eda-copilot

name	eda-copilot
description	Explore datasets in Jupyter notebooks by inspecting dataframes, summarizing schemas, checking data quality, and proposing EDA cells. Use when asked to analyze data or add exploratory analysis.
license	MIT
compatibility	opencode
metadata	{"audience":"data-scientists","workflow":"analysis"}

What I do

I help explore and understand datasets loaded in Jupyter notebooks. I:

1. Find data-loading cells and loaded variables
2. Inspect dataframe schemas, shapes, and quality
3. Summarize distributions, nulls, and key patterns
4. Propose or insert compact EDA cells if requested

When to use me

• "Analyze this dataframe"
• "Add EDA"
• "What does this dataset look like?"
• "Explore the data in this notebook"

How I work

Step 1: Find the Data

If in live mode, list variables:

notebook-tools list-variables --notebook <path> --start-if-missing --pretty

Or search for data-loading patterns:

notebook-tools search-cells --notebook <path> --query "read_csv" --pretty

Step 2: Inspect the Dataframe

notebook-tools inspect-dataframe --notebook <path> --variable-name <name> --pretty

This returns shape, columns, dtypes, null counts, and sample rows.

Step 3: Read Loading Context (if needed)

notebook-tools read-cells --notebook <path> --index <N> --summary-mode compact --pretty

Step 4: Propose EDA

Based on the dataframe inspection, propose:

• Schema summary
• Null/missing value analysis
• Numeric distributions
• Categorical value counts
• Recommended visualizations

If the user wants EDA cells inserted, use:

notebook-tools insert-cell --notebook <path> --position <N> --cell-type code --content "<eda-code>"

Rules

• Always inspect schema before proposing analysis
• Prefer bounded summaries over full dataframe dumps
• Only read data-loading cells if the source is relevant to the question
• In file-only mode, recommend live inspection for accurate analysis
• Propose EDA cells but ask for confirmation before inserting

Confirmation Required

Ask before inserting new cells into the notebook.

Dependencies

Requires notebook-tools CLI. Live inspection requires ipykernel, jupyter_client, and pandas.