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Found 16 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.
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.
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.
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.
Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
Python data processing with pandas, openpyxl, and lxml. Covers DataFrame operations, Excel I/O, XML parsing, bulk data transformation, and large-file handling. Use when processing tabular data, spreadsheets, or XML in Python. USE WHEN: user mentions "pandas", "DataFrame", "openpyxl", "read_excel", "lxml", "XPath", "CSV processing", "Excel parsing", "bulk data", "large file", "data transformation", "UTF-16", "codecs" DO NOT USE FOR: SQL databases (use sql-expert), NumPy-only math, ML/training
Generate professional data reports with charts, tables, and visualizations
Best practices for NumPy array programming, numerical computing, and performance optimization in Python
Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thresholds, and reporting.
Data analysis, SQL queries, BigQuery operations, and data insights. Use for data analysis tasks and queries.
Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.