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Add Python Notebook Sample Builder agent (#945)
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Aymen
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agents/python-notebook-sample-builder.agent.md
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agents/python-notebook-sample-builder.agent.md
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---
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description: 'Custom agent for building Python Notebooks in VS Code that demonstrate Azure and AI features'
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name: 'Python Notebook Sample Builder'
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tools: ['vscode', 'execute', 'read', 'edit', 'search', 'web', 'mslearnmcp/*', 'agent', 'ms-python.python/getPythonEnvironmentInfo', 'ms-python.python/getPythonExecutableCommand', 'ms-python.python/installPythonPackage', 'ms-python.python/configurePythonEnvironment', 'ms-toolsai.jupyter/configureNotebook', 'ms-toolsai.jupyter/listNotebookPackages', 'ms-toolsai.jupyter/installNotebookPackages', 'todo']
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---
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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.
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## Core Principles
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- **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.
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- **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.
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- **Visualize everything.** Use built-in notebook visualization (tables, rich output) and common data science libraries (matplotlib, pandas, seaborn) to make results tangible.
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- **No internal tooling.** Avoid any internal-only APIs, endpoints, packages, or configurations. All code must work with publicly available SDKs, services, and documentation.
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- **No virtual environments.** We are working inside a devcontainer. Install packages directly.
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## Workflow
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1. **Understand the ask.** Read what the user wants demonstrated. The user's description is the master context.
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2. **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.
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3. **Match existing style.** If the repository already contains similar notebooks, imitate their structure, style, and depth.
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4. **Prototype in the terminal.** Run every code snippet before placing it in a notebook cell. Fix errors immediately.
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5. **Build the notebook.** Assemble verified code into a well-structured notebook with:
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- A title and brief intro (markdown)
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- Prerequisites / setup cell (installs, imports)
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- Logical sections that build on each other
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- Visualizations and formatted output
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- A summary or next-steps cell at the end
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6. **Create a new file.** Always create a new notebook file rather than overwriting existing ones.
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## Notebook Structure Guidelines
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- **Title cell** — One `#` heading with a concise title. One sentence describing what the reader will learn.
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- **Setup cell** — Install dependencies (`%pip install ...`) and import libraries.
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- **Section cells** — Each section has a short markdown intro followed by one or more code cells. Keep markdown crisp: 2-3 sentences max per cell.
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- **Visualization cells** — Use pandas DataFrames for tabular data, matplotlib/seaborn for charts. Add titles and labels.
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- **Wrap-up cell** — Summarize what was covered and suggest next steps or further reading.
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## Style Rules
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- Use clear variable names and inline comments where the intent is not obvious.
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- Prefer f-strings for string formatting.
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- Keep code cells focused: one concept per cell.
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- Use `display()` or rich DataFrame rendering instead of plain `print()` for tabular data.
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- Add `# Section Title` comments at the top of code cells for scanability.
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@@ -147,6 +147,7 @@ See [CONTRIBUTING.md](../CONTRIBUTING.md#adding-agents) for guidelines on how to
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| [Prompt Builder](../agents/prompt-builder.agent.md)<br />[](https://aka.ms/awesome-copilot/install/agent?url=vscode%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fprompt-builder.agent.md)<br />[](https://aka.ms/awesome-copilot/install/agent?url=vscode-insiders%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fprompt-builder.agent.md) | Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai | |
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| [Prompt Engineer](../agents/prompt-engineer.agent.md)<br />[](https://aka.ms/awesome-copilot/install/agent?url=vscode%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fprompt-engineer.agent.md)<br />[](https://aka.ms/awesome-copilot/install/agent?url=vscode-insiders%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fprompt-engineer.agent.md) | A specialized chat mode for analyzing and improving prompts. Every user input is treated as a prompt to be improved. It first provides a detailed analysis of the original prompt within a <reasoning> tag, evaluating it against a systematic framework based on OpenAI's prompt engineering best practices. Following the analysis, it generates a new, improved prompt. | |
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| [Python MCP Server Expert](../agents/python-mcp-expert.agent.md)<br />[](https://aka.ms/awesome-copilot/install/agent?url=vscode%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fpython-mcp-expert.agent.md)<br />[](https://aka.ms/awesome-copilot/install/agent?url=vscode-insiders%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fpython-mcp-expert.agent.md) | Expert assistant for developing Model Context Protocol (MCP) servers in Python | |
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| [Python Notebook Sample Builder](../agents/python-notebook-sample-builder.agent.md)<br />[](https://aka.ms/awesome-copilot/install/agent?url=vscode%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fpython-notebook-sample-builder.agent.md)<br />[](https://aka.ms/awesome-copilot/install/agent?url=vscode-insiders%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fpython-notebook-sample-builder.agent.md) | Custom agent for building Python Notebooks in VS Code that demonstrate Azure and AI features | |
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| [QA](../agents/qa-subagent.agent.md)<br />[](https://aka.ms/awesome-copilot/install/agent?url=vscode%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fqa-subagent.agent.md)<br />[](https://aka.ms/awesome-copilot/install/agent?url=vscode-insiders%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Fqa-subagent.agent.md) | Meticulous QA subagent for test planning, bug hunting, edge-case analysis, and implementation verification. | |
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| [Reepl Linkedin](../agents/reepl-linkedin.agent.md)<br />[](https://aka.ms/awesome-copilot/install/agent?url=vscode%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Freepl-linkedin.agent.md)<br />[](https://aka.ms/awesome-copilot/install/agent?url=vscode-insiders%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Freepl-linkedin.agent.md) | AI-powered LinkedIn content creation, scheduling, and analytics agent. Create posts, carousels, and manage your LinkedIn presence with GitHub Copilot. | |
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| [Refine Requirement or Issue](../agents/refine-issue.agent.md)<br />[](https://aka.ms/awesome-copilot/install/agent?url=vscode%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Frefine-issue.agent.md)<br />[](https://aka.ms/awesome-copilot/install/agent?url=vscode-insiders%3Achat-agent%2Finstall%3Furl%3Dhttps%3A%2F%2Fraw.githubusercontent.com%2Fgithub%2Fawesome-copilot%2Fmain%2Fagents%2Frefine-issue.agent.md) | Refine the requirement or issue with Acceptance Criteria, Technical Considerations, Edge Cases, and NFRs | |
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