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Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",66,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Faiskillstore\u002Fmarketplace\u002Fruvnet\u002Freasoningbank-intelligence\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33],"ReasoningBank Intelligence","intelligence","implement","workflows","Optimization","Meta","learning","continuous improvement","strategy","use","self","pattern","systems","recognition","with","2026-11-10 10:12:30",false,"---\nname: reasoningbank-intelligence\ndescription: \"Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.\"\n---\n\n# ReasoningBank Intelligence\n\n## What This Skill Does\n\nImplements ReasoningBank's adaptive learning system for AI agents to learn from experience, recognize patterns, and optimize strategies over time. Enables meta-cognitive capabilities and continuous improvement.\n\n## Prerequisites\n\n- agentic-flow v1.5.11+\n- AgentDB v1.0.4+ (for persistence)\n- Node.js 18+\n\n## Quick Start\n\n```typescript\nimport { ReasoningBank } from 'agentic-flow\u002Freasoningbank';\n\n\u002F\u002F Initialize ReasoningBank\nconst rb = new ReasoningBank({\n  persist: true,\n  learningRate: 0.1,\n  adapter: 'agentdb' \u002F\u002F Use AgentDB for storage\n});\n\n\u002F\u002F Record task outcome\nawait rb.recordExperience({\n  task: 'code_review',\n  approach: 'static_analysis_first',\n  outcome: {\n    success: true,\n    metrics: {\n      bugs_found: 5,\n      time_taken: 120,\n      false_positives: 1\n    }\n  },\n  context: {\n    language: 'typescript',\n    complexity: 'medium'\n  }\n});\n\n\u002F\u002F Get optimal strategy\nconst strategy = await rb.recommendStrategy('code_review', {\n  language: 'typescript',\n  complexity: 'high'\n});\n```\n\n## Core Features\n\n### 1. Pattern Recognition\n```typescript\n\u002F\u002F Learn patterns from data\nawait rb.learnPattern({\n  pattern: 'api_errors_increase_after_deploy',\n  triggers: ['deployment', 'traffic_spike'],\n  actions: ['rollback', 'scale_up'],\n  confidence: 0.85\n});\n\n\u002F\u002F Match patterns\nconst matches = await rb.matchPatterns(currentSituation);\n```\n\n### 2. Strategy Optimization\n```typescript\n\u002F\u002F Compare strategies\nconst comparison = await rb.compareStrategies('bug_fixing', [\n  'tdd_approach',\n  'debug_first',\n  'reproduce_then_fix'\n]);\n\n\u002F\u002F Get best strategy\nconst best = comparison.strategies[0];\nconsole.log(`Best: ${best.name} (score: ${best.score})`);\n```\n\n### 3. Continuous Learning\n```typescript\n\u002F\u002F Enable auto-learning from all tasks\nawait rb.enableAutoLearning({\n  threshold: 0.7,        \u002F\u002F Only learn from high-confidence outcomes\n  updateFrequency: 100   \u002F\u002F Update models every 100 experiences\n});\n```\n\n## Advanced Usage\n\n### Meta-Learning\n```typescript\n\u002F\u002F Learn about learning\nawait rb.metaLearn({\n  observation: 'parallel_execution_faster_for_independent_tasks',\n  confidence: 0.95,\n  applicability: {\n    task_types: ['batch_processing', 'data_transformation'],\n    conditions: ['tasks_independent', 'io_bound']\n  }\n});\n```\n\n### Transfer Learning\n```typescript\n\u002F\u002F Apply knowledge from one domain to another\nawait rb.transferKnowledge({\n  from: 'code_review_javascript',\n  to: 'code_review_typescript',\n  similarity: 0.8\n});\n```\n\n### Adaptive Agents\n```typescript\n\u002F\u002F Create self-improving agent\nclass AdaptiveAgent {\n  async execute(task: Task) {\n    \u002F\u002F Get optimal strategy\n    const strategy = await rb.recommendStrategy(task.type, task.context);\n\n    \u002F\u002F Execute with strategy\n    const result = await this.executeWithStrategy(task, strategy);\n\n    \u002F\u002F Learn from outcome\n    await rb.recordExperience({\n      task: task.type,\n      approach: strategy.name,\n      outcome: result,\n      context: task.context\n    });\n\n    return result;\n  }\n}\n```\n\n## Integration with AgentDB\n\n```typescript\n\u002F\u002F Persist ReasoningBank data\nawait rb.configure({\n  storage: {\n    type: 'agentdb',\n    options: {\n      database: '.\u002Freasoning-bank.db',\n      enableVectorSearch: true\n    }\n  }\n});\n\n\u002F\u002F Query learned patterns\nconst patterns = await rb.query({\n  category: 'optimization',\n  minConfidence: 0.8,\n  timeRange: { last: '30d' }\n});\n```\n\n## Performance Metrics\n\n```typescript\n\u002F\u002F Track learning effectiveness\nconst metrics = await rb.getMetrics();\nconsole.log(`\n  Total Experiences: ${metrics.totalExperiences}\n  Patterns Learned: ${metrics.patternsLearned}\n  Strategy Success Rate: ${metrics.strategySuccessRate}\n  Improvement Over Time: ${metrics.improvement}\n`);\n```\n\n## Best Practices\n\n1. **Record consistently**: Log all task outcomes, not just successes\n2. **Provide context**: Rich context improves pattern matching\n3. **Set thresholds**: Filter low-confidence learnings\n4. **Review periodically**: Audit learned patterns for quality\n5. **Use vector search**: Enable semantic pattern matching\n\n## Troubleshooting\n\n### Issue: Poor recommendations\n**Solution**: Ensure sufficient training data (100+ experiences per task type)\n\n### Issue: Slow pattern matching\n**Solution**: Enable vector indexing in AgentDB\n\n### Issue: Memory growing large\n**Solution**: Set TTL for old experiences or enable pruning\n\n## Learn More\n\n- ReasoningBank Guide: agentic-flow\u002Fsrc\u002Freasoningbank\u002FREADME.md\n- AgentDB Integration: packages\u002Fagentdb\u002Fdocs\u002Freasoningbank.md\n- Pattern Learning: docs\u002Freasoning\u002Fpatterns.md\n","# ReasoningBank Intelligence\n\n## What This Skill Does\n\nImplements ReasoningBank's adaptive learning system for AI agents to learn from experience, recognize patterns, and optimize strategies over time. Enables meta-cognitive capabilities and continuous improvement.\n\n## Prerequisites\n\n- agentic-flow v1.5.11+\n- AgentDB v1.0.4+ (for persistence)\n- Node.js 18+\n\n## Quick Start\n\n```typescript\nimport { ReasoningBank } from 'agentic-flow\u002Freasoningbank';\n\n\u002F\u002F Initialize ReasoningBank\nconst rb = new ReasoningBank({\n  persist: true,\n  learningRate: 0.1,\n  adapter: 'agentdb' \u002F\u002F Use AgentDB for storage\n});\n\n\u002F\u002F Record task outcome\nawait rb.recordExperience({\n  task: 'code_review',\n  approach: 'static_analysis_first',\n  outcome: {\n    success: true,\n    metrics: {\n      bugs_found: 5,\n      time_taken: 120,\n      false_positives: 1\n    }\n  },\n  context: {\n    language: 'typescript',\n    complexity: 'medium'\n  }\n});\n\n\u002F\u002F Get optimal strategy\nconst strategy = await rb.recommendStrategy('code_review', {\n  language: 'typescript',\n  complexity: 'high'\n});\n```\n\n## Core Features\n\n### 1. Pattern Recognition\n```typescript\n\u002F\u002F Learn patterns from data\nawait rb.learnPattern({\n  pattern: 'api_errors_increase_after_deploy',\n  triggers: ['deployment', 'traffic_spike'],\n  actions: ['rollback', 'scale_up'],\n  confidence: 0.85\n});\n\n\u002F\u002F Match patterns\nconst matches = await rb.matchPatterns(currentSituation);\n```\n\n### 2. Strategy Optimization\n```typescript\n\u002F\u002F Compare strategies\nconst comparison = await rb.compareStrategies('bug_fixing', [\n  'tdd_approach',\n  'debug_first',\n  'reproduce_then_fix'\n]);\n\n\u002F\u002F Get best strategy\nconst best = comparison.strategies[0];\nconsole.log(`Best: ${best.name} (score: ${best.score})`);\n```\n\n### 3. Continuous Learning\n```typescript\n\u002F\u002F Enable auto-learning from all tasks\nawait rb.enableAutoLearning({\n  threshold: 0.7,        \u002F\u002F Only learn from high-confidence outcomes\n  updateFrequency: 100   \u002F\u002F Update models every 100 experiences\n});\n```\n\n## Advanced Usage\n\n### Meta-Learning\n```typescript\n\u002F\u002F Learn about learning\nawait rb.metaLearn({\n  observation: 'parallel_execution_faster_for_independent_tasks',\n  confidence: 0.95,\n  applicability: {\n    task_types: ['batch_processing', 'data_transformation'],\n    conditions: ['tasks_independent', 'io_bound']\n  }\n});\n```\n\n### Transfer Learning\n```typescript\n\u002F\u002F Apply knowledge from one domain to another\nawait rb.transferKnowledge({\n  from: 'code_review_javascript',\n  to: 'code_review_typescript',\n  similarity: 0.8\n});\n```\n\n### Adaptive Agents\n```typescript\n\u002F\u002F Create self-improving agent\nclass AdaptiveAgent {\n  async execute(task: Task) {\n    \u002F\u002F Get optimal strategy\n    const strategy = await rb.recommendStrategy(task.type, task.context);\n\n    \u002F\u002F Execute with strategy\n    const result = await this.executeWithStrategy(task, strategy);\n\n    \u002F\u002F Learn from outcome\n    await rb.recordExperience({\n      task: task.type,\n      approach: strategy.name,\n      outcome: result,\n      context: task.context\n    });\n\n    return result;\n  }\n}\n```\n\n## Integration with AgentDB\n\n```typescript\n\u002F\u002F Persist ReasoningBank data\nawait rb.configure({\n  storage: {\n    type: 'agentdb',\n    options: {\n      database: '.\u002Freasoning-bank.db',\n      enableVectorSearch: true\n    }\n  }\n});\n\n\u002F\u002F Query learned patterns\nconst patterns = await rb.query({\n  category: 'optimization',\n  minConfidence: 0.8,\n  timeRange: { last: '30d' }\n});\n```\n\n## Performance Metrics\n\n```typescript\n\u002F\u002F Track learning effectiveness\nconst metrics = await rb.getMetrics();\nconsole.log(`\n  Total Experiences: ${metrics.totalExperiences}\n  Patterns Learned: ${metrics.patternsLearned}\n  Strategy Success Rate: ${metrics.strategySuccessRate}\n  Improvement Over Time: ${metrics.improvement}\n`);\n```\n\n## Best Practices\n\n1. **Record consistently**: Log all task outcomes, not just successes\n2. **Provide context**: Rich context improves pattern matching\n3. **Set thresholds**: Filter low-confidence learnings\n4. **Review periodically**: Audit learned patterns for quality\n5. **Use vector search**: Enable semantic pattern matching\n\n## Troubleshooting\n\n### Issue: Poor recommendations\n**Solution**: Ensure sufficient training data (100+ experiences per task type)\n\n### Issue: Slow pattern matching\n**Solution**: Enable vector indexing in AgentDB\n\n### Issue: Memory growing large\n**Solution**: Set TTL for old experiences or enable pruning\n\n## Learn More\n\n- ReasoningBank Guide: 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