# Build Adaptive ReasoningBank Learning

Agents often repeat weak strategies because outcomes are not captured for later use. This skill shows how to record experiences, learn patterns, and recommend better strategies with ReasoningBank.

## Install

```bash
npx skillstore add dnyoussef/reasoningbank-intelligence
```

## Metadata

- Status: approved
- Slug: dnyoussef-reasoningbank-intelligence
- Skillstore revision: r1
- Version status: missing
- Tree hash: 5b17b6297c4d3a4164652280f737c4b8fdd5df439e3701b0efb86c074e1a2038
- Author: DNYoussef
- GitHub username: DNYoussef
- License: MIT
- Repository: https://github.com/DNYoussef/ai-chrome-extension/tree/main/.claude/skills/reasoningbank-intelligence
- Ref: 72d5025b022c77f7a51bdf5c1637c689c80e89d1
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: allowed
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands
- Quality score: 78
- Quality tier: bronze
- Public page: https://skillstore.pages.dev/skills/dnyoussef-reasoningbank-intelligence
- Manifest: https://skillstore.pages.dev/api/skills/dnyoussef-reasoningbank-intelligence/manifest

## Capabilities

- Initialize ReasoningBank with persistent AgentDB storage.
- Record task outcomes with metrics and contextual labels.
- Teach agents to learn and match reusable patterns.
- Compare strategies and select the strongest option for a task type.
- Apply meta-learning and transfer learning between related domains.
- Track learning effectiveness through ReasoningBank metrics.

## Use Cases

- Improve Code Review Agents: Record review outcomes and recommend strategies based on language, complexity, and prior success metrics.
- Optimize Workflow Automation: Compare task strategies and reuse learned patterns when workflows repeat across projects.
- Prototype Self-Learning Assistants: Build agents that store experiences, learn transfer rules, and measure improvement over time.

## Prompt Templates

### Start ReasoningBank

```
Use this skill to initialize ReasoningBank with AgentDB persistence for my Node.js agent project.
```

### Record Experiences

```
Show me how to record task outcomes, metrics, and context in ReasoningBank for code review tasks.
```

### Compare Strategies

```
Use ReasoningBank to compare three debugging strategies and recommend one for a high-complexity TypeScript issue.
```

### Design Adaptive Agent

```
Design an adaptive agent loop that recommends a strategy, executes it, records the result, and updates learned patterns.
```

## Limitations

- Requires agentic-flow and AgentDB packages in the host project.
- Provides implementation guidance, not a standalone service.
- Needs enough recorded experiences before recommendations are reliable.
- Does not define privacy controls for stored task context.

## Best Practices

- Record both successful and failed outcomes so recommendations are balanced.
- Attach clear context labels such as task type, language, complexity, and constraints.
- Review learned patterns regularly and remove stale or low-confidence entries.

## Anti Patterns

- Do not store secrets, tokens, or personal data in experience context.
- Do not trust recommendations before enough relevant experiences exist.
- Do not let auto-learning update models without confidence thresholds.

## Security Audit

- Audited at: 2026-07-06T11:28:03.746\+00:00
- Summary: All static external-command findings are false positives caused by Markdown code fences and TypeScript template literals in documentation. The SQLite finding is an example local database path, and no prompt injection, exfiltration intent, or executable payload was found.

## Stats

- Views: 230
- Downloads: 9
- Favorites: 0
- Popularity score: 0