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/KIMISKI33/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/kimiski33/awesome-copilot/python-notebook-sample-builder)<a href="https://agentmods.dev/agents/kimiski33/awesome-copilot/python-notebook-sample-builder"><img src="https://agentmods.dev/badge/agents/kimiski33/awesome-copilot/python-notebook-sample-builder/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/agents/kimiski33/awesome-copilot/python-notebook-sample-builder"><img src="https://agentmods.dev/badge/agents/kimiski33/awesome-copilot/python-notebook-sample-builder.svg" alt="Reviewed on agentmods" width="80" 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 5.5 | $0.00008 | $0.00299 |
| Sonnet 5.5 | $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 1mo 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.
This is a copy
100% identical to Python Notebook Sample Builder — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 1mo ago First seen · 46 lines · 21 tokens per session scan A a2dd06852ba4
Python Notebook Sample Builder is an agent published in the GitHub repository KIMISKI33/awesome-copilot (1 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 747 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to Python Notebook Sample Builder, differing in 0 lines, and is treated as a copy.
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