# learning-loop

> learning-loop — yoder-bawt-learning-loop. Use this tool when you need to continuously improve AI agent performance through structured self-improvement, addressing issues like stagnant accuracy and incomplete knowledge sharing. It solves problems of skill degradation and limited inter-agent collaboration by implementing confidence decay, cross-agent sharing, and anomaly detection. This system accepts AI agent performance data as input and outputs optimized improvement strategies, ideal for use in dynamic environments where agent adaptability is crucial.

Canonical page: https://skillsregistry.net/skills/yoder-bawt-learning-loop  
JSON: https://api.skillsregistry.net/v1/skills/yoder-bawt-learning-loop

## Description

Structured self-improvement system for AI agents with confidence decay, cross-agent sharing, and anomaly detection.

## Trust

- **Trust score (0–1):** 0.94
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/yoder-bawt-learning-loop)

## 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": "yoder-bawt-learning-loop"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/yoder-bawt-learning-loop` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/yoder-bawt-learning-loop/pull`

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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