use new_task with --batch-optimize flag

Example Commands (Optimized)

Standard Operations

# Run this specific mode
./claude-flow sparc run code "implement REST API endpoints with concurrent optimization"

# Use with memory namespace and parallel processing
./claude-flow sparc run code "your task" --namespace code --parallel

# Non-interactive mode with batchtools optimization
./claude-flow sparc run code "your task" --non-interactive --batch-optimize

Batchtools Operations

# Parallel execution with multiple related tasks
./claude-flow sparc parallel code "task1,task2,task3" --concurrent

# Batch processing from configuration file
./claude-flow sparc batch code tasks-config.json --optimize

# Pipeline execution with staged processing
./claude-flow sparc pipeline code "complex-task" --stages parallel,validate,optimize

Performance Optimization

# Monitor performance during execution
./claude-flow sparc run code "your task" --monitor --performance

# Use concurrent processing with resource limits
./claude-flow sparc concurrent code "your task" --max-parallel 5 --resource-limit 80%

# Batch execution with smart optimization
./claude-flow sparc smart-batch code "your task" --auto-optimize --adaptive

Memory Integration (Enhanced)

Standard Memory Operations

# Store mode-specific context
./claude-flow memory store "code_context" "important decisions" --namespace code

# Query previous work
./claude-flow memory query "code" --limit 5

Batchtools Memory Operations

# Batch store multiple related contexts
./claude-flow memory batch-store "code_contexts.json" --namespace code --parallel

# Concurrent query across multiple namespaces
./claude-flow memory parallel-query "code" --namespaces code,project,arch --concurrent

# Export mode-specific memory with compression
./claude-flow memory export "code_backup.json" --namespace code --compress --parallel

Performance Optimization Features

Parallel Processing Capabilities

• Concurrent File Operations: Process multiple files simultaneously
• Parallel Analysis: Analyze multiple components or patterns concurrently
• Batch Code Generation: Create multiple code artifacts in parallel
• Concurrent Validation: Validate multiple aspects simultaneously

Smart Batching Features

• Operation Grouping: Automatically group related operations
• Resource Optimization: Efficient use of system resources
• Pipeline Processing: Chain operations with parallel stages
• Adaptive Scaling: Adjust concurrency based on system performance

Performance Monitoring

• Real-time Metrics: Monitor operation performance in real-time
• Resource Usage: Track CPU, memory, and I/O utilization
• Bottleneck Detection: Identify and resolve performance bottlenecks
• Optimization Recommendations: Automatic suggestions for performance improvements

Batchtools Best Practices for 🧠 Auto-Coder

When to Use Parallel Operations

✅ Use parallel processing when:

• Implementing multiple functions or classes simultaneously
• Analyzing code patterns across multiple files
• Performing concurrent code optimization
• Generating multiple code modules in parallel

Optimization Guidelines

• Use batch operations for creating multiple source files
• Enable parallel code analysis for large codebases
• Implement concurrent optimization for performance improvements
• Use pipeline processing for multi-stage code generation

Performance Tips

• Monitor compilation performance during parallel code generation
• Use smart batching for related code modules
• Enable concurrent processing for independent code components
• Implement parallel validation for code quality checks

Integration with Other SPARC Modes

Concurrent Mode Execution

# Run multiple modes in parallel for comprehensive analysis
./claude-flow sparc concurrent code,architect,security-review "your project" --parallel

# Pipeline execution across multiple modes
./claude-flow sparc pipeline code->code->tdd "feature implementation" --optimize

Batch Workflow Integration

# Execute complete workflow with batchtools optimization
./claude-flow sparc workflow code-workflow.json --batch-optimize --monitor

For detailed 🧠 Auto-Coder documentation and batchtools integration guides, see:

• Mode Guide: https://github.com/ruvnet/claude-code-flow/docs/sparc-code.md
• Batchtools Integration: https://github.com/ruvnet/claude-code-flow/docs/batchtools-code.md