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Make Jupyter notebooks easier to read and share. Identifies duplicated cells, improves structure, adds markdown documentation, and prepares notebooks for collaboration.
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基于 SOC 职业分类
| name | notebook-cleanup |
| description | Make Jupyter notebooks easier to read and share. Identifies duplicated cells, improves structure, adds markdown documentation, and prepares notebooks for collaboration. |
| license | MIT |
| compatibility | opencode, claude-code, codex |
| metadata | {"audience":"data-scientists","workflow":"maintenance"} |
I make Jupyter notebooks cleaner, more readable, and easier to share. I:
notebook-tools list-cells --notebook <path> --pretty
Look for:
Use search to find patterns:
notebook-tools search-cells --notebook <path> --query "import" --pretty
Check for reproducibility issues:
notebook-tools check-reproducibility --notebook <path> --pretty
Move misplaced cells:
notebook-tools move-cell --notebook <path> --index <N> --position <M>
Split long cells:
notebook-tools split-cell --notebook <path> --index <N> --split-at <line>
Merge adjacent cells:
notebook-tools merge-cells --notebook <path> --index <N> --index <M>
Add documentation:
notebook-tools insert-cell --notebook <path> --position <N> --cell-type markdown --content "## Section Title\n\nDescription here."
Report all structural changes:
Ask before:
Requires notebook-tools CLI.
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.
Diagnose and minimally fix failing notebook cells. Reads the failing cell, inspects the error output, checks dependencies, applies the smallest fix, and reruns only what is needed.
Explain what a Jupyter notebook does by inspecting structure, summarizing workflow, and reading only key cells. Use when asked to walk through, summarize, or understand a notebook.
Audit Jupyter notebooks for hidden state, stale outputs, execution order issues, and non-determinism. Produces a report with findings, confidence labels, and suggested fixes.
Improve or debug plots in Jupyter notebooks. Finds plotting cells, critiques visualizations, applies minimal fixes, and reruns only the affected cells.
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.