Skill: Jupyter Notebook Writing

Write Milvus application-level Jupyter notebook examples as a DevRel workflow. Uses a Markdown-first approach — AI edits .md files, then converts to .ipynb via jupyter-switch.

Prerequisites: Python >= 3.10, uv (uvx command available)

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When to Use

The user wants to create or edit a Jupyter notebook example, typically demonstrating Milvus usage in an application context (RAG, semantic search, hybrid search, etc.).

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Core Workflow: Markdown-First Editing

Jupyter .ipynb files contain complex JSON with metadata, outputs, and execution counts — painful for AI to edit directly. Instead:

1. Write/Edit the .md file — AI works with clean Markdown
2. Convert to .ipynb — using jupyter-switch for runnable notebook
3. Keep both files in sync — the .md is the source of truth for editing

Format Convention

In the .md file:

• Python code blocks (```python ... ```) become code cells in the notebook
• Everything else becomes markdown cells
• Cell outputs are not preserved in .md (they get generated when running the notebook)

Conversion Commands

# Markdown -> Jupyter Notebook
uvx jupyter-switch example.md
# produces example.ipynb

# Jupyter Notebook -> Markdown
uvx jupyter-switch example.ipynb
# produces example.md

• The original input file is never modified or deleted
• If the output file already exists, a .bak backup is created automatically

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Step-by-Step

Creating a New Notebook

1. Create example.md with the content (see structure below)
2. Convert: uvx jupyter-switch example.md
3. Both example.md and example.ipynb now exist

Editing an Existing Notebook

1. If only .ipynb exists, convert first: uvx jupyter-switch example.ipynb
2. Edit the .md file
3. Convert back: uvx jupyter-switch example.md

Testing / Running

1. Resolve the Jupyter execution environment

Before running any notebook, you must determine which Python environment to use. The system default jupyter execute may not have the required packages installed.

Step A — Check for saved preference. Look in the current project's CLAUDE.local.md for a ## Jupyter Notebook Execution section. If it contains a kernel name, use it directly and skip to Step 2.

Step B — If no saved preference, ask the user. Detect available environments first:

# Discover conda/mamba environments
conda env list 2>/dev/null || mamba env list 2>/dev/null

# Discover registered Jupyter kernels
jupyter kernelspec list 2>/dev/null

# Check system default Python
which python3 2>/dev/null && python3 --version 2>/dev/null

# Check for local virtual environment in the working directory
ls -d .venv/ venv/ 2>/dev/null

# Check if a uv-managed project (pyproject.toml + .venv)
test -f pyproject.toml && test -d .venv && echo "uv/pip project venv detected"

Then present a numbered list of choices. Include all detected environments:

1. System default — run jupyter execute as-is, no --kernel_name
2. Each detected conda/mamba environment — show name and path
3. Each registered Jupyter kernel — show kernel name
4. Local venv (if .venv/ or venv/ found in working directory) — the Python inside that venv
5. Custom — let the user type a Python path or environment name

Note on uv projects: If the working directory has pyproject.toml + .venv/ (a uv-managed project), the local venv option covers this case. The user can also run uv run jupyter execute example.ipynb directly if jupyter is a project dependency.

For every option, also offer a "remember" variant. Example prompt:

Which Python environment should I use to run this notebook?

1. System default (jupyter execute as-is)
2. conda: myenv (/path/to/envs/myenv)
3. Jupyter kernel: some-kernel
4. Local venv (.venv/)
5. Custom — enter a path or environment name

Tip: add "remember" to save your choice (e.g. "2, remember"),
so it gets written to CLAUDE.local.md and I won't ask next time.

Step C — Apply the chosen environment:

Scenario	Action
Already a registered Jupyter kernel	Use jupyter execute --kernel_name=<name>
Conda env not yet registered as kernel	Register first: <env-python> -m ipykernel install --user --name <name> --display-name "<label>", then use --kernel_name=<name>
Custom Python path	Same as above — register as kernel first, then use --kernel_name

Step D — If the user chose "remember": append or update a ## Jupyter Notebook Execution section in the current project's CLAUDE.local.md:

## Jupyter Notebook Execution

- **Jupyter kernel**: `<kernel-name>`

This ensures future runs skip the prompt and use the saved kernel directly.

2. Prepare the notebook for execution

Before running, comment out "setup-only" cells in the .md file — cells that are meant for first-time users but should not run in an automated test environment. Specifically:

• pip install cells — dependencies should already be installed in the chosen Jupyter environment. If any packages are missing or need upgrading, install them externally in the target environment (with --upgrade), not inside the notebook.
• API key / credential placeholder cells — e.g. os.environ["OPENAI_API_KEY"] = "sk-***********". Instead, set environment variables externally before running (export in shell, or inject via code before jupyter execute).
• Mock / demo-only cells — any cells that exist purely for illustration and would fail or interfere in a real run.

To comment out a cell, wrap its content in a block comment so the cell still executes (producing empty output) but does nothing:

# # pip install --upgrade langchain pymilvus
# import os
# os.environ["OPENAI_API_KEY"] = "sk-***********"

This keeps the notebook structure intact (cell count, ordering) while preventing conflicts with the external Jupyter environment.

For environment variables: either export them in the shell before running jupyter execute, or prepend them to the command:

OPENAI_API_KEY="sk-real-key" jupyter execute --kernel_name=<name> example.ipynb

3. Convert and run

1. Convert .md to .ipynb if needed
2. Install any missing dependencies in the target environment externally: <env-python> -m pip install --upgrade <packages>
3. Run: jupyter execute --kernel_name=<name> example.ipynb (omit --kernel_name if using system default)
4. If errors found, fix in the .md file, uncomment setup cells if needed for debugging, and re-convert

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Notebook Structure Template

A typical Milvus example notebook follows this structure:

# Title

Brief description of what this notebook demonstrates.

## Prerequisites

Install dependencies:

` ``python
!pip install pymilvus some-other-package
` ``

## Setup

Import and configuration:

` ``python
from pymilvus import MilvusClient

client = MilvusClient(uri="http://localhost:19530")
` ``

## Prepare Data

Load or generate example data:

` ``python
# data preparation code
` ``

## Create Collection & Insert Data

` ``python
# collection creation and data insertion
` ``

## Query / Search

` ``python
# search or query examples
` ``

## Cleanup

` ``python
client.drop_collection("example_collection")
` ``

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Reference Documents

This skill includes two reference documents under references/. Read them when the task involves their topics.

Reference	When to Read	File
Bootcamp Format	Writing a Milvus integration tutorial (badges, document structure, section format, example layout)	references/bootcamp-format.md
Milvus Code Style	Writing pymilvus code (collection creation, MilvusClient connection args, schema patterns, best practices)	references/milvus-code-style.md

Bootcamp Format (references/bootcamp-format.md)

Read this when the user is writing a Milvus integration tutorial for the bootcamp repository. It covers:

• Badge format (Colab + GitHub badges at the top)
• Document structure: Header -> Prerequisites -> Main Content -> Conclusion
• Dependency install format with Google Colab restart note
• API key placeholder conventions ("sk-***********")
• Each code block should have a short text introduction before it

Milvus Code Style (references/milvus-code-style.md)

Read this when the notebook involves pymilvus code. Key rules:

• Always use MilvusClient API — never use the legacy ORM layer (connections.connect(), Collection(), FieldSchema(), etc.)
• Always define schema explicitly (create_schema + add_field) — do not use the shortcut create_collection(dimension=...) without schema
• Include has_collection check before creating collections
• Add commented consistency_level="Strong" line in create_collection()
• No need to call load_collection() — collections auto-load on creation
• First MilvusClient connection must include the blockquote explaining uri options (Milvus Lite / Docker / Zilliz Cloud)

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Important Notes

• Always edit the .md file, not the .ipynb directly. The .md is easier for AI to read and write.
• Keep both files — .md for editing, .ipynb for running/sharing.
• After editing .md, always re-run uvx jupyter-switch example.md to sync the .ipynb.