Marcinmiklitz Jupyter Notebooks Skill Jupyter Notebooks

Jupyter Notebooks Skill

Programmatic notebook operations from CLI only (no Jupyter UI):

• create notebooks and templates,
• perform cell-level CRUD and metadata edits,
• execute whole notebooks or selective ranges,
• validate structure and operational notebook hygiene,
• convert formats,
• inspect/strip outputs,
• run content-aware diff and merge.

Python requirement: 3.9+.

Dependencies and Invocation

Scripts require these Python packages:

• Core: nbformat, nbclient, nbconvert, nbdime
• Optional: papermill, nbstripout

Each script declares its own dependencies via PEP 723 inline metadata (the # /// script block at the top of each file). Read this block to determine exactly which packages a script needs.

Before first use

Run the preflight check to verify environment readiness:

python scripts/nb_preflight.py --mode auto

This script has no dependencies (stdlib only). It supports --mode auto|python|uv:

• auto (default): succeed if either Python-mode or uv-mode is ready,
• python: check interpreter package availability directly,
• uv: verify uv run readiness by smoke-testing all script --help entrypoints.

It emits a JSON report to stdout with ok, ok_python, ok_uv, ok_execute, and mode-specific details.

If packages are missing:

1. Determine the project's package manager by inspecting the environment (look for pyproject.toml, requirements.txt, Pipfile, uv.lock, poetry.lock, or similar).
2. Install the missing packages using that tooling. If you lack permission or are unsure, ask the user.
3. Re-run nb_preflight.py to confirm.

Do not skip or abandon a script because its dependencies are not currently installed. Resolve them first.

How to invoke

Run scripts with whatever Python interpreter the project environment provides:

python scripts/<tool>.py [args]

If the project uses a tool that handles PEP 723 inline metadata automatically (e.g. uv run), that works too — the metadata block in each script is compatible.

Additional requirements

• Execution (nb_execute.py) requires a Jupyter kernel (typically ipykernel).
• PDF export depends on external tools (pandoc/TeX or webpdf stack) beyond Python packages.

Script Map

• scripts/nb_create.py: Create notebook from blank/template/script; inject script as one cell into existing notebook.
• scripts/nb_cells.py: Cell CRUD, reorder, metadata/tags, bulk ops, regex search.
• scripts/nb_execute.py: Execute with nbclient (default), selective range execution, or papermill mode.
• scripts/nb_validate.py: Schema validation + operational lint checks for CI/pre-commit.
• scripts/nb_convert.py: Convert notebook to html/pdf/latex/script/markdown/rst/slides.
• scripts/nb_metadata.py: Notebook/cell metadata and tag management.
• scripts/nb_outputs.py: Output listing, size checks, stripping, image extraction, clear execution counts.
• scripts/nb_diff.py: nbdime-backed diff (text/json) and three-way merge.

Task Routing

• Create new notebook/template/script-to-notebook:
• Use scripts/nb_create.py.
• Inspect or mutate cell layout/content:
• Use scripts/nb_cells.py; details in references/cell-operations.md.
• Execute notebook:
• Use scripts/nb_execute.py; decision guidance in references/execution-guide.md.
• Parameterized runs:
• Use scripts/nb_execute.py --papermill; patterns in references/papermill-patterns.md.
• Validate for CI:
• Use scripts/nb_validate.py; rules in references/validation-and-linting.md.
• Export to document/script formats:
• Use scripts/nb_convert.py.
• Manage metadata/tags:
• Use scripts/nb_metadata.py; schema map in references/metadata-reference.md.
• Handle outputs/size/images:
• Use scripts/nb_outputs.py; guidance in references/output-handling.md.
• Diff/merge in git workflows:
• Use scripts/nb_diff.py; setup in references/versioning-guide.md.
• Failure/timeout/kernel issues:
• See references/error-handling.md.

Quick-Reference Table

• Create notebook: nb_create.py
• Edit cells: nb_cells.py
• Execute notebook: nb_execute.py
• Validate notebook: nb_validate.py
• Convert/export: nb_convert.py
• Metadata/tag ops: nb_metadata.py
• Output management: nb_outputs.py
• Diff/merge: nb_diff.py

Quick-Start One-Liners

Create from template:

python scripts/nb_create.py --template data-analysis --output notebooks/0.1-mmk-initial-eda.ipynb

Add a code cell:

python scripts/nb_cells.py --input notebooks/0.1-mmk-initial-eda.ipynb add --cell-type code --source "print('hello')"

Execute selective range (cornerstone pattern):

python scripts/nb_execute.py --input notebooks/0.1-mmk-initial-eda.ipynb --start-index 3 --end-index 7

Validate for CI:

python scripts/nb_validate.py --input notebooks/0.1-mmk-initial-eda.ipynb --forbid-outputs

Convert to markdown:

python scripts/nb_convert.py --input notebooks/0.1-mmk-initial-eda.ipynb --to markdown --output reports/eda.md

Output Convention

Scripts follow a consistent contract:

• Human-readable progress logs on stderr.
• Machine-readable JSON payload on stdout.
• Exit codes:
• general scripts: 0 success, 1 error.
• nb_validate.py: 0 no issues, 1 issues found, 2 runtime/tool failure.

Versioning Defaults

• Use CCDS-style notebook naming for order and ownership:
• <step>-<owner>-<description>.ipynb
• Prefer nbstripout + nbdime together for repository hygiene and readable diffs.
• Example .gitattributes is provided in assets/.gitattributes.example.

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