# ai-fit-tracker

> ai-fit-tracker — zitaharry-ai-fit-tracker. Use this tool when you need to track and analyze AI model performance and fitness across different versions and iterations. It solves problems related to model optimization, comparison, and selection by providing a standardized interface for logging and retrieving model metrics. The ai-fit-tracker takes in model performance data as input and outputs comparable metrics and visualizations, ideal for use in machine learning development and deployment contexts.

Canonical page: https://skillsregistry.net/skills/zitaharry-ai-fit-tracker  
JSON: https://api.skillsregistry.net/v1/skills/zitaharry-ai-fit-tracker

## Trust

- **Trust score (0–1):** 0.95
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/zitaharry/ai-fit-tracker)

## 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": "zitaharry-ai-fit-tracker"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/zitaharry-ai-fit-tracker` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/zitaharry-ai-fit-tracker/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