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.
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.
curl -O https://raw.githubusercontent.com/microsoft/ai-agents-for-beginners/main/.agents/skills/jupyter-notebook/SKILL.mdgit clone --depth 1 https://github.com/microsoft/ai-agents-for-beginnersWrote 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.
[](https://agentmods.dev/skills/microsoft/ai-agents-for-beginners/jupyter-notebook)<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.
<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>- Snyk pass
- NVIDIA SkillSpector warn
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.
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.
| Model | Per session | Once 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 |
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.
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.
Copies of this mod
6 near-identical copies found in the catalogue:
- jupyter-notebook — 100% identical, 0 lines differ
- jupyter-notebook — 100% identical, 0 lines differ
- jupyter-notebook — 100% identical, 0 lines differ
- jupyter-notebook — 100% identical, 0 lines differ
- jupyter-notebook — 94% identical, 15 lines differ
- jupyter-notebook — 86% identical, 22 lines differ
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
.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.
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 325 B
- assets/experiment-template.ipynb 2.5 KB
- assets/jupyter-small.svg 1.0 KB
- assets/jupyter.png 2.6 KB
- assets/tutorial-template.ipynb 2.4 KB
- LICENSE.txt 11 KB
- references/experiment-patterns.md 699 B
- references/notebook-structure.md 751 B
- references/quality-checklist.md 572 B
- references/tutorial-patterns.md 685 B
- scripts/new_notebook.py 4.0 KB runs code
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.
- 10d ago First seen · 108 lines · 57 tokens per session scan A 62f102e8554b
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.
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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.
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…