# data-cleaning-annotation-workflow

> data-cleaning-annotation-workflow — deyashmukh-data-cleaning-annotation-workflow. Use this tool when you need to streamline data preparation for time series datasets in energy, manufacturing, or climate domains. It solves problems related to data inconsistency and annotation by providing a complete workflow from Kaggle to a data annotation platform. The tool takes in raw time series data and outputs cleaned, annotated datasets ready for model training.

Canonical page: https://skillsregistry.net/skills/deyashmukh-data-cleaning-annotation-workflow  
JSON: https://api.skillsregistry.net/v1/skills/deyashmukh-data-cleaning-annotation-workflow

## Description

Complete workflow for time series datasets (Energy, Manufacturing, Climate) on Kaggle to Data Annotation platform.

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** scanned
- **Last scanned:** 2026-05-20

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** other
- **Updated:** 2026-09-14

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/deyashmukh-data-cleaning-annotation-workflow)

## 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": "deyashmukh-data-cleaning-annotation-workflow"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/deyashmukh-data-cleaning-annotation-workflow` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/deyashmukh-data-cleaning-annotation-workflow/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