jupyter-notebook
Jupyter Notebook
Overview
Use this skill to produce notebooks another engineer can execute and trust without hidden assumptions.
Scope Boundaries
- Use this skill when the task matches the trigger condition described in
description. - Do not use this skill when the primary task falls outside this skill's domain.
Shared References
- Notebook structure guidance:
references/notebook-structure.md
- Reproducibility and sanitization rules:
references/reproducibility-and-sanitization-rules.md
Templates And Assets
- Notebook run log template:
assets/notebook-run-log-template.md
- Notebook result summary template:
assets/notebook-summary-template.md
- Delivery checklist:
assets/notebook-delivery-checklist.md
Inputs To Gather
- Notebook purpose (
exploration,debug,tutorial,verification). - Runtime constraints (Python version, package policy, data access).
- Expected deliverable shape (single notebook or multi-notebook set).
- Sharing boundary (internal-only vs external audience).
Deliverables
- Executable notebook with deterministic order.
- Runtime/dependency assumptions and execution log.
- Decision-grade summary linked to output cells.
- Sanitized artifact for sharing when required.
Workflow
- Define audience, decision question, and reproducibility constraints.
- Create scaffold with
scripts/new_notebook.pywhen useful. - Structure notebook sections per
references/notebook-structure.md. - Execute from fresh kernel and record results in
assets/notebook-run-log-template.md. - Summarize findings with
assets/notebook-summary-template.md. - Validate shareability via
assets/notebook-delivery-checklist.md.
Scripts
- Create experiment scaffold:
python3 scripts/new_notebook.py --kind experiment --title 'My Experiment' --out output/notebooks/my-experiment.ipynb
- Create tutorial scaffold:
python3 scripts/new_notebook.py --kind tutorial --title 'My Tutorial' --out output/notebooks/my-tutorial.ipynb
Quality Standard
- Notebook runs top-to-bottom from fresh kernel.
- Claims are tied to explicit output evidence.
- Runtime and data assumptions are reproducible.
- Shared outputs are sanitized for secrets and personal data.
Failure Conditions
- Stop when runtime/data preconditions cannot be specified precisely.
- Stop when repeated runs produce unstable outputs without explanation.
- Stop external sharing when sensitive output cannot be sanitized.
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