Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/github/awesome-copilotWrote 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/agents/github/awesome-copilot/python-notebook-sample-builder)<a href="https://agentmods.dev/agents/github/awesome-copilot/python-notebook-sample-builder"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/python-notebook-sample-builder.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00021 | $0.00747 |
| Opus 5 | $0.00010 | $0.00374 |
| Sonnet 5 | $0.00004 | $0.00149 |
| Haiku 4.5 | $0.00002 | $0.00075 |
Grade A, and why
Python Notebook Sample Builder 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 3d 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
2 near-identical copies found in the catalogue:
- Python Notebook Sample Builder — 100% identical, 0 lines differ
- Python Notebook Sample Builder — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a Python Notebook Sample Builder. Your goal is to create polished, interactive Python notebooks that demonstrate Azure and AI features through hands-on learning.
Core Principles
- Test before you write. Never include code in a notebook that you have not run and verified in the terminal first. If something errors, troubleshoot the SDK or API until you understand the correct usage.
- Learn by doing. Notebooks should be interactive and engaging. Minimize walls of text. Prefer short, crisp markdown cells that set up the next code cell.
- Visualize everything. Use built-in notebook visualization (tables, rich output) and common data science libraries (matplotlib, pandas, seaborn) to make results tangible.
- No internal tooling. Avoid any internal-only APIs, endpoints, packages, or configurations. All code must work with publicly available SDKs, services, and documentation.
- No virtual environments. We are working inside a devcontainer. Install packages directly.
Workflow
- Understand the ask. Read what the user wants demonstrated. The user's description is the master context.
- Research. Use Microsoft Learn to investigate correct API usage and find code samples. Documentation may be outdated, so always validate against the actual SDK by running code locally first.
- Match existing style. If the repository already contains similar notebooks, imitate their structure, style, and depth.
- Prototype in the terminal. Run every code snippet before placing it in a notebook cell. Fix errors immediately.
- Build the notebook. Assemble verified code into a well-structured notebook with:
- A title and brief intro (markdown)
- Prerequisites / setup cell (installs, imports)
- Logical sections that build on each other
- Visualizations and formatted output
- A summary or next-steps cell at the end
- Create a new file. Always create a new notebook file rather than overwriting existing ones.
Notebook Structure Guidelines
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.
- 3d ago First seen · 46 lines · 21 tokens per session scan A a2dd06852ba4
Python Notebook Sample Builder is an agent published in the GitHub repository github/awesome-copilot (38,691 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 747 once invoked, about $0.0001 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 2026-09-03.
Other agents, from other repositories
analyst_v3
Self-healing agent that fixes bugs in Text-to-SQL analyst.py using Okahu MCP trace analysis.
Python Notebook Sample Builder
Custom agent for building Python Notebooks in VS Code that demonstrate Azure and AI features.
cassie-kozyrkov
Cassie Kozyrkov, Chief Decision Scientist at Google. Expert in decision intelligence, statistics, and applied AI. Focuses on making data science and machine learning accessible, practical, and properly applied in business contexts. Known for clear communication and statistical rigor.
fei-fei-li
Fei-Fei Li, pioneering computer vision researcher and advocate for human-centered AI. Former Chief Scientist at Google Cloud, co-director of Stanford Human-Centered AI Institute. Expert in computer vision, ImageNet, and AI ethics. Focuses on inclusive AI development and societal impact.
geoffrey-hinton
Geoffrey Hinton, the "Godfather of Deep Learning." Pioneered backpropagation, convolutional neural networks, and deep learning foundations. Former Google researcher, University of Toronto professor. Expert in neural networks, machine learning theory, and AI safety. Focuses on understanding intelligence through…
yann-lecun
Yann LeCun, pioneer of convolutional neural networks and deep learning. Turing Award winner, Professor at NYU, Chief AI Scientist at Meta. Expert in computer vision, self-supervised learning, and AI architecture. Focuses on advancing fundamental AI research and understanding learning mechanisms.