# excel-normalizer

> excel-normalizer — climik-excel-normalizer. Use this tool when you need to standardize and clean Excel data from various sources into a unified format. It solves problems of data inconsistency and incompatibility by converting any Excel file with varying headers into 19 canonical fields, outputting clean JSON. Ideal for use cases requiring data normalization, integration, or analysis, where a standardized format is essential.

Canonical page: https://skillsregistry.net/skills/climik-excel-normalizer  
JSON: https://api.skillsregistry.net/v1/skills/climik-excel-normalizer

## Description

Any Excel, any headers → 19 canonical fields → clean JSON. Works offline, LLM-enhanced, MCP server included.

## Trust

- **Trust score (0–1):** 0.64
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/climik/excel-normalizer)

## 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": "climik-excel-normalizer"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/climik-excel-normalizer` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/climik-excel-normalizer/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