Profile tables or files to reveal shape, quality, and distributions.
npx skills add https://github.com/openyak/desktop --skill explore-data-openyak
Please help me install this Agent Skill. Skill: explore-data Source: https://github.com/openyak/desktop/tree/main/backend/app/data/plugins/data/skills/explore-data Command: npx skills add https://github.com/openyak/desktop --skill explore-data-openyak
Profiling and exploring a dataset to understand its shape, data quality, and patterns before deeper analysis.
Profile the provided table or file to reveal its shape, quality, and distributions.
High-intent search queries and answers about installing and using this skill.
You can profile a dataset by applying exploration logic to your warehouse tables or uploaded files. This reveals the dataset's shape, column types, null rates, top values, and potential quality issues, ultimately returning a concise data profile and a summary of data quality risks.
The best way to explore column distributions and identify null rates is by running a data profile on the table. This process evaluates the dataset to return column types, null rates, top values, and a summary of data quality risks like duplicates and anomalies.
Yes, you can apply data profiling to both warehouse tables and uploaded files. The profiling mechanism works across both data sources to return column types, null rates, top values, and potential quality issues for your datasets.
To find data quality issues like duplicates and anomalies before deeper analysis, profile your dataset to evaluate null rates and distributions. The resulting data profile highlights potential quality issues and provides a summary of data quality risks.
After profiling your dataset, you should review the recommended follow-up explorations included in the results. The data profile provides recommended next steps and potential dimensions and metrics to guide your subsequent analytics projects.
joonlabSplit project files between Git repositories and Hugging Face datasets.
EvolutionAPIProfile PostgreSQL tables or uploaded files to identify data quality issues.
cy-waliProfile datasets to assess shape, column types, null rates, and distributions.
lilbom32Profile dataset structure, quality, and patterns to guide analysis.
