# aelfrice

> aelfrice — robotrocketscience-aelfrice. Use this tool when you need to enhance the learning capabilities of LLM agents through Bayesian memory, which adapts and improves from feedback, providing more accurate outputs over time. It solves problems related to knowledge retention and updating in AI models, allowing for more efficient and effective decision-making. Ideal for use cases where continuous learning and improvement are crucial, with inputs including feedback data and outputs being refined model predictions.

Canonical page: https://skillsregistry.net/skills/robotrocketscience-aelfrice  
JSON: https://api.skillsregistry.net/v1/skills/robotrocketscience-aelfrice

## Description

Bayesian memory that learns from feedback for LLM agents

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** ai-ml
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/robotrocketscience/aelfrice)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "robotrocketscience-aelfrice"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/robotrocketscience-aelfrice` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/robotrocketscience-aelfrice/pull`

---
SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt