Loading...
Loading...
Found 17 Skills
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.
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.
Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.
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.
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".