Run SQL analytics directly on CSV, Parquet, and JSON files and Pandas or Polars DataFrames.
npx skills add https://github.com/silvainfm/claude-skills --skill duckdb
Please help me install this Agent Skill. Skill: duckdb Source: https://github.com/silvainfm/claude-skills/tree/main/duckdb Command: npx skills add https://github.com/silvainfm/claude-skills --skill duckdb
This Skill enables fast, in-process SQL analytics directly on data files (CSV, Parquet, JSON) and Python DataFrames (pandas, Polars) without loading data into memory or running a separate database server.
Use this skill to run a simple SQL query on a CSV file, for example: SELECT category, AVG(price) AS avg_price FROM 'sales.csv' GROUP BY category
High-intent search queries and answers about installing and using this skill.
SQL queries on CSV, Parquet, and JSON files execute in-process without importing data into memory or running a separate server. DuckDB provides a file-first query engine that reads data directly from files, applies SQL operations like aggregations and joins, and returns results immediately for ad-hoc exploration.
Yes, pandas and Polars DataFrames can be queried directly with SQL. DuckDB accepts DataFrames as data sources, enabling you to write SQL queries—including joins, window functions, and CTEs—against DataFrame data without converting to another format.
Cross-source joins combine CSV, Parquet, JSON files and pandas or Polars DataFrames in a single SQL query. DuckDB treats all sources uniformly, allowing you to join files to DataFrames, aggregate across sources, and retrieve results without manual data consolidation.
No database server is required. DuckDB runs as an in-process SQL engine on your local machine, querying files and DataFrames directly without external infrastructure. Setup is minimal—queries execute immediately against your data sources.
Yes, DuckDB supports advanced SQL features including window functions, common table expressions (CTEs), and subqueries. These enable complex aggregations, ranking, and multi-step analytics on CSV, Parquet, JSON files and DataFrames within a single query.
CSV, Parquet, and JSON files are supported for direct SQL queries. DuckDB also works with pandas and Polars DataFrames, enabling analytics across multiple file formats and in-memory data structures in a single workflow.
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