Profile unknown datasets for structure, quality, and patterns.
npx skills add https://github.com/MuhammadUA/Axe --skill exploration-muhammadua
Please help me install this Agent Skill. Skill: exploration Source: https://github.com/MuhammadUA/Axe/tree/main/.kortix/opencode/skills/GENERAL-KNOWLEDGE-WORKER/exploration Command: npx skills add https://github.com/MuhammadUA/Axe --skill exploration-muhammadua
Manually analyzing unfamiliar datasets to understand their structure, quality, and underlying patterns is time-consuming, inconsistent, and prone to oversight for data teams. This skill eliminates that friction by providing a standardized, repeatable methodology for end-to-end dataset exploration.
Use the exploration skill to profile the 'customer_transactions' dataset, assess its data quality, and generate a standardized schema documentation template for your analytics team.
High-intent search queries and answers about installing and using this skill.
Data profiling systematically assesses dataset structure, column types, and statistical distributions to evaluate data quality. It helps with new dataset onboarding by automatically identifying completeness gaps, consistency issues, and underlying patterns without requiring manual ad-hoc analysis.
Schema discovery applies column-level statistical profiling and cardinality checks to map unknown relational data warehouse tables. It identifies hierarchical relationships and foreign key links, generating standardized schema documentation for analytical tables.
Perform a data quality audit by scoring completeness, consistency, accuracy, and timeliness across dataset columns. The process flags data quality issues with clear severity ratings to highlight critical gaps and red flags in the dataset.
Yes, dataset exploration includes pattern and relationship discovery to identify correlations, temporal trends, and hierarchical relationships. It characterizes statistical distributions across columns to unlock inherent data insights during the profiling workflow.
Yes, data exploration generates standardized, shareable dataset documentation and lineage maps. This provides a complete schema guide for analysts to use after assessing column types, null rates, and data quality metrics.
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