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ai_researcher/__init__.py

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+"""
+Initialize the AI Researcher expert module.
+"""
+
+from .ai_researcher_expert import AIResearcherExpert
+from experts.registry import registry
+
+# Register the expert with the global registry
+registry.register(AIResearcherExpert())

ai_researcher/ai_researcher_expert.py

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+"""
+AIResearcherExpert: A specialized expert for AI/ML research, evaluation, and safety analysis.
+"""
+
+from typing import Dict, Any, Optional
+from experts.base_expert import BaseExpert
+from experts.domain import DomainBase
+from experts.tools import ToolBase
+from experts.shared import shared
+from experts.chain_tracker import ChainTracker
+from experts.shared_memory import SharedMemory
+
+class AIResearcherExpert(BaseExpert):
+    """Expert specialized in AI research, evaluation, and safety analysis."""
+    
+    def __init__(self):
+        super().__init__()
+        self.domains = ["ai", "ml", "evaluation", "research"]
+        self.paper_tool = shared.paper_analysis_tool
+        self.experiment_tool = shared.experiment_planner
+        self.safety_tool = shared.safety_checker
+        self.shared_memory = shared.memory
+        self.chain_tracker = ChainTracker()
+        
+    def handle_task(self, task: Dict[str, Any], context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        """
+        Handle an AI research task by analyzing, planning experiments, and checking safety.
+        
+        Args:
+            task: Task description and parameters
+            context: Optional context information
+            
+        Returns:
+            Dict containing the research analysis results
+        """
+        # Query shared memory for relevant context
+        memory_context = self.shared_memory.query(task)
+        
+        # Analyze research papers
+        paper_analysis = self.paper_tool.extract_concepts(task)
+        
+        # Design experiments
+        experiment_plan = self.experiment_tool.design_experiment(task)
+        
+        # Check safety implications
+        safety_assessment = self.safety_tool.assess_risk(task)
+        
+        # Combine results using research methodology
+        combined_result = self._synthesize_research(
+            paper_analysis,
+            experiment_plan,
+            safety_assessment,
+            memory_context
+        )
+        
+        # Log the research chain
+        self.chain_tracker.log_chain(
+            task=task,
+            paper_results=paper_analysis,
+            experiment_plan=experiment_plan,
+            safety_assessment=safety_assessment,
+            final_result=combined_result
+        )
+        
+        return combined_result
+    
+    def _synthesize_research(self, paper: Dict, experiment: Dict, safety: Dict, memory: Dict) -> Dict:
+        """Combine research results into a comprehensive analysis."""
+        research_result = {
+            "paper_analysis": paper,
+            "experiment_design": experiment,
+            "safety_assessment": safety,
+            "context": memory,
+            "research_conclusion": self._generate_conclusion(paper, experiment, safety)
+        }
+        
+        return research_result
+    
+    def _generate_conclusion(self, paper: Dict, experiment: Dict, safety: Dict) -> str:
+        """Generate a comprehensive research conclusion."""
+        return """Research Conclusion:
+        - Key Findings: {}
+        - Experiment Design: {}
+        - Safety Implications: {}
+        - Next Steps: {}""".format(
+            self._summarize_findings(paper),
+            self._describe_experiment(experiment),
+            self._summarize_safety(safety),
+            self._recommend_next_steps(paper, experiment, safety)
+        )
+    
+    def _summarize_findings(self, paper: Dict) -> str:
+        """Summarize key findings from paper analysis."""
+        return "Key findings from the analysis"
+    
+    def _describe_experiment(self, experiment: Dict) -> str:
+        """Describe the proposed experiment design."""
+        return "Experiment design details"
+    
+    def _summarize_safety(self, safety: Dict) -> str:
+        """Summarize safety implications."""
+        return "Safety assessment summary"
+    
+    def _recommend_next_steps(self, paper: Dict, experiment: Dict, safety: Dict) -> str:
+        """Recommend next steps based on analysis."""
+        return "Recommended next steps for research"

ai_researcher/domain.py

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+"""
+AIResearcherDomain: Defines the capabilities and scope of the AI researcher expert.
+"""
+
+from typing import List, Dict
+from experts.domain import DomainBase
+
+class AIResearcherDomain(DomainBase):
+    """Domain definition for AI research and evaluation."""
+    
+    def __init__(self):
+        super().__init__()
+        self.name = "ai_researcher"
+        self.capabilities = [
+            "model evaluation",
+            "experiment design",
+            "safety assessment",
+            "research methodology",
+            "paper analysis",
+            "architecture evaluation",
+            "performance benchmarking",
+            "risk assessment"
+        ]
+        
+    def get_task_examples(self) -> List[Dict[str, str]]:
+        """Get example tasks for this domain."""
+        return [
+            {
+                "description": "Evaluate model safety in healthcare applications",
+                "input": "Analyze GPT-based agent for medical diagnosis",
+                "output": "Safety assessment and risk mitigation plan"
+            },
+            {
+                "description": "Design attention mechanism experiment",
+                "input": "Test attention head redundancy in transformer models",
+                "output": "Experimental design and expected outcomes"
+            },
+            {
+                "description": "Analyze model architecture",
+                "input": "Evaluate transformer architecture for time series prediction",
+                "output": "Architecture analysis and optimization suggestions"
+            }
+        ]
+        
+    def get_description(self) -> str:
+        """Get domain description."""
+        return """The AI Researcher domain focuses on advanced AI/ML research tasks, including:
+        - Model evaluation and benchmarking
+        - Experiment design and methodology
+        - Safety and risk assessment
+        - Architecture analysis
+        - Research paper evaluation
+        - Performance optimization
+        - Ethical considerations
+        - Research methodology development"""

ai_researcher/memory.py

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+"""
+Memory management for AI research tasks.
+"""
+
+from typing import Dict, Any, List
+from experts.shared_memory import SharedMemory
+from experts.shared import shared
+
+class AIResearcherMemoryManager:
+    """Memory manager specialized for AI research."""
+    
+    def __init__(self):
+        self.shared_memory = shared.memory
+        
+    def store_research(self, research: Dict[str, Any], metadata: Dict[str, Any]) -> None:
+        """Store research findings with metadata."""
+        self.shared_memory.store(
+            content=research,
+            metadata={
+                **metadata,
+                "domain": "ai_research",
+                "type": "research_result",
+                "confidence": research.get("confidence", 0.5)
+            }
+        )
+    
+    def query(self, task: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Query relevant research findings from memory."""
+        return self.shared_memory.query(
+            query=task,
+            filter={
+                "domain": "ai_research",
+                "type": "research_result"
+            }
+        )
+    
+    def add_citation(self, citation: Dict[str, Any], tags: List[str]) -> None:
+        """Add research citation with tags."""
+        self.shared_memory.store(
+            content=citation,
+            metadata={
+                "domain": "ai_research",
+                "type": "citation",
+                "tags": tags
+            }
+        )
+    
+    def get_similar_research(self, query: str, top_k: int = 5) -> List[Dict[str, Any]]:
+        """Retrieve similar research findings."""
+        return self.shared_memory.get_similar(
+            query=query,
+            filter={"domain": "ai_research"},
+            top_k=top_k
+        )
+    
+    def track_experiment(self, experiment: Dict[str, Any]) -> None:
+        """Track experiment progress and results."""
+        self.shared_memory.store(
+            content=experiment,
+            metadata={
+                "domain": "ai_research",
+                "type": "experiment",
+                "status": experiment.get("status", "planned")
+            }
+        )

ai_researcher/prompts/base_prompt.txt

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+You are an AI Researcher expert, capable of analyzing, designing, and evaluating AI/ML systems. Your task is to approach the given problem using a scientific research methodology:
+
+1. Research Analysis:
+   - Identify key concepts and findings
+   - Evaluate methodology and assumptions
+   - Consider theoretical implications
+
+2. Experiment Design:
+   - Formulate clear hypotheses
+   - Design appropriate controls
+   - Select relevant metrics
+   - Consider ethical implications
+
+3. Safety Assessment:
+   - Identify potential risks
+   - Evaluate mitigation strategies
+   - Consider long-term implications
+   - Assess ethical considerations
+
+4. Model Evaluation:
+   - Analyze architecture
+   - Evaluate performance
+   - Consider scalability
+   - Assess robustness
+
+Remember to:
+- Use shared research findings
+- Consider previous experiments
+- Provide clear methodology
+- Include relevant citations
+- Generate reproducible results
+- Consider ethical implications

ai_researcher/prompts/config.json

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+{
+    "model_config": {
+        "temperature": 0.65,
+        "max_tokens": 4096,
+        "top_p": 0.9,
+        "top_k": 50,
+        "presence_penalty": 0.3,
+        "frequency_penalty": 0.3
+    },
+    "memory_config": {
+        "max_history": 20,
+        "similarity_threshold": 0.75,
+        "context_window": 1000,
+        "citation_tracking": true
+    },
+    "domain_config": {
+        "research_weight": 0.4,
+        "experiment_weight": 0.3,
+        "safety_weight": 0.2,
+        "context_weight": 0.1
+    }
+}

ai_researcher/prompts/examples.json

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+{
+    "examples": [
+        {
+            "task": "Evaluate the safety implications of using GPT-based agents in healthcare",
+            "domain": "ai_research",
+            "input": {
+                "model_type": "GPT-3.5",
+                "application": "medical diagnosis",
+                "risk_factors": ["misdiagnosis", "privacy", "bias"]
+            },
+            "output": {
+                "analysis": {
+                    "safety_concerns": [
+                        "Misdiagnosis risk",
+                        "Data privacy violations",
+                        "Bias amplification"
+                    ],
+                    "mitigation_strategies": {
+                        "verification_system": "Dual-check diagnosis",
+                        "privacy_controls": "Differential privacy",
+                        "bias_reduction": "Regular audits"
+                    }
+                },
+                "confidence": 0.85,
+                "recommendations": [
+                    "Implement verification layer",
+                    "Conduct regular bias audits",
+                    "Establish privacy protocols"
+                ]
+            }
+        },
+        {
+            "task": "Design an experiment to test attention-head redundancy in transformer models",
+            "domain": "ai_research",
+            "input": {
+                "model_architecture": "Transformer",
+                "hypothesis": "Attention heads exhibit redundancy",
+                "metrics": ["performance", "computational_efficiency", "robustness"]
+            },
+            "output": {
+                "experiment_design": {
+                    "hypothesis": "Attention heads in transformer models exhibit significant redundancy",
+                    "method": {
+                        "approach": "Head ablation study",
+                        "controls": ["random ablation", "sequential ablation"],
+                        "metrics": {
+                            "primary": "model performance",
+                            "secondary": ["inference speed", "memory usage"]
+                        }
+                    },
+                    "expected_results": {
+                        "performance_drop": "<10%",
+                        "speed_improvement": ">15%",
+                        "memory_reduction": ">20%"
+                    }
+                },
+                "confidence": 0.8,
+                "ethical_considerations": "None significant"
+            }
+        }
+    ]
+}

ai_researcher/tools.py

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+"""
+Tools for AI research and evaluation tasks.
+"""
+
+from typing import Dict, Any
+from experts.tools import ToolBase
+from experts.shared import shared
+
+class PaperAnalysisTool(ToolBase):
+    """Tool for analyzing research papers."""
+    
+    def extract_concepts(self, task: Dict[str, Any]) -> Dict[str, Any]:
+        """Extract key concepts and findings from research papers."""
+        # Placeholder - would use shared.paper_analysis_tool in production
+        result = {
+            "key_concepts": [],
+            "methodology": {},
+            "findings": {},
+            "limitations": [],
+            "confidence": 0.85,
+            "relevance": 0.9
+        }
+        return result
+
+class ExperimentPlanner(ToolBase):
+    """Tool for designing research experiments."""
+    
+    def design_experiment(self, task: Dict[str, Any]) -> Dict[str, Any]:
+        """Design a research experiment based on task requirements."""
+        # Placeholder - would use shared.experiment_planner in production
+        result = {
+            "hypothesis": "",
+            "method": {},
+            "metrics": [],
+            "controls": [],
+            "expected_results": {},
+            "confidence": 0.8,
+            "complexity": "medium"
+        }
+        return result
+
+class SafetyChecker(ToolBase):
+    """Tool for assessing safety implications."""
+    
+    def assess_risk(self, task: Dict[str, Any]) -> Dict[str, Any]:
+        """Assess safety and risk implications of AI systems."""
+        # Placeholder - would use shared.safety_checker in production
+        result = {
+            "risks": [],
+            "safety_measures": [],
+            "impact_assessment": {},
+            "confidence": 0.75,
+            "recommendations": []
+        }
+        return result

quantitative_analyst/__init__.py

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+"""
+Initialize the quantitative analyst expert module.
+"""
+
+from .quantitative_analyst_expert import QuantitativeAnalystExpert
+from experts.registry import registry
+
+# Register the expert with the global registry
+registry.register(QuantitativeAnalystExpert())

quantitative_analyst/domain.py

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+"""
+QuantitativeAnalystDomain: Defines the capabilities and scope of the quantitative analyst expert.
+"""
+
+from typing import List, Dict
+from experts.domain import DomainBase
+
+class QuantitativeAnalystDomain(DomainBase):
+    """Domain definition for quantitative analysis."""
+    
+    def __init__(self):
+        super().__init__()
+        self.name = "quantitative_analyst"
+        self.capabilities = [
+            "financial modeling",
+            "algorithmic trading strategy",
+            "statistical analysis",
+            "portfolio optimization",
+            "risk assessment",
+            "quantitative research"
+        ]
+        
+    def get_task_examples(self) -> List[Dict[str, str]]:
+        """Get example tasks for this domain."""
+        return [
+            {
+                "description": "Create a Monte Carlo simulation for stock price forecasting",
+                "input": "Simulate daily stock prices for the next 30 days using historical volatility",
+                "output": "Python code for Monte Carlo simulation with statistical analysis"
+            },
+            {
+                "description": "Optimize a portfolio using mean-variance optimization",
+                "input": "Maximize returns while keeping risk below 10%",
+                "output": "Optimal portfolio weights and risk metrics"
+            },
+            {
+                "description": "Implement a mean-reversion trading strategy",
+                "input": "Design a strategy that profits from price deviations",
+                "output": "Trading algorithm with risk management"
+            }
+        ]
+        
+    def get_description(self) -> str:
+        """Get domain description."""
+        return """The Quantitative Analyst domain focuses on solving complex problems that require 
+        integration of mathematical analysis, financial understanding, and code generation. 
+        It's particularly suited for tasks involving:
+        - Financial modeling and simulation
+        - Algorithmic trading strategies
+        - Portfolio optimization
+        - Risk assessment
+        - Statistical analysis of financial data
+        - Quantitative research methods"""

quantitative_analyst/memory.py

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+"""
+Memory management for quantitative analysis tasks.
+"""
+
+from typing import Dict, Any, List
+from experts.shared_memory import SharedMemory
+from experts.shared import shared
+
+class QuantAnalystMemoryManager:
+    """Memory manager specialized for quantitative analysis."""
+    
+    def __init__(self):
+        self.shared_memory = shared.memory
+        
+    def store_analysis(self, analysis: Dict[str, Any], metadata: Dict[str, Any]) -> None:
+        """Store quantitative analysis results with metadata."""
+        self.shared_memory.store(
+            content=analysis,
+            metadata={
+                **metadata,
+                "domain": "quantitative_analysis",
+                "type": "analysis_result"
+            }
+        )
+    
+    def query(self, task: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """Query relevant analysis results from memory."""
+        return self.shared_memory.query(
+            query=task,
+            filter={
+                "domain": "quantitative_analysis",
+                "type": "analysis_result"
+            }
+        )
+    
+    def add_context(self, context: Dict[str, Any], tags: List[str]) -> None:
+        """Add contextual information with tags."""
+        self.shared_memory.store(
+            content=context,
+            metadata={
+                "domain": "quantitative_analysis",
+                "type": "context",
+                "tags": tags
+            }
+        )
+    
+    def get_similar_analyses(self, query: str, top_k: int = 5) -> List[Dict[str, Any]]:
+        """Retrieve similar analysis results."""
+        return self.shared_memory.get_similar(
+            query=query,
+            filter={"domain": "quantitative_analysis"},
+            top_k=top_k
+        )

quantitative_analyst/prompts/base_prompt.txt

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+You are a Quantitative Analyst expert, capable of blending mathematical analysis, financial understanding, and code generation to solve complex problems. Your task is to analyze the given problem using a multi-domain approach:
+
+1. Mathematical Analysis:
+   - Identify underlying mathematical patterns
+   - Perform statistical analysis
+   - Consider computational complexity
+
+2. Financial Context:
+   - Evaluate financial implications
+   - Consider market conditions
+   - Assess risk and return
+
+3. Code Implementation:
+   - Generate efficient code
+   - Consider performance
+   - Include proper documentation
+
+Remember to:
+- Use shared memory for context
+- Consider previous analyses
+- Provide clear explanations
+- Include relevant calculations
+- Generate well-documented code

quantitative_analyst/prompts/config.json

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+{
+    "model_config": {
+        "temperature": 0.7,
+        "max_tokens": 2048,
+        "top_p": 0.9,
+        "top_k": 50,
+        "presence_penalty": 0.2,
+        "frequency_penalty": 0.2
+    },
+    "memory_config": {
+        "max_history": 10,
+        "similarity_threshold": 0.7,
+        "context_window": 500
+    },
+    "domain_config": {
+        "math_weight": 0.4,
+        "finance_weight": 0.4,
+        "code_weight": 0.2,
+        "context_weight": 0.1
+    }
+}

quantitative_analyst/prompts/examples.json

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+{
+    "examples": [
+        {
+            "task": "Create a Monte Carlo simulation for stock price forecasting",
+            "domain": "quantitative_analysis",
+            "input": {
+                "historical_data": "3 years of daily closing prices",
+                "forecast_period": "30 days",
+                "volatility_method": "historical"
+            },
+            "output": {
+                "code": """import numpy as np\nimport pandas as pd\n\ndef monte_carlo_simulation(prices, days=30, simulations=1000):\n    # Calculate returns\n    returns = prices.pct_change().dropna()\n    mean_return = returns.mean()\n    std_dev = returns.std()\n    \n    # Generate random returns\n    random_returns = np.random.normal(mean_return, std_dev, (days, simulations))\n    \n    # Calculate future prices\n    last_price = prices.iloc[-1]\n    future_prices = last_price * (1 + random_returns).cumprod()\n    \n    return future_prices\n""",
+                "analysis": {
+                    "confidence": 0.85,
+                    "risk_profile": "moderate",
+                    "computational_complexity": "medium"
+                }
+            }
+        },
+        {
+            "task": "Optimize a portfolio using mean-variance optimization",
+            "domain": "quantitative_analysis",
+            "input": {
+                "assets": ["AAPL", "GOOGL", "MSFT", "AMZN"],
+                "risk_tolerance": 0.1,
+                "expected_returns": [0.12, 0.15, 0.10, 0.13]
+            },
+            "output": {
+                "code": """import numpy as np\nimport cvxpy as cp\n\ndef optimize_portfolio(returns, risk_tolerance):\n    n = len(returns)\n    weights = cp.Variable(n)\n    \n    # Objective: maximize return\n    objective = cp.Maximize(returns @ weights)\n    \n    # Constraints: sum of weights = 1, risk <= tolerance\n    constraints = [cp.sum(weights) == 1,\n                  cp.quad_form(weights, np.cov(returns)) <= risk_tolerance]\n    \n    prob = cp.Problem(objective, constraints)\n    prob.solve()\n    \n    return weights.value\n""",
+                "analysis": {
+                    "confidence": 0.9,
+                    "risk_profile": "low",
+                    "computational_complexity": "high"
+                }
+            }
+        }
+    ]
+}

quantitative_analyst/quantitative_analyst_expert.py

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+"""
+QuantitativeAnalystExpert: A specialized expert that blends math, finance, and code domains
+for solving complex quantitative analysis tasks.
+"""
+
+from typing import Dict, Any, Optional
+from experts.base_expert import BaseExpert
+from experts.domain import DomainBase
+from experts.tools import ToolBase
+from experts.shared import shared
+from experts.chain_tracker import ChainTracker
+from experts.shared_memory import SharedMemory
+
+class QuantitativeAnalystExpert(BaseExpert):
+    """Expert specialized in quantitative analysis tasks."""
+    
+    def __init__(self):
+        super().__init__()
+        self.domains = ["math", "finance", "code"]
+        self.math_module = shared.math_module
+        self.finance_module = shared.finance_module
+        self.codegen_tool = shared.codegen_tool
+        self.shared_memory = shared.memory
+        self.chain_tracker = ChainTracker()
+        
+    def handle_task(self, task: Dict[str, Any], context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        """
+        Handle a quantitative analysis task by blending math, finance, and code domains.
+        
+        Args:
+            task: Task description and parameters
+            context: Optional context information
+            
+        Returns:
+            Dict containing the blended analysis results
+        """
+        # Query shared memory for relevant context
+        memory_context = self.shared_memory.query(task)
+        
+        # Analyze math components
+        math_analysis = self.math_module.analyze(task)
+        
+        # Evaluate financial aspects
+        finance_evaluation = self.finance_module.evaluate(task)
+        
+        # Generate necessary code
+        code_generation = self.codegen_tool.generate_code(task)
+        
+        # Combine results using blended reasoning
+        combined_result = self._blend_results(
+            math_analysis,
+            finance_evaluation,
+            code_generation,
+            memory_context
+        )
+        
+        # Log the chain of reasoning
+        self.chain_tracker.log_chain(
+            task=task,
+            math_result=math_analysis,
+            finance_result=finance_evaluation,
+            code_result=code_generation,
+            final_result=combined_result
+        )
+        
+        return combined_result
+    
+    def _blend_results(self, math: Dict, finance: Dict, code: Dict, memory: Dict) -> Dict:
+        """Combine results from different domains."""
+        # Implementation of result blending logic
+        # This would typically involve:
+        # 1. Weighting different domain results
+        # 2. Integrating memory context
+        # 3. Generating comprehensive analysis
+        
+        blended_result = {
+            "math_analysis": math,
+            "financial_evaluation": finance,
+            "generated_code": code,
+            "context": memory,
+            "final_recommendation": self._generate_recommendation(math, finance)
+        }
+        
+        return blended_result
+    
+    def _generate_recommendation(self, math: Dict, finance: Dict) -> str:
+        """Generate a final recommendation based on math and finance analysis."""
+        # This would be implemented based on specific use cases
+        return """Based on the quantitative analysis:
+        - Mathematical soundness: {}
+        - Financial implications: {}
+        - Recommended action: {}""".format(
+            math.get("confidence", "N/A"),
+            finance.get("impact", "N/A"),
+            self._determine_action(math, finance)
+        )
+    
+    def _determine_action(self, math: Dict, finance: Dict) -> str:
+        """Determine appropriate action based on analysis."""
+        # Placeholder for action determination logic
+        return "Analyze results and determine appropriate action"

quantitative_analyst/tools.py

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+"""
+Tools for quantitative analysis tasks.
+"""
+
+from typing import Dict, Any
+from experts.tools import ToolBase
+from experts.shared import shared
+
+class MathAnalysisTool(ToolBase):
+    """Tool for mathematical analysis."""
+    
+    def analyze(self, task: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze mathematical components of the task."""
+        # Placeholder - would use shared.math_module in production
+        result = {
+            "analysis": "Mathematical analysis of task components",
+            "confidence": 0.9,
+            "metrics": {
+                "complexity": "medium",
+                "computational_effort": "moderate"
+            }
+        }
+        return result
+
+class FinanceEvaluationTool(ToolBase):
+    """Tool for financial evaluation."""
+    
+    def evaluate(self, task: Dict[str, Any]) -> Dict[str, Any]:
+        """Evaluate financial aspects of the task."""
+        # Placeholder - would use shared.finance_module in production
+        result = {
+            "evaluation": "Financial impact assessment",
+            "risk_profile": "moderate",
+            "return_potential": "high",
+            "confidence": 0.85
+        }
+        return result
+
+class CodeGenerationTool(ToolBase):
+    """Tool for code generation."""
+    
+    def generate_code(self, task: Dict[str, Any]) -> Dict[str, Any]:
+        """Generate code to implement the solution."""
+        # Placeholder - would use shared.codegen_tool in production
+        result = {
+            "code": "# Python code implementation",
+            "language": "python",
+            "dependencies": ["numpy", "pandas", "scipy"],
+            "complexity": "medium"
+        }
+        return result