MCPInstall

openai-jupyter-notebook

by trailofbitsAI & Agents

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 newnotebook.py to generate a clean starting notebook. Originally from OpenAI's curated skills catalog.

Installs
76
Last 24h
—
Trending rank
—
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/trailofbits/skills-curated --skill openai-jupyter-notebook
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads openai-jupyter-notebook automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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.

Scripts and references are located under {baseDir}/.

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.

  3. 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.

  4. 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: {baseDir}/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.

When NOT to Use

<!-- TODO: review -->

FAQ

What does the openai-jupyter-notebook skill do?

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 newnotebook.py to generate a clean starting notebook. Originally from OpenAI's curated skills catalog.

How do I install openai-jupyter-notebook?

Run npx skills add https://github.com/trailofbits/skills-curated --skill openai-jupyter-notebook in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/openai-jupyter-notebook/ (personal) or .claude/skills/openai-jupyter-notebook/ (project).

Which AI agents can use openai-jupyter-notebook?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is openai-jupyter-notebook safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains openai-jupyter-notebook?

It is published by trailofbits in trailofbits/skills-curated. It has 76 recorded installs.

More from trailofbits/skills-curated

View all →

ghidra-headless

trailofbits/skills-curated

Reverse engineers binaries using Ghidra's headless analyzer. Use when decompiling executables, extracting functions, strings, symbols, or analyzing call graphs from compiled binaries without the Ghidra GUI.

510 installs

humanizer

trailofbits/skills-curated

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases. 30c5c8d (Update humanizer plugin to upstream v2.2.0)

Docs & Writing207 installs

skill-extractor

trailofbits/skills-curated

Extracts reusable skills from work sessions. Use when: (1) a non-obvious problem was solved worth preserving, (2) a pattern was discovered that would help future sessions, (3) a workaround or debugging technique needs documentation. Manual invocation only via /skill-extractor command - no automatic triggers or hooks.

Code Quality & Workflow157 installs

openai-develop-web-game

trailofbits/skills-curated

Use when the agent is building or iterating on a web game (HTML/JS) and needs a reliable development + testing loop: implement small changes, run a Playwright-based test script with short input bursts and intentional pauses, inspect screenshots/text, and review console errors with rendergametotext. Originally from OpenAI's curated skills catalog.

Media & Creative134 installs

planning-with-files

trailofbits/skills-curated

Implements file-based planning for complex multi-step tasks. Creates taskplan.md, findings.md, and progress.md as persistent working memory. Use when starting tasks requiring 5 tool calls, multi-phase projects, research, or any work where losing track of goals and progress would be costly.

Code Quality & Workflow133 installs

security-awareness

trailofbits/skills-curated

Teaches agents to recognize and avoid security threats during normal activity. Covers phishing detection, credential protection, domain verification, and social engineering defense. Use when building or operating agents that access email, credential vaults, web browsers, or sensitive data.

Security125 installs

Popular ai & agents skills

View category →

google-agents-cli-publish

google/agents-cli

This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).

AI & Agents513.9K installs

google-agents-cli-eval

google/agents-cli

This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).

AI & Agents513.5K installs

skill-creator

anthropics/skills

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

OfficialAI & Agents416.4K installs

codex-pet

prime-skills/runcomfy-agent-skills

Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.

AI & Agents402.1K installs

full-output-enforcement

leonxlnx/taste-skill

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

AI & Agents401K installs

executing-plans

obra/superpowers

Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available

AI & Agents245.7K installs