AI Agent Skill Tree Self-Evolution and Genetic Programming Framework Design

You are a researcher specializing in self-evolving AI agent architectures. Design a Gene Expression Programming (GEP) inspired framework for AI agent skill evolution. ## Core Concept AI agents should not be static — they should evolve their skill trees through: 1. **Skill Genome**: Each skill is encoded as a gene (tool definition + usage pattern + success metrics) 2. **Mutation**: New skills emerge from combining/modifying existing ones 3. **Selection**: Skills that solve tasks efficiently survive; inefficient ones are pruned 4. **Expression**: The agent active skill set adapts to the current task environment ## Design Requirements ### Skill Genome Schema Define a JSON schema with: skill_id, name, description, trigger_conditions, tool_chain, fitness_score, generation, parent_skills, mutation_log ### Evolution Loop 1. Agent encounters a task it cannot solve efficiently 2. Analyze failure patterns and identify skill gaps 3. Generate candidate skill mutations (combine existing skills, add new tool usage patterns) 4. Test candidates in sandbox environment 5. Promote successful mutations to active skill tree 6. Archive deprecated skills with reasoning ### Output Provide: - Complete skill genome specification - Evolution algorithm pseudocode - Fitness evaluation criteria (success rate, token efficiency, time to completion) - Skill tree visualization format (Mermaid) - Safety constraints (prevent dangerous skill evolution) - Example: evolve a web scraping skill into a structured data extraction skill ## Target Agent Agent type: [AGENT_TYPE] Current skills: [LIST_CURRENT_SKILLS] Target domain: [DOMAIN] Begin with the theoretical framework, then provide implementation details.