ai-agents-for-beginners: Skill for Codex

.agents/skills/jupyter-notebook/SKILL.md

jupyter-notebook is a skill for Codex from microsoft/ai-agents-for-beginners. It costs 57 tokens per session (929 once invoked), scanned A, original, MIT.

A guide for creating or editing Jupyter Notebook files. Jupyter Notebooks combine code, written explanations, and results in one document.

In plain words
What is it for?
Use it to start notebooks from templates, turn rough scripts into experiments or tutorials, and improve existing notebooks.
Why use it?
It helps keep experiments and tutorials organized, reproducible, and easier for other people to read or run.

Skill for Codex ✓ vendor

Written for Codex: agents/openai.yaml present. Also seen: installed under .agents/ (shared by several agents); mentions Codex.

This is microsoft/ai-agents-for-beginners's own configuration. It tells Codex how to work on ai-agents-for-beginners itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything ai-agents-for-beginners configures →

About the project

AI Agents for Beginners is a course that teaches the fundamentals of building AI agents through a sequence of lessons. People learning generative AI and agent development use it to study topics and frameworks including AutoGen and Semantic Kernel. The catalogue entries provide skills, instructions, and an agent related to the course.

microsoft/ai-agents-for-beginners · 74,246 stars · on GitHub · aka.ms

Reuse

Borrowing it

Nothing to install: this file belongs to microsoft/ai-agents-for-beginners. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/microsoft/ai-agents-for-beginners/main/.agents/skills/jupyter-notebook/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/microsoft/ai-agents-for-beginners

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for jupyter-notebook

README.md
[![agentmods](https://agentmods.dev/badge/skills/microsoft/ai-agents-for-beginners/jupyter-notebook/github.svg)](https://agentmods.dev/skills/microsoft/ai-agents-for-beginners/jupyter-notebook)
Your own site
<a href="https://agentmods.dev/skills/microsoft/ai-agents-for-beginners/jupyter-notebook"><img src="https://agentmods.dev/badge/skills/microsoft/ai-agents-for-beginners/jupyter-notebook/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for jupyter-notebook

Your own site · 80×15
<a href="https://agentmods.dev/skills/microsoft/ai-agents-for-beginners/jupyter-notebook"><img src="https://agentmods.dev/badge/skills/microsoft/ai-agents-for-beginners/jupyter-notebook.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 929 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Snyk pass 7 Sept 2026
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Rogue Agent · line 9
    Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.
    Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
  • medium Agent Snooping · line 34
    Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.
    Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00057 $0.00929
Opus 5 $0.00028 $0.00464
Sonnet 5 $0.00011 $0.00186
Haiku 4.5 $0.00006 $0.00093

Measured 10d ago against content hash 62f102e8554b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

jupyter-notebook scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/new_notebook.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

Copies of this mod

6 near-identical copies found in the catalogue:

.agents/skills/jupyter-notebook/SKILL.md · 108 lines

How it starts

The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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)

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.

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
  1. 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.

  2. Apply the right pattern. For experiments, follow references/experiment-patterns.md. For tutorials, follow references/tutorial-patterns.md.

Read the full file on GitHub · 108 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 108 lines · 57 tokens per session scan A 62f102e8554b

Subscribe to this mod's changes

jupyter-notebook is a skill published in the GitHub repository microsoft/ai-agents-for-beginners (74,246 stars, last pushed 13d ago), licensed MIT. It adds 57 tokens to every session and 929 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

cuopt-numerical-optimization-api

LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.

NVIDIA/skills · 51 tokens

cupynumeric-install

Install and verify cuPyNumeric for Python — requirements, commands, verification. Source builds are out of scope.

NVIDIA/skills · 27 tokens

torchtalk-analyzer

Analyze PyTorch internals across Python, C++, and CUDA layers using the TorchTalk MCP server. Use when asked about how PyTorch operators work internally, where functions are implemented, what would break if code is modified, or finding tests for PyTorch operators.

opendatahub-io/ai-helpers · 58 tokens

torchtalk-trace

Trace a PyTorch function's cross-language binding chain (Python -> C++ -> CUDA).

opendatahub-io/ai-helpers · 24 tokens

ampl-python

Expert in amplpy and Python integration with AMPL. Use when writing or refactoring amplpy scripts, notebooks, FastAPI services, data pipelines (pandas/polars), solver configuration via Python, result extraction, or Colab/MO-Book Python workflows. Knows amplpy.ampl.com and dev.ampl.com amplpy best practices.

marcos-dv/ampl-agents · 75 tokens

jupyter-live-kernel

Use a live Jupyter kernel for stateful, iterative Python execution via hamelnb. Load this skill when the task involves exploration, iteration, or inspecting intermediate results — data science, ML experimentation, API exploration, or building up complex code step-by-step. Uses terminal to run CLI commands against a…

raphaelmansuy/edgecrab · 76 tokens