# Build Adaptive ReasoningBank Agents

AI agents lose useful lessons when outcomes are not recorded. This skill shows how to capture experiences, learn patterns, and recommend better strategies.

## Install

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

## Metadata

- Status: approved
- Slug: ruvnet-reasoningbank-intelligence
- Skillstore revision: r1
- Version status: missing
- Tree hash: 5b17b6297c4d3a4164652280f737c4b8fdd5df439e3701b0efb86c074e1a2038
- Author: ruvnet
- GitHub username: ruvnet
- License: MIT
- Repository: https://github.com/ruvnet/claude-flow/tree/main/.claude/skills/reasoningbank-intelligence
- Ref: dbe0e719813583400773166a0621f8a9b8185c72
- 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: 76
- Public page: https://skillstore.pages.dev/skills/ruvnet-reasoningbank-intelligence
- Manifest: https://skillstore.pages.dev/api/skills/ruvnet-reasoningbank-intelligence/manifest

## Capabilities

- Shows how to initialize ReasoningBank with AgentDB persistence.
- Demonstrates recording task experiences with outcomes, metrics, and context.
- Explains pattern learning, pattern matching, and strategy comparison workflows.
- Covers meta-learning, transfer learning, and adaptive agent examples.
- Shows querying stored patterns and tracking learning metrics.

## Use Cases

- Improve Code Review Agents: Record review outcomes so an agent can compare strategies and recommend better review approaches over time.
- Optimize Repeated Workflows: Capture task context and outcomes to identify patterns across recurring automation workflows.
- Prototype Learning Systems: Use the examples to design agents that learn from outcomes, transfer knowledge, and track improvement metrics.

## Prompt Templates

### Start a ReasoningBank Plan

```
Use this skill to plan a basic ReasoningBank setup for my agent. Include the required dependencies, storage choice, and first experience records.
```

### Design Experience Records

```
Use this skill to design ReasoningBank experience records for my workflow. Include task type, approach, outcome metrics, and useful context fields.
```

### Compare Agent Strategies

```
Use this skill to compare strategies for my agent workflow. Recommend metrics, confidence thresholds, and how to choose the best strategy.
```

### Build Meta-Learning Workflow

```
Use this skill to design a meta-learning workflow with ReasoningBank. Include transfer learning, pattern review, pruning, and performance measurement.
```

## Limitations

- This is documentation and does not include executable scripts or tests.
- Projects must provide agentic-flow, AgentDB, and Node.js dependencies.
- Recommendation quality depends on enough recorded experiences.
- Learned patterns still need human review for quality and relevance.

## Best Practices

- Record successes and failures with consistent metrics.
- Include rich context so pattern matching has useful signals.
- Review learned patterns before relying on recommendations.

## Anti Patterns

- Do not train only on successful outcomes.
- Do not accept low-confidence patterns without review.
- Do not let old experiences grow without pruning rules.

## Security Audit

- Audited at: 2026-07-20T03:53:58.058\+00:00
- Summary: All 19 external-command alerts are false positives caused by Markdown code fences or JavaScript template literals in documentation examples. The SQLite reference is a local database configuration example, not evidence of sensitive-data exposure or unsafe file access. No prompt injection, exfiltration, or malicious operational intent was found.

## Stats

- Views: 232
- Downloads: 12
- Favorites: 0
- Popularity score: 0