# Occam

> Use this tool when you need to discover underlying patterns in complex data by identifying the simplest mathematical equations that accurately describe it. Occam solves problems of overfitting and model complexity by providing a straightforward and interpretable representation of the data. It takes in dataset inputs and outputs concise, symbolic equations via SINDy and PySR symbolic regression methods.

Canonical page: https://skillsregistry.net/skills/fit-occam-occam  
JSON: https://api.skillsregistry.net/v1/skills/fit-occam-occam

## Description

Finds the simplest equation consistent with your data. SINDy and PySR symbolic regression via MCP.

## Trust

- **Trust score (0–1):** 0.30
- **Verification tier:** unverified
- **Last scanned:** 2026-08-24

## Facts

- **Version:** 0.1.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-08-24

## Source

- **Source listing:** [MCP Registry](https://registry.modelcontextprotocol.io/v0/servers/fit.occam%2Foccam)

## Use it

MCP endpoint published by the skill: `https://occam.fit/mcp/`

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": "fit-occam-occam"
    }
  }
}
```

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