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Found 16 Skills
Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze.
An automated data exploration and visualization tool that provides a complete EDA solution from data loading to professional report generation. It supports multiple chart types, intelligent data diagnosis, modeling evaluation, and HTML report generation. Suitable for data analysis projects in fields such as healthcare, finance, e-commerce, etc.
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
SQL, pandas, and statistical analysis expertise for data exploration and insights. Use when: analyzing data, writing SQL queries, using pandas, performing statistical analysis, or when user mentions data analysis, SQL, pandas, statistics, or needs help exploring datasets.
Guide for creating Observable Notebooks 2.0, the open-source notebook system for interactive data visualization and exploration. Use this skill when creating, editing, or building Observable notebooks.
Runs metrics queries against Axiom MetricsDB via scripts. Discovers available metrics, tags, and tag values. Use when asked to query metrics, explore metric datasets, check metric values, or investigate OTel metrics data.
Databricks CLI operations: auth, profiles, Unity Catalog, data exploration, jobs, pipelines, clusters, model serving, bundles and more. Contains up-to-date guidelines for all Databricks CLI tasks, useful for all Databricks-related tasks.
Databricks CLI operations: auth, profiles, data exploration, and bundles. Contains up-to-date guidelines for Databricks-related CLI tasks.
Comprehensive academic writing skill for drafting journal-ready manuscripts. Orchestrates specialized sub-skills for introduction sections (q-intro), descriptive analysis (q-descriptive-analysis), methods sections (q-methods), and results sections (q-results). Use when the user needs end-to-end support for academic manuscript preparation, from initial data exploration through publication-ready prose. Follows APA 7th edition formatting standards.
Use when querying Outlit customer data via MCP tools (outlit_*). Triggers on customer analytics, revenue metrics, activity timelines, cohort analysis, churn risk assessment, SQL queries against analytics data, or any Outlit data exploration task.
Analyze lakehouse data interactively using Fabric Livy sessions and PySpark/Spark SQL for advanced analytics, DataFrames, cross-lakehouse joins, Delta time-travel, and unstructured/JSON data. Use when the user explicitly asks for PySpark, Spark DataFrames, Livy sessions, or Python-based analysis — NOT for simple SQL queries. Triggers: "PySpark", "Spark SQL", "analyze with PySpark", "Spark DataFrame", "Livy session", "lakehouse with Python", "PySpark analysis", "PySpark data quality", "Delta time-travel with Spark".
Use these skills when you need to handle large-scale data exploration and dataset management. Use when users need to find data assets or run SQL at scale. Provides metadata discovery and query execution across the data warehouse.