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Found 51 Skills
Query FRED (Federal Reserve Economic Data) API for 800,000+ economic time series from 100+ sources. Access GDP, unemployment, inflation, interest rates, exchange rates, housing, and regional data. Use for macroeconomic analysis, financial research, policy studies, economic forecasting, and academic research requiring U.S. and international economic indicators.
In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files, remote databases, and cloud storage without a server. Use when the user wants to write SQL queries against Parquet/CSV/ JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.), build stateful analytical pipelines with Session, use parametrized queries, window functions, or other advanced ClickHouse SQL features. Also use when the user explicitly mentions chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins. Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead.
This skill should be used when the user asks to "connect to MySQL with asyncio", "use aiomysql", "set up an async MySQL connection pool", "query MySQL asynchronously in Python", or needs guidance on aiomysql best practices, connection lifecycle, transactions, or cursor types.
This skill should be used when the user asks to "use SQLModel", "define SQLModel models", "connect SQLModel with FastAPI", "set up a database with SQLModel", or needs guidance on SQLModel best practices, relationships, or session management.
Generates Python code using BigQuery DataFrames (BigFrames), the pandas/scikit-learn-style API over BigQuery. Use when writing BigFrames code or doing pandas-style dataframe/ML work against BigQuery (e.g. in a notebook). Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.
Provides Python patterns for type-first development with dataclasses, discriminated unions, NewType, and Protocol. Must use when reading or writing Python files.
Retrieve Chinese financial market data (A-shares, Hong Kong stocks, US stocks, funds, futures, bonds). Supports over 220 Tushare Pro interfaces: stock quotes, financial statements, macroeconomic indicators. Use this when users request stock price data, financial analysis, index quotes, macro data such as GDP/CPI.
Manage the academic workspace — project structure, templates, and output organization.
Audits Python + BigQuery pipelines for cost safety, idempotency, and production readiness. Returns a structured report with exact patch locations.
Expert in high-performance CSV processing, parsing, and data cleaning using Python, DuckDB, and command-line tools. Use when working with CSV files, cleaning data, transforming datasets, or processing large tabular data files.
Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.
Build end-to-end ETL pipelines and analytics dashboards using the Harvard Art Museums API with Python, SQL, and Streamlit