Publisher: OpenAI · Editorial profile by AI Tools Ratings · Review method
Not runtime tested; source-document review only. No security certification.
What task is this skill designed for?
Turn a small synthetic experiment into a notebook with an objective, a controlled comparison and clearly explained results.
What input and output should you expect?
Input: The experiment question, a synthetic dataset, the intended audience and a definition of a successful run.
Output: Expected, not independently tested: an .ipynb notebook with focused code cells, explanatory Markdown and a documented validation result.
What does it need to run?
A Codex environment able to write files and run Python. The source says the scaffold helper uses the Python standard library; executing the notebook may additionally need Jupyter, an appropriate kernel and task-specific libraries.
Author-provided Codex skill in OpenAI's skills repository; other agents have not been verified.
How should you approach installation?
- Inspect the pinned SKILL.md, helper script and experiment or tutorial template.
- Follow the repository's current Codex installation guidance for jupyter-notebook; installation has not been independently tested here.
- Create a synthetic example, review its code, and run from a fresh kernel only in an appropriate test environment.
Installation guide: discovery, dependencies and runtime checks
What files, accounts or data can it access?
Writes notebook and temporary files and can execute Python when permitted. Notebook cells can access the environment available to their kernel; review generated cells before running against private data or credentials.
What does the skill cost?
No skill purchase is listed. Agent runtime, compute and any paid data services are separate.
What are its limits and alternatives?
A valid notebook file is not proof that every cell runs. Hidden kernel state, unavailable datasets and package drift can break reproducibility. Validate from a fresh kernel with an explicit execution order.
For a fixed production job, a script with a defined interface is often easier to automate. Choose notebooks for exploration or explanations that benefit from adjacent code and narrative.
What could you ask in a small trial?
Try this with a small sample:
Create a tutorial notebook from this synthetic CSV. Explain each step, keep the data local, and clearly state whether you executed every cell in a fresh kernel.
How would you verify the result?
- Open the notebook and confirm all expected sections and cells exist.
- Run from a fresh kernel with the documented dependencies when execution is authorized.
- Compare computed values with a simple independent calculation.
Use a baseline and record acceptance evidence before treating a successful task as a reliable workflow.
Which source and license were checked?
Read the reviewed SKILL.md at its pinned commit · Complete reviewed folder
Repository and current publisher guidance
Checked:
Apache-2.0 — identified in skills/.curated/jupyter-notebook/LICENSE.txt at the reviewed commit. Third-party assets and dependencies may have separate terms. License evidence
Version details and review scope
Source: openai/skills/skills/.curated/jupyter-notebook
Reviewed commit: 49f948faa9258a0c61caceaf225e179651397431
SKILL.md SHA-256: 62f102e8554b25716dccef0ffab4572d4e3eaf05ccc76562d33a065bc9c521fb
Source-document review. Source text and stated dependencies were inspected for this profile. Referenced code was not comprehensively audited, no runtime test was performed, and source files are not mirrored here.