| 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, claude-code, codex |
| metadata | {"audience":"data-scientists","workflow":"analysis"} |
What I do
I help explore and understand datasets loaded in Jupyter notebooks. I:
- Find data-loading cells and loaded variables
- Inspect dataframe schemas, shapes, and quality
- Summarize distributions, nulls, and key patterns
- 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.
