# Build AgentDB Learning Plugins

Teams need repeatable ways to build learning agents without rebuilding reinforcement learning scaffolding. This skill guides AgentDB plugin creation, training, evaluation, and algorithm selection.

## Install

```bash
npx skillstore add ruvnet/agentdb-learning-plugins
```

## Metadata

- Status: approved
- Slug: ruvnet-agentdb-learning-plugins
- Skillstore revision: r1
- Version status: missing
- Tree hash: 2ac370685dbabf9483665383672bcfc650356de39cd8f7a6ce21d7f0e34e4116
- Author: ruvnet
- GitHub username: ruvnet
- License: MIT
- Repository: https://github.com/ruvnet/claude-flow/tree/main/.claude/skills/agentdb-learning
- 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, network
- Quality score: 67
- Public page: https://skillstore.pages.dev/skills/ruvnet-agentdb-learning-plugins
- Manifest: https://skillstore.pages.dev/api/skills/ruvnet-agentdb-learning-plugins/manifest

## Capabilities

- Creates AgentDB plugin workflows with CLI examples for templates and inspection.
- Explains nine learning algorithms and the cases where each is useful.
- Shows how to store experiences, train models, and evaluate suggested actions.
- Covers experience replay, prioritized replay, and incremental training patterns.
- Connects trained learning models with AgentDB reasoning retrieval workflows.
- Includes troubleshooting guidance for convergence, overfitting, and slow training.

## Use Cases

- Prototype a learning agent: Select a template, create a plugin, and plan a training loop for a new agent task.
- Add adaptive behavior to agents: Store task experiences in AgentDB and retrieve learned patterns for future decisions.
- Compare reinforcement learning methods: Review algorithm strengths and choose a method for offline, value-based, or policy-gradient experiments.

## Prompt Templates

### Create my first plugin

```
Use AgentDB Learning Plugins to help me choose a starter template for a simple game agent. Include prerequisites and safe setup steps.
```

### Choose an algorithm

```
Compare Decision Transformer, Q-Learning, SARSA, and Actor-Critic for my task. Recommend one and explain the tradeoffs.
```

### Design a training workflow

```
Plan an AgentDB training workflow that collects experiences, trains in batches, validates results, and reports model quality.
```

### Plan a multi-agent learning system

```
Design an advanced AgentDB learning setup for several agents sharing experiences. Include replay strategy, evaluation signals, and safety checks.
```

## Limitations

- Requires Node.js 18 and AgentDB from agentic-flow.
- Assumes users understand basic reinforcement learning concepts.
- Provides guidance and examples; it does not train models by itself.
- Uses npx examples that should be reviewed before execution.

## Best Practices

- Pin AgentDB package versions before running generated npx commands.
- Keep training experiences free of credentials and customer secrets.
- Validate learned behavior against separate evaluation data before deployment.

## Anti Patterns

- Do not run npx commands from untrusted branches without review.
- Do not store raw private user data as training experiences.
- Do not treat sample metrics as production readiness evidence.

## Security Audit

- Audited at: 2026-07-20T03:41:18.066\+00:00
- Summary: The 55 static findings are false positives caused mainly by Markdown code fences, documentation links, and an example SQLite path. One contextual concern remains: the skill recommends executing an unpinned npx package tagged latest, which creates a supply-chain trust risk but does not show malicious intent.

## Stats

- Views: 272
- Downloads: 11
- Favorites: 0
- Popularity score: 0