AI45Lab
Registry indexed
skill.jupyter-notebook
Use when the user asks to create, scaffold, or edit Jupyter notebooks
Overview
Use when the user asks to create, scaffold, or edit Jupyter notebooks
Read full documentation
Source documentation, not instructions for this website. Review permissions before running any commands.
Jupyter Notebook Skill
Create clean, reproducible Jupyter notebooks for two primary modes:
- Experiments and exploratory analysis
- Tutorials and teaching-oriented walkthroughs
Prefer the bundled templates and the helper script for consistent structure and fewer JSON mistakes.
When to use
- Create a new
.ipynbnotebook from scratch. - Convert rough notes or scripts into a structured notebook.
- Refactor an existing notebook to be more reproducible and skimmable.
- Build experiments or tutorials that will be read or re-run by other people.
Decision tree
- If the request is exploratory, analytical, or hypothesis-driven, choose
experiment. - If the request is instructional, step-by-step, or audience-specific, choose
tutorial. - If editing an existing notebook, treat it as a refactor: preserve intent and improve structure.
Skill path (set once)
export CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
export JUPYTER_NOTEBOOK_CLI="$CODEX_HOME/skills/jupyter-notebook/scripts/new_notebook.py"
User-scoped skills install under $CODEX_HOME/skills (default: ~/.codex/skills).
Workflow
-
Lock the intent. Identify the notebook kind:
experimentortutorial. Capture the objective, audience, and what "done" looks like. -
Scaffold from the template. Use the helper script to avoid hand-authoring raw notebook JSON.
uv run --python 3.12 python "$JUPYTER_NOTEBOOK_CLI" \
--kind experiment \
--title "Compare prompt variants" \
--out output/jupyter-notebook/compare-prompt-variants.ipynb
uv run --python 3.12 python "$JUPYTER_NOTEBOOK_CLI" \
--kind tutorial \
--title "Intro to embeddings" \
--out output/jupyter-notebook/intro-to-embeddings.ipynb
-
Fill the notebook with small, runnable steps. Keep each code cell focused on one step. Add short markdown cells that explain the purpose and expected result. Avoid large, noisy outputs when a short summary works.
-
Apply the right pattern. For experiments, follow
references/experiment-patterns.md. For tutorials, followreferences/tutorial-patterns.md. -
Edit safely when working with existing notebooks. Preserve the notebook structure; avoid reordering cells unless it improves the top-to-bottom story. Prefer targeted edits over full rewrites. If you must edit raw JSON, review
references/notebook-structure.mdfirst. -
Validate the result. Run the notebook top-to-bottom when the environment allows. If execution is not possible, say so explicitly and call out how to validate locally. Use the final pass checklist in
references/quality-checklist.md.
Templates and helper script
- Templates live in
assets/experiment-template.ipynbandassets/tutorial-template.ipynb. - The helper script loads a template, updates the title cell, and writes a notebook.
Script path:
$JUPYTER_NOTEBOOK_CLI(installed default:$CODEX_HOME/skills/jupyter-notebook/scripts/new_notebook.py)
Temp and output conventions
- Use
tmp/jupyter-notebook/for intermediate files; delete when done. - Write final artifacts under
output/jupyter-notebook/when working in this repo. - Use stable, descriptive filenames (for example,
ablation-temperature.ipynb).
Dependencies (install only when needed)
Prefer uv for dependency management.
Optional Python packages for local notebook execution:
uv pip install jupyterlab ipykernel
The bundled scaffold script uses only the Python standard library and does not require extra dependencies.
Environment
No required environment variables.
Reference map
references/experiment-patterns.md: experiment structure and heuristics.references/tutorial-patterns.md: tutorial structure and teaching flow.references/notebook-structure.md: notebook JSON shape and safe editing rules.references/quality-checklist.md: final validation checklist.
File metadata
name: skill.jupyter-notebook description: Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.
View original text
---
name: skill.jupyter-notebook
description: Use when the user asks to create, scaffold, or edit Jupyter notebooks
(`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates
and run the helper script `new_notebook.py` to generate a clean starting notebook.
---
# Jupyter Notebook Skill
Create clean, reproducible Jupyter notebooks for two primary modes:
- Experiments and exploratory analysis
- Tutorials and teaching-oriented walkthroughs
Prefer the bundled templates and the helper script for consistent structure and fewer JSON mistakes.
## When to use
- Create a new `.ipynb` notebook from scratch.
- Convert rough notes or scripts into a structured notebook.
- Refactor an existing notebook to be more reproducible and skimmable.
- Build experiments or tutorials that will be read or re-run by other people.
## Decision tree
- If the request is exploratory, analytical, or hypothesis-driven, choose `experiment`.
- If the request is instructional, step-by-step, or audience-specific, choose `tutorial`.
- If editing an existing notebook, treat it as a refactor: preserve intent and improve structure.
## Skill path (set once)
```bash
export CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
export JUPYTER_NOTEBOOK_CLI="$CODEX_HOME/skills/jupyter-notebook/scripts/new_notebook.py"
```
User-scoped skills install under `$CODEX_HOME/skills` (default: `~/.codex/skills`).
## Workflow
1. Lock the intent.
Identify the notebook kind: `experiment` or `tutorial`.
Capture the objective, audience, and what "done" looks like.
2. Scaffold from the template.
Use the helper script to avoid hand-authoring raw notebook JSON.
```bash
uv run --python 3.12 python "$JUPYTER_NOTEBOOK_CLI" \
--kind experiment \
--title "Compare prompt variants" \
--out output/jupyter-notebook/compare-prompt-variants.ipynb
```
```bash
uv run --python 3.12 python "$JUPYTER_NOTEBOOK_CLI" \
--kind tutorial \
--title "Intro to embeddings" \
--out output/jupyter-notebook/intro-to-embeddings.ipynb
```
3. Fill the notebook with small, runnable steps.
Keep each code cell focused on one step.
Add short markdown cells that explain the purpose and expected result.
Avoid large, noisy outputs when a short summary works.
4. Apply the right pattern.
For experiments, follow `references/experiment-patterns.md`.
For tutorials, follow `references/tutorial-patterns.md`.
5. Edit safely when working with existing notebooks.
Preserve the notebook structure; avoid reordering cells unless it improves the top-to-bottom story.
Prefer targeted edits over full rewrites.
If you must edit raw JSON, review `references/notebook-structure.md` first.
6. Validate the result.
Run the notebook top-to-bottom when the environment allows.
If execution is not possible, say so explicitly and call out how to validate locally.
Use the final pass checklist in `references/quality-checklist.md`.
## Templates and helper script
- Templates live in `assets/experiment-template.ipynb` and `assets/tutorial-template.ipynb`.
- The helper script loads a template, updates the title cell, and writes a notebook.
Script path:
- `$JUPYTER_NOTEBOOK_CLI` (installed default: `$CODEX_HOME/skills/jupyter-notebook/scripts/new_notebook.py`)
## Temp and output conventions
- Use `tmp/jupyter-notebook/` for intermediate files; delete when done.
- Write final artifacts under `output/jupyter-notebook/` when working in this repo.
- Use stable, descriptive filenames (for example, `ablation-temperature.ipynb`).
## Dependencies (install only when needed)
Prefer `uv` for dependency management.
Optional Python packages for local notebook execution:
```bash
uv pip install jupyterlab ipykernel
```
The bundled scaffold script uses only the Python standard library and does not require extra dependencies.
## Environment
No required environment variables.
## Reference map
- `references/experiment-patterns.md`: experiment structure and heuristics.
- `references/tutorial-patterns.md`: tutorial structure and teaching flow.
- `references/notebook-structure.md`: notebook JSON shape and safe editing rules.
- `references/quality-checklist.md`: final validation checklist.
Review the source
Price & running costs
- Get the skill
- Price unconfirmed
- Run it
- Requirements have not been confirmed. Check the source for agent, API and service charges.
- License
- AGPL-3.0
- Price unconfirmed
- We have not confirmed a price for this skill. Existing source and install links remain available.
Free to get does not mean free to run. Price labels are not safety ratings. Submit pricing information →
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: AGPL-3.0
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- AI review approval is missing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Stars/forks activity: 228 stars, 21 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Start with one small task
- 1Read the source. Confirm the input, expected output, dependencies and permissions.
- 2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
- 3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.
Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.
Source & usage notes
IndexedStatic Checked
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
- Source repository
- AI45Lab/OpenART
- License
- AGPL-3.0
- Version
- Unknown
- Last GitHub push
- Oct 3, 2026
- Registry updated
- Oct 3, 2026
- Instruction path
- openart-tools/skill.jupyter-notebook/SKILL.md @ 1f7e138b8eba
Version reported in registry metadata; check source releases before relying on it.
Quality
65/100
Promising
Trust
62/100
Sandbox only
Audit
75/100
Needs review
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- AI review approval is missing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Stars/forks activity: 228 stars, 21 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
- Verified installs
- —
- Outcomes
- —
Copies are not installs. Installation counts require a reported successful installation; they are not a blanket quality guarantee.
Agent access
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
More details
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"description": "Use when the user asks to create, scaffold, or edit Jupyter notebooks",
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"repoActivity": "228 stars, 21 forks",
"lastPushed": "Pushed today",
"license": "AGPL-3.0",
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"install": "npx skills add AI45Lab/OpenART --skill skill.jupyter-notebook",
"installSafety": "standard package or runtime install path",
"permissionSurface": "secrets or environment access, shell or command execution",
"documentation": "Usable metadata, review docs",
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"Stars/forks activity: 228 stars, 21 forks; issue activity unavailable in current metadata",
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"Quality score needs review",
"Permission surface needs review: secrets or environment access, shell or command execution",
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"minimum_review_before_use": [
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"Audit: 75/100 Needs review",
"Safety: 35/100 Avoid automatic install",
"Review repository, license, install command, and permission surface before production use."
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"expected_agent_output": {
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"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
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"install": "https://www.openagentskill.com/api/skills/ai45lab-skill-jupyter-notebook/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/ai45lab-skill-jupyter-notebook"
}
}For the creator
Listing source
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This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- AI45Lab
- Source
- AI45Lab/OpenART
- Indexed by
- OpenAgentSkill community index
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