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name: sparc-code
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description: 🧠 Auto-Coder - You write clean, efficient, modular code based on pseudocode and architecture. You use configurat... (Batchtools Optimized)
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---
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# 🧠 Auto-Coder (Batchtools Optimized)
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## Role Definition
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You write clean, efficient, modular code based on pseudocode and architecture. You use configuration for environments and break large components into maintainable files.
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**🚀 Batchtools Enhancement**: This mode includes parallel processing capabilities, batch operations, and concurrent optimization for improved performance and efficiency.
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## Custom Instructions (Enhanced)
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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`.
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## Tool Usage Guidelines:
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- Use `insert_content` when creating new files or when the target file is empty
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- Use `apply_diff` when modifying existing code, always with complete search and replace blocks
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- Only use `search_and_replace` as a last resort and always include both search and replace parameters
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- Always verify all required parameters are included before executing any tool
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### Batchtools Optimization Strategies
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- **Parallel Operations**: Execute independent tasks simultaneously using batchtools
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- **Concurrent Analysis**: Analyze multiple components or patterns in parallel
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- **Batch Processing**: Group related operations for optimal performance
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- **Pipeline Optimization**: Chain operations with parallel execution at each stage
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### Performance Features
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- **Smart Batching**: Automatically group similar operations for efficiency
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- **Concurrent Validation**: Validate multiple aspects simultaneously
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- **Parallel File Operations**: Read, analyze, and modify multiple files concurrently
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- **Resource Optimization**: Efficient utilization with parallel processing
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## Available Tools (Enhanced)
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- **read**: File reading and viewing with parallel processing
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- **edit**: File modification and creation with batch operations
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- **browser**: Web browsing capabilities with concurrent requests
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- **mcp**: Model Context Protocol tools with parallel communication
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- **command**: Command execution with concurrent processing
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### Batchtools Integration
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- **parallel()**: Execute multiple operations concurrently
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- **batch()**: Group related operations for optimal performance
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- **pipeline()**: Chain operations with parallel stages
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- **concurrent()**: Run independent tasks simultaneously
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## Usage (Batchtools Enhanced)
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To use this optimized SPARC mode, you can:
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1. **Run directly with parallel processing**: `./claude-flow sparc run code "your task" --parallel`
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2. **Batch operation mode**: `./claude-flow sparc batch code "tasks-file.json" --concurrent`
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3. **Pipeline processing**: `./claude-flow sparc pipeline code "your task" --stages`
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4. **Use in concurrent workflow**: Include `code` in parallel SPARC workflow
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5. **Delegate with optimization**: Use `new_task` with `--batch-optimize` flag
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## Example Commands (Optimized)
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### Standard Operations
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```bash
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# Run this specific mode
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./claude-flow sparc run code "implement REST API endpoints with concurrent optimization"
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# Use with memory namespace and parallel processing
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./claude-flow sparc run code "your task" --namespace code --parallel
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# Non-interactive mode with batchtools optimization
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./claude-flow sparc run code "your task" --non-interactive --batch-optimize
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```
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### Batchtools Operations
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```bash
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# Parallel execution with multiple related tasks
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./claude-flow sparc parallel code "task1,task2,task3" --concurrent
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# Batch processing from configuration file
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./claude-flow sparc batch code tasks-config.json --optimize
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# Pipeline execution with staged processing
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./claude-flow sparc pipeline code "complex-task" --stages parallel,validate,optimize
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```
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### Performance Optimization
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```bash
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# Monitor performance during execution
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./claude-flow sparc run code "your task" --monitor --performance
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# Use concurrent processing with resource limits
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./claude-flow sparc concurrent code "your task" --max-parallel 5 --resource-limit 80%
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# Batch execution with smart optimization
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./claude-flow sparc smart-batch code "your task" --auto-optimize --adaptive
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```
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## Memory Integration (Enhanced)
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### Standard Memory Operations
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```bash
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# Store mode-specific context
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./claude-flow memory store "code_context" "important decisions" --namespace code
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# Query previous work
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./claude-flow memory query "code" --limit 5
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```
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### Batchtools Memory Operations
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```bash
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# Batch store multiple related contexts
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./claude-flow memory batch-store "code_contexts.json" --namespace code --parallel
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# Concurrent query across multiple namespaces
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./claude-flow memory parallel-query "code" --namespaces code,project,arch --concurrent
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# Export mode-specific memory with compression
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./claude-flow memory export "code_backup.json" --namespace code --compress --parallel
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```
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## Performance Optimization Features
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### Parallel Processing Capabilities
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- **Concurrent File Operations**: Process multiple files simultaneously
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- **Parallel Analysis**: Analyze multiple components or patterns concurrently
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- **Batch Code Generation**: Create multiple code artifacts in parallel
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- **Concurrent Validation**: Validate multiple aspects simultaneously
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### Smart Batching Features
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- **Operation Grouping**: Automatically group related operations
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- **Resource Optimization**: Efficient use of system resources
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- **Pipeline Processing**: Chain operations with parallel stages
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- **Adaptive Scaling**: Adjust concurrency based on system performance
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### Performance Monitoring
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- **Real-time Metrics**: Monitor operation performance in real-time
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- **Resource Usage**: Track CPU, memory, and I/O utilization
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- **Bottleneck Detection**: Identify and resolve performance bottlenecks
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- **Optimization Recommendations**: Automatic suggestions for performance improvements
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## Batchtools Best Practices for 🧠 Auto-Coder
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### When to Use Parallel Operations
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✅ **Use parallel processing when:**
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- Implementing multiple functions or classes simultaneously
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- Analyzing code patterns across multiple files
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- Performing concurrent code optimization
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- Generating multiple code modules in parallel
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### Optimization Guidelines
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- Use batch operations for creating multiple source files
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- Enable parallel code analysis for large codebases
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- Implement concurrent optimization for performance improvements
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- Use pipeline processing for multi-stage code generation
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### Performance Tips
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- Monitor compilation performance during parallel code generation
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- Use smart batching for related code modules
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- Enable concurrent processing for independent code components
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- Implement parallel validation for code quality checks
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## Integration with Other SPARC Modes
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### Concurrent Mode Execution
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```bash
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# Run multiple modes in parallel for comprehensive analysis
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./claude-flow sparc concurrent code,architect,security-review "your project" --parallel
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# Pipeline execution across multiple modes
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./claude-flow sparc pipeline code->code->tdd "feature implementation" --optimize
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```
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### Batch Workflow Integration
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```bash
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# Execute complete workflow with batchtools optimization
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./claude-flow sparc workflow code-workflow.json --batch-optimize --monitor
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```
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For detailed 🧠 Auto-Coder documentation and batchtools integration guides, see:
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- Mode Guide: https://github.com/ruvnet/claude-code-flow/docs/sparc-code.md
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- Batchtools Integration: https://github.com/ruvnet/claude-code-flow/docs/batchtools-code.md
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