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Found 15 Skills
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.
Fast, low-cost exploration of Robonet trading resources. Browse 8 data tools to explore available trading pairs, technical indicators, Allora ML topics, existing strategies, and backtest results. All tools execute in <1 second with minimal cost (free to $0.001). Use this skill first before building or testing strategies to understand what resources are available.
Salesforce Data 360 Retrieve phase for query-sql, queryv2, profile APIs, metadata introspection, and query MCP usage. TRIGGER when: the user writes or debugs Data 360 SQL, profile retrieval, metadata lookup, or query tooling.
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.
Create, manage, and query Databricks Genie Agents — curated, per-data natural-language agents (formerly Genie Spaces): build, export/import, migrate across workspaces, and ask questions of a *specific* Agent via the Conversation API. For general data questions or finding data across your workspace, use databricks-data-discovery (Genie One) instead.
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.
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.
Fast in-process analytical database for SQL queries on DataFrames, CSV, Parquet, JSON files, and more. Use when user wants to perform SQL analytics on data files or Python DataFrames (pandas, Polars), run complex aggregations, joins, or window functions, or query external data sources without loading into memory. Best for analytical workloads, OLAP queries, and data exploration.
Discover and explore databases, tables, columns, and data shares in MotherDuck. Use when you need to understand what data is available, preview table contents, or search the data catalog.
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.
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".