---
name: reasoningbank-intelligence-b62b18984d12
description: "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."
---

# ReasoningBank Intelligence

## What This Skill Does

Implements 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.

## Prerequisites

- agentic-flow v1.5.11+
- AgentDB v1.0.4+ (for persistence)
- Node.js 18+

## Quick Start

```typescript
import { ReasoningBank } from 'agentic-flow/reasoningbank';

// Initialize ReasoningBank
const rb = new ReasoningBank({
  persist: true,
  learningRate: 0.1,
  adapter: 'agentdb' // Use AgentDB for storage
});

// Record task outcome
await rb.recordExperience({
  task: 'code_review',
  approach: 'static_analysis_first',
  outcome: {
    success: true,
    metrics: {
      bugs_found: 5,
      time_taken: 120,
      false_positives: 1
    }
  },
  context: {
    language: 'typescript',
    complexity: 'medium'
  }
});

// Get optimal strategy
const strategy = await rb.recommendStrategy('code_review', {
  language: 'typescript',
  complexity: 'high'
});
```

## Core Features

### 1. Pattern Recognition
```typescript
// Learn patterns from data
await rb.learnPattern({
  pattern: 'api_errors_increase_after_deploy',
  triggers: ['deployment', 'traffic_spike'],
  actions: ['rollback', 'scale_up'],
  confidence: 0.85
});

// Match patterns
const matches = await rb.matchPatterns(currentSituation);
```

### 2. Strategy Optimization
```typescript
// Compare strategies
const comparison = await rb.compareStrategies('bug_fixing', [
  'tdd_approach',
  'debug_first',
  'reproduce_then_fix'
]);

// Get best strategy
const best = comparison.strategies[0];
console.log(`Best: ${best.name} (score: ${best.score})`);
```

### 3. Continuous Learning
```typescript
// Enable auto-learning from all tasks
await rb.enableAutoLearning({
  threshold: 0.7,        // Only learn from high-confidence outcomes
  updateFrequency: 100   // Update models every 100 experiences
});
```

## Advanced Usage

### Meta-Learning
```typescript
// Learn about learning
await rb.metaLearn({
  observation: 'parallel_execution_faster_for_independent_tasks',
  confidence: 0.95,
  applicability: {
    task_types: ['batch_processing', 'data_transformation'],
    conditions: ['tasks_independent', 'io_bound']
  }
});
```

### Transfer Learning
```typescript
// Apply knowledge from one domain to another
await rb.transferKnowledge({
  from: 'code_review_javascript',
  to: 'code_review_typescript',
  similarity: 0.8
});
```

### Adaptive Agents
```typescript
// Create self-improving agent
class AdaptiveAgent {
  async execute(task: Task) {
    // Get optimal strategy
    const strategy = await rb.recommendStrategy(task.type, task.context);

    // Execute with strategy
    const result = await this.executeWithStrategy(task, strategy);

    // Learn from outcome
    await rb.recordExperience({
      task: task.type,
      approach: strategy.name,
      outcome: result,
      context: task.context
    });

    return result;
  }
}
```

## Integration with AgentDB

```typescript
// Persist ReasoningBank data
await rb.configure({
  storage: {
    type: 'agentdb',
    options: {
      database: './reasoning-bank.db',
      enableVectorSearch: true
    }
  }
});

// Query learned patterns
const patterns = await rb.query({
  category: 'optimization',
  minConfidence: 0.8,
  timeRange: { last: '30d' }
});
```

## Performance Metrics

```typescript
// Track learning effectiveness
const metrics = await rb.getMetrics();
console.log(`
  Total Experiences: ${metrics.totalExperiences}
  Patterns Learned: ${metrics.patternsLearned}
  Strategy Success Rate: ${metrics.strategySuccessRate}
  Improvement Over Time: ${metrics.improvement}
`);
```

## Best Practices

1. **Record consistently**: Log all task outcomes, not just successes
2. **Provide context**: Rich context improves pattern matching
3. **Set thresholds**: Filter low-confidence learnings
4. **Review periodically**: Audit learned patterns for quality
5. **Use vector search**: Enable semantic pattern matching

## Troubleshooting

### Issue: Poor recommendations
**Solution**: Ensure sufficient training data (100+ experiences per task type)

### Issue: Slow pattern matching
**Solution**: Enable vector indexing in AgentDB

### Issue: Memory growing large
**Solution**: Set TTL for old experiences or enable pruning

## Learn More

- ReasoningBank Guide: agentic-flow/src/reasoningbank/README.md
- AgentDB Integration: packages/agentdb/docs/reasoningbank.md
- Pattern Learning: docs/reasoning/patterns.md


---
Source attribution: Activer007
Original project: https://github.com/Activer007/ordinary-claude-skills/blob/e3e1b3d55f9a132a68d242040d23048a7ba8f85c/skills_all/reasoningbank-intelligence/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2025 Microck

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

i did not write most of these. i just collected them.

*   **anthropic skills:** mit license (mostly)
*   **community skills:** check the `LICENSE` file in each specific folder.

credits go to https://github.com/anthropics, https://github.com/obra, https://github.com/ComposioHQ, and the other people listed in the source tables. if you own one of these and want me to take it down, just open an issue and i will nuke it.



# Mega AI Hub resource metadata

Creator / publisher: Activer007
Original project: https://github.com/Activer007/ordinary-claude-skills/blob/e3e1b3d55f9a132a68d242040d23048a7ba8f85c/skills_all/reasoningbank-intelligence/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering, Product Management, Sales
Roles: Business Development, Data Scientist, Growth Product Manager, ML / AI Engineer, Product Manager
Specialties: API, Backend
Tools: Agentdb, Agentic Flow
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: f67d6b5ccfa624c867345a053e9f32a6ecd4bd1649ead8de0070836c555548e3