complex-analysis

name	complex-analysis
description	Run multi-step SQL plus Python/statistics/ML analysis while keeping tools thin and evidence grounded
tags	["complex-analysis","python","machine-learning","statistics"]
version	1.0.0

Complex Analysis

Use this workflow when the user asks for deeper analysis than a single SQL aggregation: correlations, clustering, forecasting, simple machine-learning models, anomaly detection, visualization, or custom scoring.

Workflow

1. Scope with SQL first

   • Use list_tables/describe_table when schema is unknown.
   • Use execute_sql to confirm row counts, columns, and a small preview.
   • Extract the narrowest table/query needed for Python.

2. Run Python only when it adds value

   • Load execute-python-analysis before execute_python.
   • In Python, use the pre-loaded conn object. Do not import duckdb or create a new connection.
   • Query data with conn.sql("SELECT ...").df().
   • Available library names are pre-loaded when installed: pandas (pd), numpy (np), matplotlib (plt), scikit-learn (sklearn), and scipy (scipy). Check optional libraries before using them; if plotting is unavailable, return text/table evidence instead of retrying chart code.

3. Model responsibly

   • Prefer simple interpretable baselines over complex models.
   • Report features, target, sample size, and validation approach.
   • If there is too little data, say so and use descriptive analysis instead.

4. Return evidence

   • Include the SQL/Python results that support the conclusion.
   • Label exploratory findings as exploratory.
   • Mention generated plot paths only if plots were created.

Safety

Connected source access remains read-only. Do not mutate external databases, write pipeline files, or publish artifacts unless the user explicitly asks and the relevant approval-gated skill allows it.