Access clinical datasets from Python and execute SQL queries to return DataFrames.
npx skills add https://github.com/hannesill/m4 --skill m4-api
Please help me install this Agent Skill. Skill: m4-api Source: https://github.com/hannesill/m4/tree/main/src/m4/skills/system/m4-api Command: npx skills add https://github.com/hannesill/m4 --skill m4-api
The M4 Python API lets developers access clinical datasets from code, enabling reproducible analyses without manual SQL scripting. It mirrors MCP tooling but returns native Python objects like DataFrames and dicts, supporting multi-step workflows and programmatic data exploration.
Set a dataset, inspect the schema, and run SQL queries through the Python API to obtain results.
High-intent search queries and answers about installing and using this skill.
To query clinical datasets programmatically, use the M4 Python API to set a dataset, inspect schemas, and execute SQL queries that return native pandas DataFrames for iterative analysis.
Discover MIMIC-IV table schemas by applying the get_schema and get_table_info functions to introspect dataset structures and retrieve table metadata before executing queries.
Yes, you can run multi-step data analyses on clinical datasets and receive direct pandas DataFrames by chaining dataset configuration, schema discovery, and SQL execution functions.
The best way to integrate clinical dataset exploration into a Python pipeline is using the M4 API to programmatically manage datasets and retrieve query results as native Python objects.
No, the M4 Python API operates without external dependencies, allowing you to configure datasets, inspect schemas, and execute SQL queries directly within your Python code execution environment.
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