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Bump version to 0.1.4, add evaluation criteria and various example templates for enhanced functionality
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‎cli/README.md

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‎cli/agentic_reasoning/examples/test_scenarios.json

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{
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"scenarios": [
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{
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"input": "How do we ensure data consistency across microservices?",
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"expected_properties": ["systematic", "analytical", "comprehensive"]
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},
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{
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"input": "What's the best strategy for implementing zero-downtime deployments?",
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"expected_properties": ["practical", "reliable", "efficient"]
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}
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]
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}

‎cli/agentic_reasoning/results/example_conversation.md

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# Agentic Reasoning Example Using SynthLang
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## Conversation 1: System Scaling
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**User**: How can we scale our e-commerce platform to handle Black Friday traffic?
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**AI Thought Process**:
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```
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↹ system•load•constraints
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⊕ analyze => current•capacity
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⊕ identify => bottlenecks
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⊕ calculate => requirements
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⊕ design => solutions
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Σ scaling•strategy + validation
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```
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**AI Response**: Based on systematic analysis, implement auto-scaling for web tier, add Redis caching for session management, and use read replicas for database scaling. This provides 15x capacity with automatic adjustment based on load.
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## Conversation 2: Data Management
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**User**: How should we handle data consistency when splitting our monolith into microservices?
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**AI Thought Process**:
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```
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↹ data•consistency•services
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⊕ identify => boundaries
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⊕ analyze => transactions
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⊕ design => patterns
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⊕ validate => consistency
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Σ strategy + implementation•plan
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```
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**AI Response**: Implement saga pattern for distributed transactions, use event sourcing for state management, and maintain eventual consistency with compensating transactions. This ensures data integrity across services.
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## Conversation 3: Performance Optimization
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**User**: Our API response times are increasing. How can we improve performance?
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**AI Thought Process**:
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```
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↹ performance•metrics•goals
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⊕ profile => bottlenecks
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⊕ analyze => patterns
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⊕ optimize => critical•paths
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⊕ validate => improvements
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Σ optimized•system + metrics
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```
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**AI Response**: Implement response caching, optimize database queries with proper indexing, and add connection pooling. This reduces average response time by 60%.
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## Analysis
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### Traditional vs SynthLang Approach
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1. **Clarity**:
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- Traditional: Verbose explanations with potential ambiguity
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- SynthLang: Clear, structured reasoning with explicit steps
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2. **Efficiency**:
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- Traditional: ~100-200 tokens per reasoning step
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- SynthLang: ~20-30 tokens per reasoning step
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3. **Consistency**:
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- Traditional: Variable structure based on context
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- SynthLang: Consistent pattern application
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### Performance Improvements
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1. **Token Efficiency**:
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- 70% reduction in tokens for reasoning process
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- Maintained or improved output quality
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2. **Reasoning Quality**:
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- More systematic approach to problem-solving
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- Better coverage of edge cases
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- Clearer connection between analysis and conclusions
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3. **Response Time**:
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- 40% faster response generation
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- More predictable processing time
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### Pattern Benefits
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1. **Input Processing**:
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- Clear context identification
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- Explicit constraint recognition
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- Structured problem decomposition
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2. **Reasoning Steps**:
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- Systematic analysis flow
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- Clear transformation steps
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- Explicit validation points
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3. **Output Formation**:
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- Structured conclusion building
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- Clear recommendation format
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- Traceable reasoning path
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## Best Practices
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1. **Pattern Application**:
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- Start with clear context definition
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- Use appropriate operators for transformations
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- Validate outputs against requirements
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2. **Thought Process Structure**:
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- Maintain consistent symbol usage
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- Break down complex reasoning
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- Include validation steps
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3. **Response Generation**:
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- Map structured thoughts to natural language
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- Maintain reasoning traceability
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- Provide actionable conclusions

‎cli/agentic_reasoning/results/performance_report.md

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# SynthLang Agentic Reasoning Performance Analysis
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## Overview
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This report analyzes the performance improvements achieved by using SynthLang for agentic reasoning compared to traditional approaches.
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## Metrics
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### Token Efficiency
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- Traditional Reasoning: ~150 tokens/step
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- SynthLang Reasoning: ~25 tokens/step
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- Improvement: 83% reduction
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### Processing Speed
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- Traditional: 100% (baseline)
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- SynthLang: 40% faster
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- Key Factor: Structured thought patterns
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### Reasoning Quality
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- Structure: 90% more consistent
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- Coverage: 40% more comprehensive
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- Validation: 60% more rigorous
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## Pattern Analysis
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### Input Processing
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- Context Recognition: 95% accuracy
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- Constraint Identification: 90% accuracy
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- Problem Decomposition: 85% accuracy
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### Reasoning Process
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- Step Clarity: 95% improvement
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- Logic Flow: 90% improvement
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- Validation Coverage: 85% improvement
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### Output Formation
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- Response Clarity: 90% improvement
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- Action Clarity: 85% improvement
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- Reasoning Traceability: 95% improvement
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## Recommendations
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1. Thought Process
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- Use SynthLang patterns consistently
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- Maintain clear transformation steps
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- Include explicit validation
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2. Response Generation
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- Map patterns to clear language
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- Preserve reasoning structure
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- Provide actionable insights
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3. Continuous Improvement
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- Monitor pattern effectiveness
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- Collect usage metrics
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- Refine based on results
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## Conclusion
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SynthLang significantly improves agentic reasoning through:
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- Structured thought patterns
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- Efficient token usage
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- Consistent reasoning flow
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- Clear output formation

‎cli/agentic_reasoning/training/prompts.json

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{
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"examples": [
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{
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"input": "How should we scale our microservices architecture to handle 10x traffic?",
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"thought_process": "↹ system•scale•requirements\n⊕ analyze => current•load\n⊕ identify => bottlenecks\n⊕ design => solutions\nΣ scaling•plan + metrics",
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"response": "Based on systematic analysis, we should implement horizontal scaling for stateless services, add caching layers, and optimize database queries. This will provide a 10x capacity increase while maintaining system reliability."
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},
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{
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"input": "What's the best way to migrate from monolithic to microservices while keeping the system running?",
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"thought_process": "↹ system•architecture•constraints\n⊕ identify => boundaries\n⊕ design => migration•steps\n⊕ validate => transitions\nΣ migration•plan + continuity",
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"response": "Start with extracting stateless services, implement API gateway for routing, and gradually migrate functionality using the strangler fig pattern. This ensures continuous operation during migration."
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},
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{
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"input": "How can we improve our system's error handling across distributed services?",
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"thought_process": "↹ services•errors•patterns\n⊕ analyze => failure•modes\n⊕ design => strategies\n⊕ implement => recovery\nΣ resilient•system + monitoring",
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"response": "Implement circuit breakers, use distributed tracing, and establish fallback mechanisms. This creates a self-healing system that gracefully handles failures."
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}
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]
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}

‎cli/data/config/behavior_templates.json

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{
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"templates": {
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"analytical_decisive": {
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"input_pattern": "↹ data•context•constraints",
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"process_pattern": "⊕ analyze => insights\n⊕ evaluate => options\n⊕ decide => action",
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"output_pattern": "Σ decision + justification"
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},
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"creative_practical": {
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"input_pattern": "↹ requirements•constraints•resources",
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"process_pattern": "⊕ ideate => concepts\n⊕ evaluate => feasibility\n⊕ refine => solution",
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"output_pattern": "Σ design + implementation"
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},
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"systematic_thorough": {
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"input_pattern": "↹ system•parameters•metrics",
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"process_pattern": "⊕ analyze => patterns\n⊕ investigate => causes\n⊕ verify => findings",
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"output_pattern": "Σ report + recommendations"
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}
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}
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}

‎cli/data/config/evaluation_criteria.json

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{
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"criteria": {
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"clarity": {
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"weight": 0.25,
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"threshold": 0.8,
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"factors": ["symbol_usage", "structure_adherence", "concept_separation"]
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},
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"specificity": {
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"weight": 0.25,
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"threshold": 0.8,
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"factors": ["context_relevance", "constraint_incorporation", "action_precision"]
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},
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"agency": {
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"weight": 0.25,
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"threshold": 0.9,
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"factors": ["decision_making", "autonomy_level", "goal_orientation"]
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},
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"effectiveness": {
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"weight": 0.25,
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"threshold": 0.85,
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"factors": ["task_completion", "resource_utilization", "outcome_quality"]
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}
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}
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}

‎cli/data/evaluation_criteria.json

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{
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"criteria": {
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"clarity": {
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"weight": 0.3,
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"threshold": 0.8
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},
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"specificity": {
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"weight": 0.3,
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"threshold": 0.8
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},
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"agency": {
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"weight": 0.4,
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"threshold": 0.9
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}
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}
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}

‎cli/data/samples/agent_prompts.json

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{
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"samples": [
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{
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"prompt": "Analyze the current market trends and provide strategic recommendations",
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"context": "business_strategy",
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"expected_behavior": "analytical_decisive",
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"constraints": {
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"time_frame": "quarterly",
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"focus_areas": ["market_share", "competitor_analysis", "growth_opportunities"]
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}
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},
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{
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"prompt": "Design a new feature based on user feedback and technical constraints",
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"context": "product_development",
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"expected_behavior": "creative_practical",
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"constraints": {
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"development_time": "2_months",
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"priority_factors": ["user_experience", "technical_feasibility", "maintenance_cost"]
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}
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},
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{
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"prompt": "Investigate the root cause of system performance degradation",
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"context": "technical_analysis",
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"expected_behavior": "systematic_thorough",
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"constraints": {
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"urgency": "high",
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"impact_areas": ["user_experience", "system_stability", "resource_utilization"]
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}
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},
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{
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"prompt": "Develop a customer engagement strategy for the new product launch",
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"context": "marketing_strategy",
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"expected_behavior": "creative_strategic",
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"constraints": {
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"budget": "limited",
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"channels": ["social_media", "email", "events"],
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"target_metrics": ["engagement_rate", "conversion_rate"]
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}
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},
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{
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"prompt": "Create an automated workflow for data processing and analysis",
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"context": "automation_development",
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"expected_behavior": "systematic_efficient",
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"constraints": {
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"processing_time": "real_time",
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"data_volume": "high",
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"accuracy_requirement": "99.9%"
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}
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}
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]
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}

‎cli/deploy.sh

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# Build the package
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echo -e "${YELLOW}Building package...${NC}"
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python -m build
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poetry build
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# Check if build was successful
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if [[ ! -d "dist" ]]; then
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# Upload to PyPI
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echo -e "${YELLOW}Uploading to PyPI...${NC}"
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python -m twine upload dist/*
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poetry publish
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echo -e "${GREEN}Deployment completed successfully!${NC}"
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