wrangling-skills
10 data wrangling skills. Trigger: messy data, format conversion, missing values, data reshaping. Design: pipeline-oriented recipes for common data cleaning and transformation tasks.
Best use case
wrangling-skills is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
10 data wrangling skills. Trigger: messy data, format conversion, missing values, data reshaping. Design: pipeline-oriented recipes for common data cleaning and transformation tasks.
Teams using wrangling-skills should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/wrangling/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How wrangling-skills Compares
| Feature / Agent | wrangling-skills | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
10 data wrangling skills. Trigger: messy data, format conversion, missing values, data reshaping. Design: pipeline-oriented recipes for common data cleaning and transformation tasks.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Data Wrangling — 10 Skills Select the skill matching the user's need, then `read` its SKILL.md. | Skill | Description | |-------|-------------| | [csv-data-analyzer](./csv-data-analyzer/SKILL.md) | Load, explore, clean, and analyze CSV data with statistical summaries | | [data-cleaning-pipeline](./data-cleaning-pipeline/SKILL.md) | Systematic data cleaning workflows for research datasets | | [data-cog-guide](./data-cog-guide/SKILL.md) | Upload messy CSVs with minimal prompting for deep automated analysis | | [missing-data-handling](./missing-data-handling/SKILL.md) | Diagnose missing data patterns and apply appropriate imputation strategies | | [pandas-data-wrangling](./pandas-data-wrangling/SKILL.md) | Data cleaning, transformation, and exploratory analysis with pandas | | [questionnaire-design-guide](./questionnaire-design-guide/SKILL.md) | Questionnaire and survey design with Likert scales and coding | | [stata-data-cleaning](./stata-data-cleaning/SKILL.md) | Clean, transform, and validate messy research data using Stata | | [streamline-analyst-guide](./streamline-analyst-guide/SKILL.md) | End-to-end data analysis AI agent with Streamlit UI | | [survey-data-processing](./survey-data-processing/SKILL.md) | Clean, recode, and prepare survey response data for analysis | | [text-mining-guide](./text-mining-guide/SKILL.md) | Apply NLP and text mining techniques to research text data |
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