--- title: Automated Jupyter Notebook Sanitization for Secure Templating permalink: /futureproof/automated-jupyter-notebook-data-scrubbing-secure-templating/ description: This entry captures a crucial moment in my development of `pipulate`, addressing the ever-present risk of client data leakage in version control. My challenge was to automate the sanitization of Jupyter notebooks, specifically targeting proprietary AI prompts and client-specific URLs within the `FAQuilizer` project, before they could be committed. The subsequent collaboration with Gemini provided a clear implementation plan, which I successfully integrated into the `nbup` method, ensuring secure templating while maintaining data integrity. meta_description: Pipulate's `nbup` method automates sensitive client data scrubbing from Jupyter raw cells, replacing it with sample content for secure, version-controlled notebook templates. meta_keywords: Jupyter Notebooks, Data Privacy, Data Scrubbing, Automated Editing, Pipulate, nbup, Security, Version Control, Notebook Templates, Raw Cells, AI Content Architect layout: post sort_order: 7 --- ## Setting the Stage: Context for the Curious Book Reader This journal entry delves into a critical aspect of software development, particularly for tools that handle sensitive client data: ensuring that proprietary information never accidentally leaks into version-controlled repositories. We explore a specific enhancement to `pipulate`'s `nbup` method, designed to automatically sanitize Jupyter notebooks by replacing client-specific prompts and URLs with safe, generic sample data before committing. This ensures robust data privacy while maintaining functional templates. --- ## Technical Journal Entry Begins Rabbit holes within rabbit holes within rabbit holes. Stop me before I subreference again, says Dennis Miller, one of the past SNL News anchors. We secure our rabbit hole rappelling rig once again to make sure this is not too deep. But we've got to do this. Client data cannot ever leak into the Pipulate repo and I am Icarus flying close to the sun with this stuff — always. And so we take a moment to take precautions. ## The Critical Need for Data Sanitization The FAQuilizer project uses a potentially very private and proprietary prompt for the AI but we want a default want to be filled-in shipping with Pipulate. Secret keepers can pass around prompts between them with emails and slack and stuff. And of course the list of URLs to be scraped is from the client site, so we want a nice default of Linux, Python, vim and git sites one should know to be filled in there as the example data. Here we craft our prompt. ## Understanding the FAQuilizer's Data Sensitivity **Me**: This is the contents of `assets/nbs/FAQuilizer.ipynb` after `pip.nbup("FAQuilizer.ipynb", modules=("secretsauce",))` is run. See, it synchronizes the `nbformat`-exported version that's not maintained under the git repo but lives in `Notebooks/` over to the version that is under the git repo. This way I can work in the live (accessible) `.ipynb` file and intermittently push the changes back to version control. Now notice how I filter certain things out. I now also need to filter out the entire contents of the raw cells labeled `prompt-input` and `url-list-input`. What's more, I actually need *edited in* the sample data found here. ```json { "cells": [ { "cell_type": "code", "execution_count": null, "id": "0", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "from pipulate import pip\n", "import secretsauce\n", "import nest_asyncio\n", "nest_asyncio.apply()\n", "\n", "job = \"faquilizer-12\" # Give your session a unique name" ] }, { "cell_type": "markdown", "id": "1", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "# This is the Prompt|" ] }, { "cell_type": "raw", "id": "2", "metadata": { "editable": true, "raw_mimetype": "", "slideshow": { "slide_type": "" }, "tags": [ "prompt-input" ] }, "source": [ "**Your Role (AI Content Strategist):**\n", "\n", "You are an AI Content Strategist. \n", "Make 5 Frequently Asked Questions for each page.\n", "For each question, produce the following so it fits the data structure:\n", "\n", "1. priority: integer (1-5, 1 is highest)\n", "2. question: string (The generated question)\n", "3. target_intent: string (What is the user's goal in asking this?)\n", "4. justification: string (Why is this a valuable question to answer? e.g., sales, seasonal, etc.)" ] }, { "cell_type": "markdown", "id": "3", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "## This is the List" ] }, { "cell_type": "raw", "id": "4", "metadata": { "editable": true, "raw_mimetype": "", "slideshow": { "slide_type": "" }, "tags": [ "url-list-input" ] }, "source": [ "https://nixos.org/ # Linux\n", "https://pypi.org/ # Python\n", "https://neovim.io/ # vim\n", "https://git-scm.com/ # git" ] }, { "cell_type": "markdown", "id": "5", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "### Run All the Cells" ] }, { "cell_type": "code", "execution_count": null, "id": "6", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "await secretsauce.scrape_and_extract(job, headless=False, verbose=False)" ] }, { "cell_type": "markdown", "id": "7", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "source": [ "#### You Get the Gist" ] }, { "cell_type": "code", "execution_count": null, "id": "8", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "pip.api_key(job, key=None)" ] }, { "cell_type": "code", "execution_count": null, "id": "9", "metadata": { "editable": true, "slideshow": { "slide_type": "" }, "tags": [] }, "outputs": [], "source": [ "secretsauce.generate_faqs(job)" ] }, { "cell_type": "code", "execution_count": null, "id": "10", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.11" } }, "nbformat": 4, "nbformat_minor": 5 } ``` So for example I first need whatever's in here: ```txt "source": [ "Proprietary ", "Secret ", "Stuff! " ] ``` ...to first be blanked using `nbformat`: ```txt "source": [ ] ``` ...and then to be filled in like so: ```txt "source": [ "**Your Role (AI Content Strategist):**\n", "\n", "You are an AI Content Strategist. \n", "Make 5 Frequently Asked Questions for each page.\n", "For each question, produce the following so it fits the data structure:\n", "\n", "1. priority: integer (1-5, 1 is highest)\n", "2. question: string (The generated question)\n", "3. target_intent: string (What is the user's goal in asking this?)\n", "4. justification: string (Why is this a valuable question to answer? e.g., sales, seasonal, etc.)" ] ``` And I need that done with the respective fields `prompt-input` and `url-list-input` so that the sample data found here is what ends up in the git repo and ***NEVER ACTUAL CLIENT DATA!!!*** Pretty clear, right? Can I have an implementation plan, please and thank you. ## Prompt Fu, Ka-POW! [And I generate the *Prompt Fu* payload] ```bash [mike@nixos:~/repos/pipulate]$ python prompt_foo.py -l --- Processing Files --- -> Converting notebook: /home/mike/repos/pipulate/Notebooks/FAQuilizer.ipynb --- Generating Auto-Context --- Generating codebase tree diagram... (2,128 tokens) Adding narrative context from articles... (5 articles) Python file(s) detected. Generating UML diagrams... -> Generating for /home/mike/repos/pipulate/pipulate/core.py... (2,216 tokens) -> Generating for /home/mike/repos/pipulate/Notebooks/secretsauce.py... (skipped) -> Generating for /home/mike/repos/pipulate/tools/scraper_tools.py... (skipped) -> Generating for /home/mike/repos/pipulate/tools/dom_tools.py... (431 tokens) ...UML generation complete. --- Files Included --- • /home/mike/repos/pipulate/assets/installer/install.sh (2,380 tokens) • /home/mike/repos/pipulate/requirements.in (254 tokens) • /home/mike/repos/pipulate/requirements.txt (8,115 tokens) • /home/mike/repos/pipulate/pyproject.toml (677 tokens) • /home/mike/repos/pipulate/flake.nix (7,217 tokens) • /home/mike/repos/pipulate/pipulate/core.py (20,473 tokens) • /home/mike/repos/pipulate/Notebooks/FAQuilizer.ipynb (557 tokens) • /home/mike/repos/pipulate/Notebooks/secretsauce.py (2,937 tokens) • /home/mike/repos/pipulate/tools/scraper_tools.py (1,630 tokens) • /home/mike/repos/pipulate/tools/dom_tools.py (3,333 tokens) --- Auto-Context Included --- • Codebase Structure (eza --tree) (2,128 tokens) • Recent Narrative Context (304 tokens) • UML Class Diagram (ASCII for /home/mike/repos/pipulate/pipulate/core.py) (2,216 tokens) • UML Class Diagram (ASCII for /home/mike/repos/pipulate/tools/dom_tools.py) (431 tokens) --- Prompt Summary --- Total Tokens: 54,642 Total Words: 21,920 --- Size Perspective --- 📚 Equivalent in length to a **Novella or a Master's Dissertation** (Note: With a token/word ratio of 2.49, this content is far denser and more complex than typical prose of this length). Markdown output copied to clipboard [mike@nixos:~/repos/pipulate]$ ``` ## Blueprinting the Solution: Gemini's Implementation Plan **Gemini**: Context verified. Of course. This is a clear and important requirement for maintaining a clean, secure separation between your working environment and your version-controlled templates. Preventing client data from accidentally being committed is crucial. Here is the implementation plan to modify the `pipulate.core.Pipulate.nbup` method to handle the replacement of proprietary data with sample data. ----- ## Implementation Plan The goal is to enhance the `nbup` method in `pipulate/core.py` to find specific tagged raw cells, clear their contents, and replace them with predefined sample data before saving the notebook to the `assets/nbs/` directory. ### 1\. Define Sample Data Constants We will add the sample data for the prompt and URL list as constants at the beginning of the `nbup` method. This makes the code cleaner and easier to update in the future. ```python SAMPLE_PROMPT_SOURCE = [ "**Your Role (AI Content Strategist):**\n", "\n", "You are an AI Content Strategist. \n", "Make 5 Frequently Asked Questions for each page.\n", "For each question, produce the following so it fits the data structure:\n", "\n", "1. priority: integer (1-5, 1 is highest)\n", "2. question: string (The generated question)\n", "3. target_intent: string (What is the user's goal in asking this?)\n", "4. justification: string (Why is this a valuable question to answer? e.g., sales, seasonal, etc.)" ] SAMPLE_URL_LIST_SOURCE = [ "https://nixos.org/ # Linux\n", "https://pypi.org/ # Python\n", "https://neovim.io/ # vim\n", "https://git-scm.com/ # git" ] ``` ### 2\. Modify the Cell Processing Loop We will iterate through each cell in the notebook. Before the existing logic that cleans code cells, we'll add a new block to specifically handle the tagged raw cells. ```python for cell in nb.cells: # Get the cell's tags, defaulting to an empty list tags = cell.metadata.get("tags", []) # Check for 'prompt-input' tag and replace content if "prompt-input" in tags: cell.source = SAMPLE_PROMPT_SOURCE print("✓ Scrubbed and replaced 'prompt-input' cell with sample data.") continue # Move to the next cell # Check for 'url-list-input' tag and replace content if "url-list-input" in tags: cell.source = SAMPLE_URL_LIST_SOURCE print("✓ Scrubbed and replaced 'url-list-input' cell with sample data.") continue # Move to the next cell # Existing logic for cleaning code cells if cell.cell_type == 'code': # ... (rest of the logic for key scrubbing, output clearing, etc.) ``` ### 3\. Integrate into `nbup` Method This new logic will be placed inside the `try` block where the notebook object (`nb`) is processed. The rest of the method's functionality, including pruning the `pip.nbup` cell, clearing code outputs, and syncing helper modules, will remain unchanged. ----- ## Updated `nbup` Method Here is the complete, updated code for the `pipulate.core.Pipulate.nbup` method in `pipulate/core.py`. You can replace the existing method with this new version. ## The Enhanced nbup Method in Action ```python def nbup(self, notebook_filename: str, modules: tuple = None): """ Cleans and syncs a notebook and optionally its associated Python modules from the working 'Notebooks/' directory back to the version-controlled 'assets/nbs/' template directory. """ # Import necessary libraries inside the function import nbformat from pathlib import Path import os import shutil # --- Define Sample Data for Scrubbing --- SAMPLE_PROMPT_SOURCE = [ "**Your Role (AI Content Strategist):**\n", "\n", "You are an AI Content Strategist. \n", "Make 5 Frequently Asked Questions for each page.\n", "For each question, produce the following so it fits the data structure:\n", "\n", "1. priority: integer (1-5, 1 is highest)\n", "2. question: string (The generated question)\n", "3. target_intent: string (What is the user's goal in asking this?)\n", "4. justification: string (Why is this a valuable question to answer? e.g., sales, seasonal, etc.)" ] SAMPLE_URL_LIST_SOURCE = [ "https://nixos.org/ # Linux\n", "https://pypi.org/ # Python\n", "https://neovim.io/ # vim\n", "https://git-scm.com/ # git" ] # 1. Find the project root in a portable way project_root = self._find_project_root(os.getcwd()) if not project_root: print("❌ Error: Could not find project root (flake.nix). Cannot sync.") return # --- Notebook Sync Logic --- print(f"🔄 Syncing notebook '{notebook_filename}'...") notebook_source_path = project_root / "Notebooks" / notebook_filename notebook_dest_path = project_root / "assets" / "nbs" / notebook_filename if not notebook_source_path.exists(): print(f"❌ Error: Source notebook not found at '{notebook_source_path}'") # Continue to module sync even if notebook is missing else: try: with open(notebook_source_path, 'r', encoding='utf-8') as f: nb = nbformat.read(f, as_version=4) # --- NEW: Scrub proprietary data and replace with samples --- for cell in nb.cells: tags = cell.metadata.get("tags", []) if "prompt-input" in tags: cell.source = SAMPLE_PROMPT_SOURCE print(" ✓ Scrubbed and replaced 'prompt-input' cell.") elif "url-list-input" in tags: cell.source = SAMPLE_URL_LIST_SOURCE print(" ✓ Scrubbed and replaced 'url-list-input' cell.") # --- Existing Cleaning Logic --- original_cell_count = len(nb.cells) pruned_cells = [ cell for cell in nb.cells if 'pip.nbup' not in cell.source ] if len(pruned_cells) < original_cell_count: print(" ✓ Auto-pruned the 'pip.nbup()' command cell from the template.") nb.cells = pruned_cells for cell in nb.cells: if cell.cell_type == 'code': # Scrub API key cell.source = re.sub( r'(key\s*=\s*)["\'].*?["\']', r'\1None', cell.source ) # Clear outputs and execution counts cell.outputs.clear() cell.execution_count = None if 'metadata' in cell and 'execution' in cell.metadata: del cell.metadata['execution'] with open(notebook_dest_path, 'w', encoding='utf-8') as f: nbformat.write(nb, f) print(f"✅ Success! Notebook '{notebook_filename}' has been cleaned and synced.") except Exception as e: print(f"❌ An error occurred during the notebook sync process: {e}") # --- Module Sync Logic --- if modules: print("\n--- Syncing Associated Modules ---") if isinstance(modules, str): modules = (modules,) for module_name in modules: module_filename = f"{module_name}.py" module_source_path = project_root / "Notebooks" / module_filename module_dest_path = project_root / "assets" / "nbs" / module_filename if module_source_path.exists(): try: shutil.copy2(module_source_path, module_dest_path) print(f" 🧬 Synced module: '{module_filename}'") except Exception as e: print(f" ❌ Error syncing module '{module_filename}': {e}") else: print(f" ⚠️ Warning: Module file not found, skipping sync: '{module_source_path}'") ``` **Me**: Watchdog restarted the server fine. How'd I do? ```diff [mike@nixos:~/repos/pipulate]$ git --no-pager diff diff --git a/pipulate/core.py b/pipulate/core.py index 963821d5..eb824517 100644 --- a/pipulate/core.py +++ b/pipulate/core.py @@ -1946,91 +1946,119 @@ class Pipulate: return None def nbup(self, notebook_filename: str, modules: tuple = None): - """ - Cleans and syncs a notebook and optionally its associated Python modules - from the working 'Notebooks/' directory back to the version-controlled - 'assets/nbs/' template directory. - """ - # Import necessary libraries inside the function - import nbformat - from pathlib import Path - import os - import shutil - - # 1. Find the project root in a portable way - project_root = self._find_project_root(os.getcwd()) - if not project_root: - print("❌ Error: Could not find project root (flake.nix). Cannot sync.") - return + """ + Cleans and syncs a notebook and optionally its associated Python modules + from the working 'Notebooks/' directory back to the version-controlled + 'assets/nbs/' template directory. + """ + # Import necessary libraries inside the function + import nbformat + from pathlib import Path + import os + import shutil + + # --- Define Sample Data for Scrubbing --- + SAMPLE_PROMPT_SOURCE = [ + "**Your Role (AI Content Strategist):**\n", + "\n", + "You are an AI Content Strategist. \n", + "Make 5 Frequently Asked Questions for each page.\n", + "For each question, produce the following so it fits the data structure:\n", + "\n", + "1. priority: integer (1-5, 1 is highest)\n", + "2. question: string (The generated question)\n", + "3. target_intent: string (What is the user's goal in asking this?)\n", + "4. justification: string (Why is this a valuable question to answer? e.g., sales, seasonal, etc.)" + ] + + SAMPLE_URL_LIST_SOURCE = [ + "https://nixos.org/ # Linux\n", + "https://pypi.org/ # Python\n", + "https://neovim.io/ # vim\n", + "https://git-scm.com/ # git" + ] + + # 1. Find the project root in a portable way + project_root = self._find_project_root(os.getcwd()) + if not project_root: + print("❌ Error: Could not find project root (flake.nix). Cannot sync.") + return - # --- Notebook Sync Logic --- - print(f"🔄 Syncing notebook '{notebook_filename}'...") - notebook_source_path = project_root / "Notebooks" / notebook_filename - notebook_dest_path = project_root / "assets" / "nbs" / notebook_filename + # --- Notebook Sync Logic --- + print(f"🔄 Syncing notebook '{notebook_filename}'...") + notebook_source_path = project_root / "Notebooks" / notebook_filename + notebook_dest_path = project_root / "assets" / "nbs" / notebook_filename - if not notebook_source_path.exists(): - print(f"❌ Error: Source notebook not found at '{notebook_source_path}'") - # Continue to module sync even if notebook is missing, in case that's all the user wants - else: - try: - with open(notebook_source_path, 'r', encoding='utf-8') as f: - nb = nbformat.read(f, as_version=4) - - for cell in nb.cells: - if cell.cell_type == 'code': - # This simpler, safer regex finds a 'key="...value..."' argument - # and replaces just the value part with None. - cell.source = re.sub( - r'(key\s*=\s*)["\'].*?["\']', - r'\1None', - cell.source - ) - - original_cell_count = len(nb.cells) - pruned_cells = [ - cell for cell in nb.cells if 'pip.nbup' not in cell.source - ] - - if len(pruned_cells) < original_cell_count: - print("✂️ Auto-pruned the 'pip.nbup()' command cell from the template.") - - nb.cells = pruned_cells - - for cell in nb.cells: - if cell.cell_type == 'code': - cell.outputs.clear() - cell.execution_count = None - if 'metadata' in cell and 'execution' in cell.metadata: - del cell.metadata['execution'] - - with open(notebook_dest_path, 'w', encoding='utf-8') as f: - nbformat.write(nb, f) - - print(f"✅ Success! Notebook '{notebook_filename}' has been cleaned and synced.") + if not notebook_source_path.exists(): + print(f"❌ Error: Source notebook not found at '{notebook_source_path}'") + # Continue to module sync even if notebook is missing + else: + try: + with open(notebook_source_path, 'r', encoding='utf-8') as f: + nb = nbformat.read(f, as_version=4) + + # --- NEW: Scrub proprietary data and replace with samples --- + for cell in nb.cells: + tags = cell.metadata.get("tags", []) + if "prompt-input" in tags: + cell.source = SAMPLE_PROMPT_SOURCE + print(" ✓ Scrubbed and replaced 'prompt-input' cell.") + elif "url-list-input" in tags: + cell.source = SAMPLE_URL_LIST_SOURCE + print(" ✓ Scrubbed and replaced 'url-list-input' cell.") + + # --- Existing Cleaning Logic --- + original_cell_count = len(nb.cells) + pruned_cells = [ + cell for cell in nb.cells if 'pip.nbup' not in cell.source + ] + + if len(pruned_cells) < original_cell_count: + print(" ✓ Auto-pruned the 'pip.nbup()' command cell from the template.") + + nb.cells = pruned_cells + + for cell in nb.cells: + if cell.cell_type == 'code': + # Scrub API key + cell.source = re.sub( + r'(key\s*=\s*)["\'].*?["\']', + r'\1None', + cell.source + ) + # Clear outputs and execution counts + cell.outputs.clear() + cell.execution_count = None + if 'metadata' in cell and 'execution' in cell.metadata: + del cell.metadata['execution'] + + with open(notebook_dest_path, 'w', encoding='utf-8') as f: + nbformat.write(nb, f) + + print(f"✅ Success! Notebook '{notebook_filename}' has been cleaned and synced.") - except Exception as e: - print(f"❌ An error occurred during the notebook sync process: {e}") - - # --- NEW: Module Sync Logic --- - if modules: - print("\n--- Syncing Associated Modules ---") - # Ensure modules is iterable even if a single string is passed - if isinstance(modules, str): - modules = (modules,) - - for module_name in modules: - module_filename = f"{module_name}.py" - module_source_path = project_root / "Notebooks" / module_filename - module_dest_path = project_root / "assets" / "nbs" / module_filename - - if module_source_path.exists(): - try: - shutil.copy2(module_source_path, module_dest_path) - print(f"🧬 Synced module: '{module_filename}'") - except Exception as e: - print(f"❌ Error syncing module '{module_filename}': {e}") - else: - print(f"⚠️ Warning: Module file not found, skipping sync: '{module_source_path}'") + except Exception as e: + print(f"❌ An error occurred during the notebook sync process: {e}") + + # --- Module Sync Logic --- + if modules: + print("\n--- Syncing Associated Modules ---") + if isinstance(modules, str): + modules = (modules,) + + for module_name in modules: + module_filename = f"{module_name}.py" + module_source_path = project_root / "Notebooks" / module_filename + module_dest_path = project_root / "assets" / "nbs" / module_filename + + if module_source_path.exists(): + try: + shutil.copy2(module_source_path, module_dest_path) + print(f" 🧬 Synced module: '{module_filename}'") + except Exception as e: + print(f" ❌ Error syncing module '{module_filename}': {e}") + else: + print(f" ⚠️ Warning: Module file not found, skipping sync: '{module_source_path}'") def api_key(self, job: str, service: str = 'google', key: str = None): """ [mike@nixos:~/repos/pipulate]$ ``` **Gemini**: You nailed it! That's a perfect implementation based on the plan. 💯 ## Code Review and Validation ### Code Review * **Constants:** Defining `SAMPLE_PROMPT_SOURCE` and `SAMPLE_URL_LIST_SOURCE` at the top is clean and makes future edits easy. * **Tag-Based Logic:** You're correctly iterating through the cells, checking the `metadata.tags`, and replacing the `source` for the `prompt-input` and `url-list-input` cells. This is exactly what was needed. * **Integration:** The new scrubbing logic is placed perfectly within the `try` block, ensuring it runs before the other cleaning steps without interfering with them. * **Feedback:** The `print` statements you added are a great touch. They provide clear, real-time feedback to the user running the `nbup` command, confirming that the scrubbing process worked as expected. The code is correct, clean, and effectively solves the problem of preventing client data from being committed to your version-controlled templates. Great job! --- ## Book Analysis ### Ai Editorial Take This journal entry represents a highly practical and essential contribution to secure software development practices, particularly in an AI-driven environment. Its strength lies in clearly articulating a common data privacy challenge and providing a precise, verifiable automated solution. This makes it an invaluable reference for developers looking to implement robust data sanitization in their CI/CD pipelines or template management, showcasing how AI can directly enhance code security and developer workflows. ### Title Brainstorm * **Title Option:** Automated Jupyter Notebook Sanitization for Secure Templating * **Filename:** `automated-jupyter-notebook-sanitization-secure-templating.md` * **Rationale:** Clearly states the core action, tool, and benefit. Emphasizes both automation and security, making it highly descriptive and appealing to developers focused on secure workflows. * **Title Option:** Preventing Data Leaks: `nbup` for Jupyter Notebook Security * **Filename:** `preventing-data-leaks-jupyter-notebook-security.md` * **Rationale:** Highlights the critical problem being solved (data leaks) and specifically names the `nbup` method, which is central to the solution. It's direct and problem-solution oriented. * **Title Option:** Pipulate's `nbup`: Automated Client Data Scrubbing in Notebooks * **Filename:** `pipulates-nbup-automated-client-data-scrubbing.md` * **Rationale:** Focuses on the specific project (`Pipulate`) and the mechanism (`nbup`), while detailing the function (data scrubbing). This title is precise and informative for those familiar with the project. * **Title Option:** From Proprietary to Public: Sanitizing Jupyter Prompts and URLs * **Filename:** `from-proprietary-to-public-jupyter-sanitization.md` * **Rationale:** A catchy title that outlines the transformation of data from sensitive to safe, specifically mentioning prompts and URLs, which are the key elements targeted for scrubbing. ### Content Potential And Polish - **Core Strengths:** - Addresses a critical real-world software development security concern regarding data leakage in version control. - Demonstrates a clear problem-solving methodology, moving from problem identification to seeking a solution, implementation, and verification. - Showcases effective and practical AI-human collaboration for complex technical tasks. - Provides concrete, executable code modifications and a detailed explanation of the changes. - Clearly explains the 'why' behind the technical changes, emphasizing security and privacy. - Uses a code diff to effectively verify the implemented solution. - **Suggestions For Polish:** - While implicit, a brief, explicit definition of `pip.nbup()` earlier in the article could benefit readers less familiar with `pipulate`'s ecosystem. - Consider adding a small, abstract example of what the 'proprietary' data *would* look like before scrubbing (e.g., generic placeholders), to further emphasize the transformation for a general audience without compromising security. - Discuss potential edge cases or future enhancements for `nbup`'s scrubbing capabilities, such as handling other cell types or more complex data structures through advanced regex patterns. - A simple diagram illustrating the data flow from `Notebooks/` to `assets/nbs/` with the scrubbing step could visually reinforce the process. ### Next Step Prompts - Draft a comprehensive unit test suite for the `nbup` method's new data scrubbing functionality, ensuring all tagged cells are correctly identified and replaced with the specified sample data. - Develop a detailed documentation update for the `pipulate` project, explaining the enhanced `nbup` method's data sanitization features, configuration options, and usage guidelines for developers, including best practices for tagging sensitive cells.