# humanumbers

> humanumbers — resultity-humanumbers. Use this tool when you need to translate raw numbers into human-readable formats, solving the problem of unclear specifications and metrics in language models. It takes numerical inputs and outputs humanized measurements, accessible via MCP, API, or Python interfaces. Ideal for use cases where readability and understanding of numerical data are crucial, such as data analysis and reporting.

Canonical page: https://skillsregistry.net/skills/resultity-humanumbers  
JSON: https://api.skillsregistry.net/v1/skills/resultity-humanumbers

## Description

Stop letting your LLM speak in raw numbers — humanize specs, metrics, and measurements via MCP, API, or Python

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** ai-ml
- **Updated:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/resultity/humanumbers)

## 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": "resultity-humanumbers"
    }
  }
}
```

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