# Optimize Agent Learning with ReasoningBank

Agents often repeat weak strategies because learning data stays unstructured. This skill organizes trajectories, patterns, benchmarks, and recommendations into a ReasoningBank workflow.

## Install

```bash
npx skillstore add dnyoussef/when-optimizing-agent-learning-use-reasoningbank-intelligence
```

## Metadata

- Status: approved
- Slug: dnyoussef-when-optimizing-agent-learning-use-reasoningbank-intelligence
- Version: 1.0.0
- Author version: 1.0.0
- Skillstore revision: r1
- Version status: valid
- Tree hash: 564d46751d872cc54377a50550542ad6adca339e1d86f023bb8352ab52ea26a2
- Author: DNYoussef
- GitHub username: DNYoussef
- License: MIT
- Repository: https://github.com/DNYoussef/ai-chrome-extension/tree/main/.claude/skills/utilities/when-optimizing-agent-learning-use-reasoningbank-intelligence
- Ref: c579efd02184f1128d6741d16e6c8d951d7d4208
- Supported tools: Claude, Codex, Claude Code
- Audit status: complete
- Agent install advisory: confirmation\_required
- Manual install advisory: allowed
- Artifact signature: available
- Audit attestation: unavailable
- Human verification: not\_verified
- Risk factors: external\_commands, filesystem
- Quality score: 38
- Quality tier: warning
- Public page: https://skillstore.pages.dev/skills/dnyoussef-when-optimizing-agent-learning-use-reasoningbank-intelligence
- Manifest: https://skillstore.pages.dev/api/skills/dnyoussef-when-optimizing-agent-learning-use-reasoningbank-intelligence/manifest

## Capabilities

- Defines a five-phase workflow for initializing ReasoningBank, capturing trajectories, optimizing strategies, validating gains, and exporting outputs.
- Shows JavaScript examples for trajectory schemas, verdict scoring, pattern extraction, recommendation generation, benchmarking, and reporting.
- Includes optional AgentDB guidance for HNSW indexing, quantization, and vector similarity search.
- Specifies validation criteria, success metrics, output artifacts, and deployment readiness checks.
- Provides hook examples for claude-flow pre-task and post-task memory integration.

## Use Cases

- Improve Agent Strategy Selection: Capture successful and failed trajectories, then turn repeated patterns into prioritized strategy recommendations.
- Benchmark Learning Gains: Compare baseline and optimized agents with shared test cases and clear performance metrics.
- Prepare Learning Artifacts for Deployment: Package models, pattern libraries, integration notes, and learning reports for controlled production rollout.

## Prompt Templates

### Plan a Learning Setup

```
Use this skill to outline a ReasoningBank setup for my agent. Include required storage, trajectory fields, verdict criteria, and validation checks.
```

### Capture Useful Trajectories

```
Use this skill to design a trajectory capture plan for my agent. Include success signals, failure labels, privacy safeguards, and pattern extraction steps.
```

### Optimize Existing Agents

```
Use this skill to analyze my current agent workflow and propose ReasoningBank optimizations, benchmark tests, and recommendation ranking criteria.
```

### Deploy a Learning System

```
Use this skill to create a production rollout plan for learned strategies. Include export controls, monitoring, retraining cadence, and rollback criteria.
```

## Limitations

- Examples are illustrative and require real ReasoningBank, agent, and memory implementations.
- It does not include a complete runnable package or automated tests.
- Model quality depends on diverse trajectories and reliable success metrics.
- The export examples use local temporary files and should be hardened before production.

## Best Practices

- Collect both successful and failed trajectories before training recommendations.
- Define measurable verdict criteria before comparing baseline and optimized agents.
- Redact private data before storing trajectories, reports, or exported learning artifacts.

## Anti Patterns

- Do not train from only successful examples because recommendations will be biased.
- Do not deploy learned strategies before benchmark results show stable improvement.
- Do not store reasoning traces or reports in shared locations without access controls.

## Security Audit

- Audited at: 2026-07-05T13:05:58.55\+00:00
- Summary: Most external-command alerts are markdown fences or JavaScript template literals and were false positives. I confirmed one documented npx workflow command and the fixed /tmp export examples as medium risks. I also found a high-severity data-handling issue because trajectories and exports can contain sensitive agent reasoning without redaction.

## Stats

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- Downloads: 5
- Favorites: 0
- Popularity score: 0