# Code Mode Workflow

## Overview

The Code mode workflow orchestrates comprehensive code development processes using SAFLA's MCP tools and CLI commands. This workflow emphasizes test-driven development, continuous integration, and adaptive optimization through MCP interfaces and command-line operations.

## Core Workflow Phases

### 1. Environment Setup and Validation

#### Initial Environment Configuration
```bash
# Validate SAFLA installation for development
use_mcp_tool safla validate_installation '{}'

# Get system information for development planning
use_mcp_tool safla get_system_info '{}'

# Check GPU availability for accelerated development
use_mcp_tool safla check_gpu_status '{}'

# Setup development environment via CLI
python -m safla.env --setup \
  --development \
  --auto-configure \
  --install-dependencies \
  --configure-tools
```

#### Environment Validation and Optimization
```bash
# Validate environment configuration
python -m safla.env --validate \
  --check-dependencies \
  --verify-tools \
  --test-connectivity \
  --export env_validation.json

# Optimize development environment
use_mcp_tool safla optimize_memory_usage '{
  "optimization_level": "balanced",
  "target_memory_mb": 4096
}'

# Access current configuration
access_mcp_resource safla "safla://config"
```

### 2. Specification Analysis and Planning

#### Specification Processing Using MCP
```bash
# Create specification analysis agent
use_mcp_tool safla create_agent_session '{
  "agent_type": "cognitive",
  "session_config": {
    "focus": "specification_analysis",
    "analysis_depth": "comprehensive",
    "output_format": "implementation_ready"
  },
  "timeout_seconds": 3600
}'

# Analyze project specifications
use_mcp_tool safla interact_with_agent '{
  "session_id": "spec_analyzer_001",
  "command": "analyze_specifications",
  "parameters": {
    "spec_files": ["phase_1_spec.md", "prompts_LS1.md"],
    "analysis_type": "implementation_planning"
  }
}'
```

#### CLI-Based Specification Analysis
```bash
# Process specification files
python -m safla.spec --analyze \
  --files phase_1_spec.md,prompts_LS1.md \
  --extract-requirements \
  --generate-tasks \
  --export spec_analysis.json

# Validate specification completeness
python -m safla.spec --validate \
  --completeness-check \
  --consistency-check \
  --export validation_report.json

# Generate implementation plan
python -m safla.plan --generate \
  --from-spec spec_analysis.json \
  --architecture modular \
  --testing-strategy comprehensive \
  --export implementation_plan.json
```

### 3. Test-Driven Development Phase

#### Test Specification Creation
```bash
# Create TDD agent for test development
use_mcp_tool safla create_agent_session '{
  "agent_type": "cognitive",
  "session_config": {
    "focus": "test_driven_development",
    "testing_framework": "jest",
    "coverage_target": 95
  },
  "timeout_seconds": 3600
}'

# Generate comprehensive test specifications
python -m safla.tdd --generate-tests \
  --spec-file implementation_plan.json \
  --coverage-target 95 \
  --framework jest \
  --export test_specifications.json

# Validate test specifications
python -m safla.test --validate \
  --test-files test_specifications.json \
  --quality-metrics coverage,maintainability,readability \
  --export test_validation.json
```

#### Test Implementation and Validation
```bash
# Implement test suite
python -m safla.tdd --implement-tests \
  --specifications test_specifications.json \
  --framework jest \
  --parallel-execution \
  --export test_implementation.json

# Run initial test suite (should fail - Red phase)
python -m safla.test --run \
  --test-suite test_implementation.json \
  --expect-failures \
  --export initial_test_results.json

# Access test results via resource
access_mcp_resource safla "safla://test-results"
```

### 4. Implementation Phase

#### Agent-Based Code Implementation
```bash
# Create implementation agent
use_mcp_tool safla create_agent_session '{
  "agent_type": "cognitive",
  "session_config": {
    "focus": "code_implementation",
    "language": "typescript",
    "architecture": "modular",
    "max_file_lines": 500
  },
  "timeout_seconds": 7200
}'

# Implement core functionality
use_mcp_tool safla interact_with_agent '{
  "session_id": "impl_agent_001",
  "command": "implement_module",
  "parameters": {
    "module_name": "core_functionality",
    "test_specifications": "test_specifications.json",
    "implementation_plan": "implementation_plan.json"
  }
}'
```

#### CLI Implementation Workflow
```bash
# Generate code scaffolding
python -m safla.code --scaffold \
  --template modular_typescript \
  --spec-file implementation_plan.json \
  --output-dir src/ \
  --max-lines 500

# Implement with TDD approach (Green phase)
python -m safla.code --implement \
  --tdd-mode \
  --test-first \
  --module-limit 500 \
  --auto-document \
  --export implementation_progress.json

# Run tests to validate implementation
python -m safla.test --run \
  --comprehensive \
  --coverage-report \
  --export implementation_test_results.json
```

### 5. Code Quality and Optimization Phase

#### Code Quality Analysis and Improvement
```bash
# Comprehensive code analysis
python -m safla.code --analyze \
  --static-analysis \
  --security-scan \
  --performance-profile \
  --complexity-analysis \
  --export code_analysis.json

# Lint and fix code issues
python -m safla.code --lint \
  --fix-auto \
  --standards typescript,security,performance \
  --export lint_report.json

# Refactor for quality (Refactor phase)
python -m safla.code --refactor \
  --extract-methods \
  --reduce-complexity \
  --improve-naming \
  --export refactor_report.json
```

#### Performance Optimization
```bash
# Optimize code performance using MCP
use_mcp_tool safla optimize_vector_operations '{
  "batch_size": 200,
  "use_gpu": true
}'

# Analyze performance bottlenecks
use_mcp_tool safla analyze_performance_bottlenecks '{
  "duration_seconds": 300,
  "include_memory_profile": true
}'

# CLI performance optimization
python -m safla.code --optimize \
  --performance-focus \
  --memory-efficient \
  --maintain-readability \
  --export optimization_report.json
```

### 6. Testing and Validation Phase

#### Comprehensive Testing Workflow
```bash
# Run full test suite
use_mcp_tool safla run_integration_tests '{
  "test_suite": "comprehensive_validation",
  "parallel": true,
  "verbose": true
}'

# Validate memory operations
use_mcp_tool safla validate_memory_operations '{
  "test_data_size": 200,
  "include_stress_test": true
}'

# Performance testing
python -m safla.test --performance \
  --benchmark-tests \
  --load-testing \
  --stress-testing \
  --export performance_test_results.json
```

#### Coverage and Quality Validation
```bash
# Analyze test coverage
python -m safla.coverage --analyze \
  --line-coverage \
  --branch-coverage \
  --function-coverage \
  --export coverage_analysis.json

# Access coverage metrics via resource
access_mcp_resource safla "safla://test-coverage"

# Validate quality gates
python -m safla.code --validate \
  --quality-gates \
  --coverage-threshold 95 \
  --complexity-threshold 10 \
  --export quality_validation.json
```

### 7. Integration and Deployment Preparation

#### Integration Testing
```bash
# Test MCP connectivity
use_mcp_tool safla test_mcp_connectivity '{
  "target_server": "safla",
  "test_depth": "comprehensive"
}'

# Integration testing via CLI
python -m safla.integration --test \
  --api-integration \
  --database-integration \
  --service-integration \
  --export integration_results.json

# End-to-end testing
python -m safla.e2e --test \
  --user-workflows \
  --cross-platform \
  --export e2e_results.json
```

#### Deployment Preparation
```bash
# Build for production
python -m safla.build --production \
  --optimization-enabled \
  --minification \
  --source-maps \
  --export build_artifacts/

# Deploy development instance
use_mcp_tool safla deploy_safla_instance '{
  "instance_name": "code_deployment_test",
  "environment": "staging",
  "config_overrides": {
    "code_optimization": "enabled",
    "performance_monitoring": true
  }
}'

# Monitor deployment status
use_mcp_tool safla check_deployment_status '{
  "instance_name": "code_deployment_test"
}'
```

## Agent Coordination Workflows

### 1. Multi-Agent Development Coordination

#### Agent Session Management
```bash
# Create multiple specialized agents
python -m safla.agents --batch-create \
  --config agents_config.yaml \
  --coordinate \
  --monitor

# List active development agents
use_mcp_tool safla list_agent_sessions '{
  "filter_by_type": "cognitive",
  "include_inactive": false
}'

# Coordinate agents for complex tasks
python -m safla.agents --coordinate \
  --sessions impl_agent_001,test_agent_002,review_agent_003 \
  --task complex_feature_implementation \
  --monitor-progress
```

#### Agent Task Distribution
```bash
# Distribute implementation tasks
use_mcp_tool safla interact_with_agent '{
  "session_id": "impl_agent_001",
  "command": "implement_core_logic",
  "parameters": {
    "module": "data_processor",
    "priority": "high"
  }
}'

use_mcp_tool safla interact_with_agent '{
  "session_id": "test_agent_002",
  "command": "generate_comprehensive_tests",
  "parameters": {
    "module": "data_processor",
    "coverage_target": 95
  }
}'

# Monitor agent performance
access_mcp_resource safla "safla://agent-sessions"
```

### 2. Adaptive Development Workflows

#### Learning and Adaptation Integration
```bash
# Trigger learning cycle for development patterns
use_mcp_tool safla trigger_learning_cycle '{
  "learning_type": "meta",
  "data_sources": ["code_metrics", "performance_data", "quality_assessments"],
  "focus_areas": ["code_optimization", "pattern_recognition"]
}'

# Update learning parameters based on development outcomes
use_mcp_tool safla update_learning_parameters '{
  "learning_rate": 0.15,
  "adaptation_threshold": 0.1,
  "exploration_factor": 0.2
}'

# Access learning metrics
access_mcp_resource safla "safla://learning-metrics"
```

#### Strategy Selection and Optimization
```bash
# Select optimal development strategy
use_mcp_tool safla select_optimal_strategy '{
  "context": "complex_algorithm_implementation",
  "constraints": {
    "time_limit": "4_hours",
    "performance_requirements": "high"
  },
  "objectives": ["performance", "maintainability", "testability"]
}'

# Create custom development strategy
use_mcp_tool safla create_custom_strategy '{
  "strategy_name": "performance_focused_tdd",
  "description": "TDD with performance optimization focus",
  "context": "high_performance_module",
  "steps": [
    "write_performance_tests",
    "implement_basic_functionality",
    "optimize_for_performance",
    "validate_benchmarks"
  ]
}'
```

## Error Handling and Recovery Workflows

### 1. Error Detection and Analysis

#### Automated Error Detection
```bash
# Monitor system health for development errors
use_mcp_tool safla monitor_system_health '{
  "check_interval": 30,
  "alert_thresholds": {
    "compilation_errors": 5,
    "test_failures": 10,
    "performance_degradation": 20
  }
}'

# Analyze error patterns
python -m safla.errors --analyze \
  --pattern-detection \
  --root-cause-analysis \
  --export error_analysis.json

# Access system logs for debugging
access_mcp_resource safla "safla://system-logs"
```

#### Error Recovery Procedures
```bash
# Apply automated fixes
python -m safla.code --fix \
  --auto-correct \
  --safe-mode \
  --backup-original \
  --export fix_report.json

# Validate fixes through testing
python -m safla.test --run \
  --regression-check \
  --validate-fixes \
  --export fix_validation.json

# Trigger learning from error patterns
use_mcp_tool safla trigger_learning_cycle '{
  "learning_type": "reinforcement",
  "data_sources": ["error_logs", "fix_outcomes"],
  "focus_areas": ["error_prevention", "code_quality"]
}'
```

### 2. Performance Recovery Workflows

#### Performance Issue Detection
```bash
# Benchmark current performance
use_mcp_tool safla benchmark_memory_performance '{
  "test_duration": 180,
  "memory_patterns": ["sequential", "random", "mixed"]
}'

# Compare with performance baselines
access_mcp_resource safla "safla://performance-baselines"

# Identify performance regressions
python -m safla.regression --performance \
  --baseline-period 7d \
  --current-period 1d \
  --metrics latency,throughput,memory_usage
```

#### Performance Optimization Recovery
```bash
# Apply performance optimizations
use_mcp_tool safla optimize_memory_usage '{
  "optimization_level": "aggressive",
  "target_memory_mb": 6144
}'

# Optimize vector operations
use_mcp_tool safla optimize_vector_operations '{
  "batch_size": 250,
  "use_gpu": true
}'

# Validate performance recovery
python -m safla.benchmark --code \
  --compare-with-baseline \
  --export recovery_validation.json
```

## Continuous Integration Workflows

### 1. CI/CD Pipeline Integration

#### Pipeline Configuration and Execution
```bash
# Configure CI/CD pipeline
python -m safla.cicd --configure \
  --github-actions \
  --automated-testing \
  --quality-gates \
  --deployment-automation \
  --export cicd_config.yaml

# Run CI/CD pipeline locally
python -m safla.cicd --run \
  --local-execution \
  --full-pipeline \
  --parallel-jobs \
  --export pipeline_results.json

# Validate pipeline configuration
python -m safla.cicd --validate \
  --configuration-check \
  --dependency-verification \
  --export pipeline_validation.json
```

#### Automated Quality Assurance
```bash
# Setup automated code review
python -m safla.automation --code-review \
  --quality-checks \
  --security-scanning \
  --performance-analysis \
  --export review_automation.json

# Automated testing workflows
python -m safla.automation --testing \
  --test-execution \
  --coverage-reporting \
  --performance-testing \
  --export test_automation.json
```

### 2. Deployment and Monitoring Workflows

#### Production Deployment
```bash
# Deploy to production
python -m safla.deploy --production \
  --zero-downtime \
  --health-checks \
  --rollback-ready \
  --monitoring-enabled \
  --export deployment_log.json

# Monitor deployment health
python -m safla.monitor --deployment \
  --real-time \
  --performance-metrics \
  --error-tracking \
  --export monitoring_data.json

# Post-deployment validation
python -m safla.validate --deployment \
  --functionality-check \
  --performance-validation \
  --export validation_results.json
```

#### Continuous Monitoring
```bash
# Real-time performance monitoring
python -m safla.monitor --performance \
  --real-time \
  --code-metrics \
  --resource-usage \
  --alerts webhook:http://alerts.example.com

# Access real-time metrics
access_mcp_resource safla "safla://performance-metrics"

# Monitor optimization recommendations
access_mcp_resource safla "safla://optimization-recommendations"
```

## Goal-Driven Development Workflows

### 1. Goal Setting and Tracking

#### Development Goal Creation
```bash
# Create code quality goal
use_mcp_tool safla create_goal '{
  "goal_name": "high_quality_implementation",
  "description": "Achieve 95% test coverage with zero critical issues",
  "priority": "high",
  "target_metrics": {
    "test_coverage": 0.95,
    "critical_issues": 0,
    "performance_score": 90
  },
  "deadline": 1735689600
}'

# Create performance optimization goal
use_mcp_tool safla create_goal '{
  "goal_name": "performance_excellence",
  "description": "Reduce response time by 30% while maintaining quality",
  "priority": "high",
  "target_metrics": {
    "response_time_reduction": 0.3,
    "quality_score": 90
  }
}'
```

#### Goal Progress Monitoring
```bash
# Evaluate goal progress
use_mcp_tool safla evaluate_goal_progress '{
  "goal_id": "high_quality_implementation",
  "include_recommendations": true
}'

# Access goals via resource
access_mcp_resource safla "safla://goals"

# Update goal status based on progress
use_mcp_tool safla update_goal '{
  "goal_id": "high_quality_implementation",
  "progress": 0.87,
  "notes": "Test coverage at 92.3%, approaching target"
}'
```

## Workflow Completion and Handoff

### 1. Task Completion Validation

#### Final Quality Assurance
```bash
# Comprehensive final validation
use_mcp_tool safla run_integration_tests '{
  "test_suite": "final_validation",
  "parallel": true,
  "verbose": true
}'

# Performance validation
use_mcp_tool safla benchmark_memory_performance '{
  "test_duration": 300,
  "memory_patterns": ["production_simulation"]
}'

# Security validation
python -m safla.security --final-scan \
  --comprehensive \
  --production-ready \
  --export final_security_report.json
```

#### Documentation and Reporting
```bash
# Generate comprehensive documentation
python -m safla.docs --generate \
  --comprehensive \
  --api-docs \
  --usage-examples \
  --export documentation/

# Create implementation report
python -m safla.report --implementation \
  --metrics \
  --quality-assessment \
  --performance-analysis \
  --export implementation_report.json

# Backup final state
use_mcp_tool safla backup_safla_data '{
  "backup_type": "full",
  "destination": "/backups/final_implementation.tar.gz",
  "compress": true
}'
```

### 2. Workflow Coordination and Handoff

#### Mode Transition Preparation
```bash
# Prepare for next workflow phase
python -m safla.workflow --prepare-handoff \
  --next-mode critic \
  --artifacts implementation_report.json \
  --export handoff_package.json

# Clean up agent sessions
use_mcp_tool safla terminate_agent_session '{
  "session_id": "impl_agent_001",
  "force": false
}'

# Update system awareness for transition
use_mcp_tool safla update_awareness_state '{
  "awareness_level": 0.9,
  "focus_areas": ["implementation_complete", "ready_for_review"],
  "introspection_depth": "moderate"
}'
```

#### Task Spawning and Completion
```bash
# Spawn next task in workflow
new_task: critic  # For code review and analysis
# OR
new_task: tdd     # For additional testing
# OR
new_task: deployment  # For deployment preparation

# Complete current task with comprehensive results
attempt_completion: "Code implementation completed successfully with 95% test coverage, comprehensive documentation, and optimized performance. All quality gates passed and system is ready for review phase."
```

This workflow ensures comprehensive, high-quality code development through systematic use of SAFLA's MCP tools and CLI commands, emphasizing test-driven development, continuous optimization, and adaptive learning throughout the development process.