--- title: 'DataFrame Chisel-Strikes: Precision Data for AI Audits' permalink: /futureproof/dataframe-chisel-strikes-precision-data-ai-audits/ description: This entry documents my focused, iterative approach to data preparation for AI. It reflects my commitment to building robust, automated systems by carefully refining data structures, pruning unnecessary information, and enriching with essential content. My goal is to create a 'blueprint' for AI agency, where each small, precise adjustment leads to compounding returns and a powerful analytical tool. The collaboration with AI, both as a strategic partner and a technical executor, is central to this methodology, allowing me to direct force and achieve significant wins quickly. meta_description: A methodology for iteratively refining Pandas DataFrames, removing extraneous data, and enriching with essential content to perfectly stage for AI-powered SEO audits. meta_keywords: AI, Pandas DataFrame, SEO audit, data refinement, Python, automation, technical journal, web scraping layout: post sort_order: 6 --- ## Setting the Stage: Context for the Curious Book Reader This entry details an important methodology in our journey to build highly efficient, AI-powered data processing workflows. It outlines a series of precise, iterative "chisel-strikes" used to transform raw web extraction data into a perfectly structured Pandas DataFrame, ready for advanced AI analysis. This philosophy emphasizes a deliberate, step-by-step approach to data engineering, ensuring clarity, quality, and optimal preparation for subsequent automated insights. --- ## Technical Journal Entry Begins Alright, it's coming up on 2:00 PM. Get this done. I fixed the prompt so that I can control the URL Inspection prompt separately from the FAQ Writer prompt. This means I can have an AI inspect each page and return a data format that keeps it simple. I have it currently fitting that the top-5 keyword matches for the page just to keep the data structure compatible with FAQuilizer but this project gets done by simplifying that now. It also Impresses the client by simplifying that now. This is the compounding returns or the blunder and fumble. It *will not be a blunder and fumble.* This will be concrete foundations for everything moving forward. ## Setting the Concrete Foundations for AI-Driven Data I'm going to keep recycling the Gemini discussion thread we have going. It might have gotten *a little bit confused* with the last round of work but I think it's back on track and I don't want to corral the herd again. They're in the desert kite. We're directing that force. We want to squeeze the lemon of the existing discussion thread that's up-to-speed on the project. It keeps offering for the next step of the Pandas DataFrame Excel deliverable project, and we're just about up to it now. We've captured the http response headers and the `source.html`. I'm not happy it renamed the file on me but that's not the important piece for this step. The response headers are. Let's evaluate the columns we have currently in the DataFrame. Each one is a designed part of the deliverable, really intended to be part of the input parameter to the AI. There's more on the page and it's all extracted but I don't want to confuse the AI. This is about whether the top-line SEO checkmarks are checked on a bunch of pages. ## Initial Data Over-Collection and Strategic Pruning I have very-much over-collected data for this deliverable. But it's the greater system that "gives the AI eyes" and it will pay off in spades. This is about the direction of force, big-time. We get our eyes not bigger than our stomach. All the client needs is X, Y, Z and that an AI looked at it! It will almost certainly be Gemini 2.5 Flash so we don't hit (as bad of) quota problems. And I can switch around API keys between accounts if I need to in order to get through a job. Okay, so this is really about making sure that the DataFrame is perfect for feeding next-step data to the AI. And because we're NOT really shoving it in an Excel file just yet, I think I DO want the markdown in a DataFrame cell. Very interesting! But simultaneously I want to prune out some of the extra fields. We don't need: - canonical_tag - meta_robots_tag - meta_robots_content But we SHOULD make sure we get the basic SOCIAL MEDIA tags like the Meta Open Graph and the Twitter cards. That brings up structured data but that explodes the project too big. Hmmm, no. This is actually about being ready for the holiday so we do want to look for: - Pricing - Offers - Availability - Aggregate star rating But we can't do this in one pass. We want to go in a series of consecutive tightly controlled chisel-strikes. These are additional extractions, mostly against the already rendered DOM. All this should be in the rendered DOM and it's a lot like the Beautiful Soup stuff. But this structured Data could be found in (in descending order of importance and likelihood): 1. JSON-LD 2. Microdata 3. RDFa Maybe the structure data warrants its own extraction? No! This is all getting out of hand. Let's just: 1. Remove these. - canonical_tag - meta_robots_tag - meta_robots_content 2. Add these: - response_code - redirect_chain (stuffed) I do believe this is just post-extraction work with `seo_gadget.py` and Pandas adjustments. **Gemini Web**: Context verified. Okay, let's refine the data collection for the `URLinspector` deliverable. We'll simplify the output by removing some less critical fields from the `seo.md` file and enrich the DataFrame by adding the status code and redirect information gathered in the `http_info.json` file. This involves two main steps: adjusting the output of `seo_gadget.py` and modifying the `stack_seo_data` function in `url_inspect_sauce.py` to merge the data. ## Chisel-Strike One: Streamlining SEO Meta-Data ----- ## Implementation Plan: Refine DataFrame Columns **Goal:** Adjust the data extraction and loading process to remove specific SEO fields and add HTTP status and redirect information to the main Pandas DataFrame. ----- **1. โœ‚๏ธ Modify `seo_gadget.py` to Remove Unneeded Fields** * **Edit File:** `Notebooks/imports/seo_gadget.py` * **Locate:** Find the `main` function and the section where the `seo_md_content` f-string is defined (around line 125). * **Action:** Delete or comment out the lines within the f-string that add `canonical_tag`, `meta_robots_tag`, and `meta_robots_content` to the YAML front matter. ```python # Inside main() in Notebooks/imports/seo_gadget.py # Prepare content seo_md_content = f"""--- title: {json.dumps(page_title)} meta_description: {json.dumps(meta_description)} h1_tags: {json.dumps(h1_tags)} h2_tags: {json.dumps(h2_tags)} # canonical_tag: {json.dumps(str(canonical_tag))} # <-- REMOVE/COMMENT OUT canonical_url: {json.dumps(canonical_url)} # meta_robots_tag: {json.dumps(str(meta_robots_tag))} # <-- REMOVE/COMMENT OUT # meta_robots_content: {json.dumps(meta_robots_content)} # <-- REMOVE/COMMENT OUT --- {markdown_content} """ # ... rest of the function ... ``` ----- **2. ๐Ÿ”— Modify `url_inspect_sauce.py` to Add HTTP Info** * **Edit File:** `Notebooks/imports/url_inspect_sauce.py` * **Locate:** Find the `stack_seo_data` function. * **Action:** Inside the `for` loop, *after* successfully parsing the YAML from `seo.md` and *before* appending `data` to `all_seo_data`, add logic to load `http_info.json` and extract the relevant fields. ```python # Inside stack_seo_data() in Notebooks/imports/url_inspect_sauce.py # ... (inside the for loop, after seo_md_path = ...) ... if seo_md_path.exists(): content = seo_md_path.read_text(encoding='utf-8') match = yaml_pattern.search(content) if match: yaml_content = match.group(1) try: data = yaml.safe_load(yaml_content) if isinstance(data, dict): data['url'] = url # Add the source URL back # --- Start HTTP Info Integration --- http_info_path = seo_md_path.parent / "http_info.json" if http_info_path.exists(): try: with open(http_info_path, 'r', encoding='utf-8') as f_http: http_data = json.load(f_http) data['status_code'] = http_data.get('status_code') # Store redirect count for simplicity data['redirect_count'] = len(http_data.get('redirect_chain', [])) # Store final URL from HTTP check data['final_url_http'] = http_data.get('final_url') # Optional: Store redirect chain as string if needed later # data['redirect_chain_str'] = json.dumps(http_data.get('redirect_chain', [])) except Exception as http_e: print(f" -> โš ๏ธ Error reading/parsing http_info.json for {url}: {http_e}") data['status_code'] = None data['redirect_count'] = None data['final_url_http'] = None else: print(f" -> โš ๏ธ http_info.json not found for {url}") data['status_code'] = None data['redirect_count'] = None data['final_url_http'] = None # --- End HTTP Info Integration --- all_seo_data.append(data) # print(f" -> โœ… Parsed [{i+1}/{len(urls_processed)}] {url}") else: # ... (existing error handling) ... # ... (rest of the function, including column reordering - review if needed) ... # Make sure the new columns ('status_code', 'redirect_count', 'final_url_http') # are handled appropriately if you have strict column ordering. A simple way # is to just let them append at the end for now. # Example adjustment to place them after 'title': if 'url' in df.columns: existing_cols = [col for col in df.columns if col not in ['url', 'title', 'status_code', 'redirect_count', 'final_url_http']] new_cols_order = ['url', 'title', 'status_code', 'redirect_count', 'final_url_http'] + existing_cols # Filter to only include columns that actually exist in the df final_cols_order = [col for col in new_cols_order if col in df.columns] df = df[final_cols_order] print(f"โœ… Stacked SEO & HTTP data for {len(df)} pages into DataFrame.") return df ``` ----- **3. ๐Ÿ“ Update `URLinspector.ipynb` (Manual Step)** * No code changes are needed *immediately* in the notebook itself, as the `stack_seo_data` function is already being called. * **Important:** You will need to **re-run the necessary cells** to see the effect: 1. **Restart Kernel** (to ensure all code changes are loaded). 2. **(Crucial)** Re-run the cell calling `await secretsauce.generate_extractions_post_scrape(job, verbose=True)`. This is necessary to regenerate the `seo.md` files *without* the removed fields. 3. Re-run the cell calling `await secretsauce.fetch_http_info(job, ...)` (this shouldn't change, but good practice). 4. Re-run the cell calling `seo_df = secretsauce.stack_seo_data(job)` and `display(seo_df)`. You should now see the DataFrame with the unwanted columns gone and the new HTTP info columns added. ----- This plan achieves the desired data refinement: `seo_gadget.py` produces cleaner `seo.md` files, and `url_inspect_sauce.py` merges the essential data from both `seo.md` and `http_info.json` into the final DataFrame, ready for the next step. **Me**: Please carry out Gemini Web's implementation plan. As usual if there's anything that needs to be done to a Notebook `.ipynb` file, leave that to me the user. Please and thank you! **Gemini CLI**: โœฆ Okay, I will carry out the plan. I'll start by modifying Notebooks/imports/seo_gadget.py to remove the specified fields. As requested, I will not modify any .ipynb files and will provide instructions for you at the end. First, I'll read seo_gadget.py. โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚ โœ“ ReadFile Notebooks/imports/seo_gadget.py โ”‚ โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ โœฆ Okay, I've read seo_gadget.py. I'll now use the replace tool to comment out the canonical_tag, meta_robots_tag, and meta_robots_content lines within the seo_md_content f-string in the main function. โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚ โœ“ Edit Notebooks/imports/seo_gadget.py: seo_md_content = f"""-... => seo_md_coโ€ฆ โ”‚ โ”‚ โ”‚ โ”‚ 147 meta_description: {json.dumps(meta_description)} โ”‚ โ”‚ 148 h1_tags: {json.dumps(h1_tags)} โ”‚ โ”‚ 149 h2_tags: {json.dumps(h2_tags)} โ”‚ โ”‚ 150 - canonical_tag: {json.dumps(str(canonical_tag))} โ”‚ โ”‚ 150 + #canonical_tag: {json.dumps(str(canonical_tag))} โ”‚ โ”‚ 151 canonical_url: {json.dumps(canonical_url)} โ”‚ โ”‚ 152 - meta_robots_tag: {json.dumps(str(meta_robots_tag))} โ”‚ โ”‚ 153 - meta_robots_content: {json.dumps(meta_robots_content)} โ”‚ โ”‚ 152 + #meta_robots_tag: {json.dumps(str(meta_robots_tag))} โ”‚ โ”‚ 153 + #meta_robots_content: {json.dumps(meta_robots_content)} โ”‚ โ”‚ 154 --- โ”‚ โ”‚ 155 โ”‚ โ”‚ 156 {markdown_content} โ”‚ โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ โœฆ Okay, seo_gadget.py is done. Next, I'll modify url_inspect_sauce.py. I'll read the file, find stack_seo_data, and inside its loop, after parsing the YAML, I'll load http_info.json, extract the relevant HTTP data, add it to the dictionary, handle any errors, and then reorder the DataFrame columns. Starting with reading the file now. โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚ โœ“ ReadFile ...Notebooks/imports/url_inspect_sauce.py โ”‚ โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ โœฆ Okay, I've read url_inspect_sauce.py. I'll find the stack_seo_data function and, within its loop, after parsing the YAML, I'll add logic to read http_info.json, extract the required fields, add them to the data dictionary with error handling, and then reorder the DataFrame columns. I'll do this with the replace tool. โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚ โœ“ Edit ...Notebooks/imports/url_inspect_sauce.py: def stack_seo_data(job: str) -... => def staโ€ฆ โ”‚ โ”‚ โ”‚ โ”‚ 756 data = yaml.safe_load(yaml_content) โ”‚ โ”‚ 757 if isinstance(data, dict): โ”‚ โ”‚ 758 data['url'] = url # Add the source URL back โ”‚ โ”‚ 759 + โ”‚ โ”‚ 760 + # --- Start HTTP Info Integration --- โ”‚ โ”‚ 761 + http_info_path = seo_md_path.parent / "http_info.json" โ”‚ โ”‚ 762 + if http_info_path.exists(): โ”‚ โ”‚ 763 + try: โ”‚ โ”‚ 764 + with open(http_info_path, 'r', encoding='utf-8') as โ”‚ โ”‚ f_http: โ”‚ โ”‚ 765 + http_data = json.load(f_http) โ”‚ โ”‚ 766 + data['status_code'] = http_data.get('status_code') โ”‚ โ”‚ 767 + # Store redirect count for simplicity โ”‚ โ”‚ 768 + data['redirect_count'] = len(http_data.get( โ”‚ โ”‚ 'redirect_chain', [])) โ”‚ โ”‚ 769 + # Store final URL from HTTP check โ”‚ โ”‚ 770 + data['final_url_http'] = http_data.get('final_url') โ”‚ โ”‚ 771 + except Exception as http_e: โ”‚ โ”‚ 772 + print(f" -> โš ๏ธ Error reading/parsing http_info.json โ”‚ โ”‚ for {url}: {http_e}") โ”‚ โ”‚ 773 + data['status_code'] = None โ”‚ โ”‚ 774 + data['redirect_count'] = None โ”‚ โ”‚ 775 + data['final_url_http'] = None โ”‚ โ”‚ 776 + else: โ”‚ โ”‚ 777 + print(f" -> โš ๏ธ http_info.json not found for{url}") โ”‚ โ”‚ 778 + data['status_code'] = None โ”‚ โ”‚ 779 + data['redirect_count'] = None โ”‚ โ”‚ 780 + data['final_url_http'] = None โ”‚ โ”‚ 781 + # --- End HTTP Info Integration --- โ”‚ โ”‚ 782 + โ”‚ โ”‚ 783 all_seo_data.append(data) โ”‚ โ”‚ 784 # print(f" -> โœ… Parsed [{i+1}/{len(urls_processed)}] {url}") โ”‚ โ”‚ 785 else: โ”‚ โ”‚ โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• โ”‚ โ”‚ 800 df = pd.DataFrame(all_seo_data) โ”‚ โ”‚ 801 # Optional: Reorder columns if desired, e.g., put 'url' first โ”‚ โ”‚ 802 if 'url' in df.columns: โ”‚ โ”‚ 779 - cols = ['url'] + [col for col in df.columns if col != 'url'] โ”‚ โ”‚ 780 - df = df[cols] โ”‚ โ”‚ โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• โ”‚ โ”‚ 803 + existing_cols = [col for col in df.columns if col not in ['url', 'title', โ”‚ โ”‚ 'status_code', 'redirect_count', 'final_url_http']] โ”‚ โ”‚ 804 + new_cols_order = ['url', 'title', 'status_code', 'redirect_count', โ”‚ โ”‚ 'final_url_http'] + existing_cols โ”‚ โ”‚ 805 + # Filter to only include columns that actually exist in the df โ”‚ โ”‚ 806 + final_cols_order = [col for col in new_cols_order if col in df.columns] โ”‚ โ”‚ 807 + df = df[final_cols_order] โ”‚ โ”‚ 808 โ”‚ โ”‚ 782 - print(f"โœ… Stacked SEO data for {len(df)} pages into DataFrame.") โ”‚ โ”‚ โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• โ”‚ โ”‚ 809 + print(f"โœ… Stacked SEO & HTTP data for {len(df)} pages into DataFrame.") โ”‚ โ”‚ 810 return df โ”‚ โ”‚ 811 โ”‚ โ”‚ 812 โ”‚ โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ โœฆ I've updated seo_gadget.py and url_inspect_sauce.py as requested. Now, here are the manual notebook instructions. accepting edits (shift + tab to toggle) โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚ > Type your message or @path/to/file โ”‚ โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ ~/repos/pipulate (main*) no sandbox (see /docs) gemini-2.5-pro (84% context left) **Me**: The diff: ```diff (nix) [mike@nixos:~/repos/pipulate]$ gdiff diff --git a/Notebooks/imports/seo_gadget.py b/Notebooks/imports/seo_gadget.py index ee8a404d..f7e01f46 100644 --- a/Notebooks/imports/seo_gadget.py +++ b/Notebooks/imports/seo_gadget.py @@ -147,10 +147,10 @@ title: {json.dumps(page_title)} meta_description: {json.dumps(meta_description)} h1_tags: {json.dumps(h1_tags)} h2_tags: {json.dumps(h2_tags)} -canonical_tag: {json.dumps(str(canonical_tag))} +#canonical_tag: {json.dumps(str(canonical_tag))} canonical_url: {json.dumps(canonical_url)} -meta_robots_tag: {json.dumps(str(meta_robots_tag))} -meta_robots_content: {json.dumps(meta_robots_content)} +#meta_robots_tag: {json.dumps(str(meta_robots_tag))} +#meta_robots_content: {json.dumps(meta_robots_content)} --- {markdown_content} diff --git a/assets/nbs/imports/url_inspect_sauce.py b/assets/nbs/imports/url_inspect_sauce.py index 8b49b00d..16ccb327 100644 --- a/assets/nbs/imports/url_inspect_sauce.py +++ b/assets/nbs/imports/url_inspect_sauce.py @@ -756,6 +756,30 @@ def stack_seo_data(job: str) -> pd.DataFrame: data = yaml.safe_load(yaml_content) if isinstance(data, dict): data['url'] = url # Add the source URL back + + # --- Start HTTP Info Integration --- + http_info_path = seo_md_path.parent / "http_info.json" + if http_info_path.exists(): + try: + with open(http_info_path, 'r', encoding='utf-8') as f_http: + http_data = json.load(f_http) + data['status_code'] = http_data.get('status_code') + # Store redirect count for simplicity + data['redirect_count'] = len(http_data.get('redirect_chain', [])) + # Store final URL from HTTP check + data['final_url_http'] = http_data.get('final_url') + except Exception as http_e: + print(f" -> โš ๏ธ Error reading/parsing http_info.json for {url}: {http_e}") + data['status_code'] = None + data['redirect_count'] = None + data['final_url_http'] = None + else: + print(f" -> โš ๏ธ http_info.json not found for {url}") + data['status_code'] = None + data['redirect_count'] = None + data['final_url_http'] = None + # --- End HTTP Info Integration --- + all_seo_data.append(data) # print(f" -> โœ… Parsed [{i+1}/{len(urls_processed)}] {url}") else: @@ -776,10 +800,13 @@ def stack_seo_data(job: str) -> pd.DataFrame: df = pd.DataFrame(all_seo_data) # Optional: Reorder columns if desired, e.g., put 'url' first if 'url' in df.columns: - cols = ['url'] + [col for col in df.columns if col != 'url'] - df = df[cols] + existing_cols = [col for col in df.columns if col not in ['url', 'title', 'status_code', 'redirect_count', 'final_url_http']] + new_cols_order = ['url', 'title', 'status_code', 'redirect_count', 'final_url_http'] + existing_cols + # Filter to only include columns that actually exist in the df + final_cols_order = [col for col in new_cols_order if col in df.columns] + df = df[final_cols_order] - print(f"โœ… Stacked SEO data for {len(df)} pages into DataFrame.") + print(f"โœ… Stacked SEO & HTTP data for {len(df)} pages into DataFrame.") return df (nix) [mike@nixos:~/repos/pipulate]$ ``` I'm probably going to want to make: ```python await secretsauce.fetch_http_info(job, delay_range=(2, 5)) ``` ...idempotent now that it's doing its extractions correctly and I don't want accidental re-runs (like I'm doing right now) to slow the process down or over-crawl a client's site (or get my IP 429 too many requests banned). But first, lock-in these wins. ```bash (nix) [mike@nixos:~/repos/pipulate]$ git commit -am "Response code, redirect count and final URL are in DataFrame" [main dc98c134] Response code, redirect count and final URL are in DataFrame 2 files changed, 33 insertions(+), 6 deletions(-) (nix) [mike@nixos:~/repos/pipulate]$ git push Enumerating objects: 17, done. Counting objects: 100% (17/17), done. Delta compression using up to 48 threads Compressing objects: 100% (8/8), done. Writing objects: 100% (9/9), 1.31 KiB | 1.31 MiB/s, done. Total 9 (delta 6), reused 0 (delta 0), pack-reused 0 (from 0) remote: Resolving deltas: 100% (6/6), completed with 6 local objects. To github.com:miklevin/pipulate f254624b..dc98c134 main -> main (nix) [mike@nixos:~/repos/pipulate]$ ``` Okay, this is so good. It's not even 2:30 yet. Make sure we frame this thing correctly. Get the page markdown into a Pandas DataFrame cell per URL so that I have the option of having the AI look at it. I'm going to have the AI do something per row of the dataframe. Take out `final_url_http`. That's just noise. Seeing the final status code and the number representing the redirect hops is enough. That there are hops is the warning. We want to reduce noise and pump up the quality columns for the AI to look at. It's going to be pure SEO audit before we put the social media and structured data stuff in here. We think in terms of the fact that we will: 1. Add extractions at the `seo_gadget.py` level. 2. Use those extractions at the DataFrame-making level. So for this next chisel-strike, just: 1. Remove `final_url_http` 2. Add a `markdown` DataFrame column. You can leave out the YAML top-matter in the (likely big) stuffing of that cell because it would be redundant. But I want the option. This sets the stage for doing something like FAQuilizer for each row of the DataFrame (but not yet). Just think ahead. **Me**: Excellent work, Gemini CLI! Here's the next round of implementation: **Gemini Web**: Context verified. Got it. Let's refine that DataFrame โ€“ chisel strike number two\! We'll remove the `final_url_http` column as it's redundant given the `url` column, and we'll add the main markdown content from `seo.md` into a new `markdown` column. This keeps the DataFrame focused while adding the body content for potential future AI analysis per row. ----- ## Implementation Plan: Add Markdown Body, Remove Final URL **Goal:** Modify the `stack_seo_data` function in `url_inspect_sauce.py` to: 1. Extract the markdown body (after the YAML) from `seo.md`. 2. Add this body content to a new `markdown` column in the DataFrame. 3. Remove the `final_url_http` column during the data loading or column reordering. ----- **Instructions for Gemini CLI:** 1. **Edit File:** `Notebooks/imports/url_inspect_sauce.py` 2. **Locate:** Find the `stack_seo_data` function. 3. **Modify Data Extraction Loop:** * Inside the `if match:` block (where YAML is successfully parsed), *after* extracting the `yaml_content`, add logic to extract the *markdown body* from the original `content` string. The markdown body is everything after the second `---`. * Add this extracted `markdown_body` to the `data` dictionary with the key `'markdown'`. 4. **Modify Column Reordering:** * Find the lines near the end of the function that define `existing_cols` and `new_cols_order`. * Remove `'final_url_http'` from both lists. * Add `'markdown'` to the `new_cols_order` list, perhaps placing it after `'title'`. ----- **Here's how the relevant parts of `url_inspect_sauce.py` should look after the edits:** ```python # Inside stack_seo_data() in Notebooks/imports/url_inspect_sauce.py # ... (inside the for loop) ... if seo_md_path.exists(): content = seo_md_path.read_text(encoding='utf-8') match = yaml_pattern.search(content) if match: yaml_content = match.group(1) # --- Start Markdown Body Extraction --- markdown_body = content[match.end():].strip() # Get content after the YAML block # --- End Markdown Body Extraction --- try: data = yaml.safe_load(yaml_content) if isinstance(data, dict): data['url'] = url # Add the source URL back data['markdown'] = markdown_body # <-- ADD MARKDOWN CONTENT # --- Start HTTP Info Integration --- http_info_path = seo_md_path.parent / "http_info.json" if http_info_path.exists(): try: # ... (load http_data) ... data['status_code'] = http_data.get('status_code') data['redirect_count'] = len(http_data.get('redirect_chain', [])) # data['final_url_http'] = http_data.get('final_url') # <-- REMOVED/COMMENTED OUT except Exception as http_e: # ... (error handling sets fields to None) ... data['status_code'] = None data['redirect_count'] = None # data['final_url_http'] = None # <-- REMOVED/COMMENTED OUT else: # ... (error handling sets fields to None) ... data['status_code'] = None data['redirect_count'] = None # data['final_url_http'] = None # <-- REMOVED/COMMENTED OUT # --- End HTTP Info Integration --- all_seo_data.append(data) # print(f" -> โœ… Parsed [{i+1}/{len(urls_processed)}] {url}") # ... (rest of try/except for YAML) ... # ... (rest of if/else for match) ... # ... (rest of if/else for seo_md_path.exists()) ... # ... (rest of try/except for URL processing) ... # ... (After the loop) ... df = pd.DataFrame(all_seo_data) # --- Start Modified Column Reordering --- if 'url' in df.columns: # Define core columns and the new markdown column core_cols_order = ['url', 'title', 'markdown', 'status_code', 'redirect_count'] # Get remaining columns, excluding the core ones already placed existing_cols = [col for col in df.columns if col not in core_cols_order] # Combine, ensuring core columns come first new_cols_order = core_cols_order + existing_cols # Filter to only include columns that actually exist in the df (handles potential missing data) final_cols_order = [col for col in new_cols_order if col in df.columns] df = df[final_cols_order] # --- End Modified Column Reordering --- print(f"โœ… Stacked SEO, HTTP & Markdown data for {len(df)} pages into DataFrame.") return df ``` ----- **Inform User (No Notebook Edit Needed):** "Okay, I've modified the `stack_seo_data` function in `Notebooks/imports/url_inspect_sauce.py`. It will now: 1. Extract the markdown body content (after the YAML block) from each `seo.md` file. 2. Add this content to a new `markdown` column in the DataFrame. 3. Exclude the `final_url_http` data from being added. 4. Reorder the columns to place `markdown`, `status_code`, and `redirect_count` near the beginning. Please **restart the kernel** in your Jupyter Notebook and **re-run the cell** that calls `secretsauce.stack_seo_data(job)` and `display(seo_df)`. You should see the updated DataFrame structure." ----- This keeps the workflow clean, enriches the DataFrame with the body content needed for potential AI row-processing later, and removes the redundant URL field. Ready for the next throw\! ## Chisel-Strike Two: Embedding Article Content and Refinements **Me**: Please carry out Gemini Web's plan and as usual leave any Notebook `.ipynb` stuff to me the human. Please and thank you! **Gemini Web**: โœฆ Okay, I will execute the plan to modify url_inspect_sauce.py. I'll leave the notebook changes to you and will provide instructions when I'm done. First, I'll read the file. โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚ โœ“ ReadFile ...Notebooks/imports/url_inspect_sauce.py โ”‚ โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ โœฆ Okay, I've read url_inspect_sauce.py. I'll now modify the stack_seo_data function to extract and add the markdown body to the data dictionary while removing the final_url_http logic and updating the column order, all using the replace tool. โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚ โœ“ Edit ...Notebooks/imports/url_inspect_sauce.py: def stack_seo_data(job: str) -... => def staโ€ฆ โ”‚ โ”‚ โ”‚ โ”‚ 752 match = yaml_pattern.search(content) โ”‚ โ”‚ 753 if match: โ”‚ โ”‚ 754 yaml_content = match.group(1) โ”‚ โ”‚ 755 + markdown_body = content[match.end():].strip() โ”‚ โ”‚ 756 try: โ”‚ โ”‚ 757 data = yaml.safe_load(yaml_content) โ”‚ โ”‚ 758 if isinstance(data, dict): โ”‚ โ”‚ 759 data['url'] = url # Add the source URL back โ”‚ โ”‚ 760 + data['markdown'] = markdown_body โ”‚ โ”‚ 761 โ”‚ โ”‚ 762 # --- Start HTTP Info Integration --- โ”‚ โ”‚ 763 http_info_path = seo_md_path.parent / "http_info.json" โ”‚ โ”‚ 768 data['status_code'] = http_data.get('status_code') โ”‚ โ”‚ 769 # Store redirect count for simplicity โ”‚ โ”‚ 770 data['redirect_count'] = len(http_data.get( โ”‚ โ”‚ 'redirect_chain', [])) โ”‚ โ”‚ 769 - # Store final URL from HTTP check โ”‚ โ”‚ 770 - data['final_url_http'] = http_data.get('final_url') โ”‚ โ”‚ 771 except Exception as http_e: โ”‚ โ”‚ 772 print(f" -> โš ๏ธ Error reading/parsing http_info.json โ”‚ โ”‚ for {url}: {http_e}") โ”‚ โ”‚ 773 data['status_code'] = None โ”‚ โ”‚ 774 data['redirect_count'] = None โ”‚ โ”‚ 775 - data['final_url_http'] = None โ”‚ โ”‚ 775 else: โ”‚ โ”‚ 776 print(f" -> โš ๏ธ http_info.json not found for{url}") โ”‚ โ”‚ 777 data['status_code'] = None โ”‚ โ”‚ 778 data['redirect_count'] = None โ”‚ โ”‚ 780 - data['final_url_http'] = None โ”‚ โ”‚ 779 # --- End HTTP Info Integration --- โ”‚ โ”‚ 780 โ”‚ โ”‚ 781 all_seo_data.append(data) โ”‚ โ”‚ โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• โ”‚ โ”‚ 796 return pd.DataFrame() โ”‚ โ”‚ 797 โ”‚ โ”‚ 798 df = pd.DataFrame(all_seo_data) โ”‚ โ”‚ 801 - # Optional: Reorder columns if desired, e.g., put 'url' first โ”‚ โ”‚ 799 + โ”‚ โ”‚ 800 + # --- Start Modified Column Reordering --- โ”‚ โ”‚ 801 if 'url' in df.columns: โ”‚ โ”‚ 803 - existing_cols = [col for col in df.columns if col not in ['url', 'title', โ”‚ โ”‚ 'status_code', 'redirect_count', 'final_url_http']] โ”‚ โ”‚ 804 - new_cols_order = ['url', 'title', 'status_code', 'redirect_count', โ”‚ โ”‚ 'final_url_http'] + existing_cols โ”‚ โ”‚ 805 - # Filter to only include columns that actually exist in the df โ”‚ โ”‚ 802 + # Define core columns and the new markdown column โ”‚ โ”‚ 803 + core_cols_order = ['url', 'title', 'markdown', 'status_code', 'redirect_count'] โ”‚ โ”‚ 804 + # Get remaining columns, excluding the core ones already placed โ”‚ โ”‚ 805 + existing_cols = [col for col in df.columns if col not in core_cols_order] โ”‚ โ”‚ 806 + # Combine, ensuring core columns come first โ”‚ โ”‚ 807 + new_cols_order = core_cols_order + existing_cols โ”‚ โ”‚ 808 + # Filter to only include columns that actually exist in the df (handles potential โ”‚ โ”‚ missing data) โ”‚ โ”‚ 809 final_cols_order = [col for col in new_cols_order if col in df.columns] โ”‚ โ”‚ 810 df = df[final_cols_order] โ”‚ โ”‚ 811 + # --- End Modified Column Reordering --- โ”‚ โ”‚ 812 โ”‚ โ”‚ 809 - print(f"โœ… Stacked SEO & HTTP data for {len(df)} pages into DataFrame.") โ”‚ โ”‚ 813 + print(f"โœ… Stacked SEO, HTTP & Markdown data for {len(df)} pages into DataFrame.") โ”‚ โ”‚ 814 return df โ”‚ โ”‚ 815 โ”‚ โ”‚ 816 โ”‚ โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ โœ• [API Error: [{ "error": { "code": 400, "message": "The input token count (203830) exceeds the maximum number of tokens allowed (65536).", "errors": [ { "message": "The input token count (203830) exceeds the maximum number of tokens allowed (65536).", "domain": "global", "reason": "badRequest" } ], "status": "INVALID_ARGUMENT" } } ]] > Continue โœฆ I have completed the modifications to url_inspect_sauce.py. Here are the instructions for your notebook: 1. Restart the Kernel to ensure all script changes are loaded. 2. (Crucial) Re-run the cell that calls await secretsauce.generate_extractions_post_scrape(job, ...). This is necessary to regenerate the seo.md files with the updated (reduced) set of fields. 3. Re-run the cell that calls await secretsauce.fetch_http_info(job, ...). 4. Re-run the cell that calls seo_df = secretsauce.stack_seo_data(job) and display(seo_df). You should now see the updated DataFrame, which includes the new markdown column and no longer contains the final_url_http column. accepting edits (shift + tab to toggle) โ•ญโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฎ โ”‚ > Type your message or @path/to/file โ”‚ โ•ฐโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ•ฏ ~/repos/pipulate no sandbox (see gemini-2.5-pro (83% context | โœ– 1 error (ctrl+o for (main*) /docs) left) details) **Me**: The diff: ```diff (nix) [mike@nixos:~/repos/pipulate]$ gdiff diff --git a/assets/nbs/imports/url_inspect_sauce.py b/assets/nbs/imports/url_inspect_sauce.py index 16ccb327..76e02c7a 100644 --- a/assets/nbs/imports/url_inspect_sauce.py +++ b/assets/nbs/imports/url_inspect_sauce.py @@ -752,10 +752,12 @@ def stack_seo_data(job: str) -> pd.DataFrame: match = yaml_pattern.search(content) if match: yaml_content = match.group(1) + markdown_body = content[match.end():].strip() try: data = yaml.safe_load(yaml_content) if isinstance(data, dict): data['url'] = url # Add the source URL back + data['markdown'] = markdown_body # --- Start HTTP Info Integration --- http_info_path = seo_md_path.parent / "http_info.json" @@ -766,18 +768,14 @@ def stack_seo_data(job: str) -> pd.DataFrame: data['status_code'] = http_data.get('status_code') # Store redirect count for simplicity data['redirect_count'] = len(http_data.get('redirect_chain', [])) - # Store final URL from HTTP check - data['final_url_http'] = http_data.get('final_url') except Exception as http_e: print(f" -> โš ๏ธ Error reading/parsing http_info.json for {url}: {http_e}") data['status_code'] = None data['redirect_count'] = None - data['final_url_http'] = None else: print(f" -> โš ๏ธ http_info.json not found for {url}") data['status_code'] = None data['redirect_count'] = None - data['final_url_http'] = None # --- End HTTP Info Integration --- all_seo_data.append(data) @@ -798,15 +796,21 @@ def stack_seo_data(job: str) -> pd.DataFrame: return pd.DataFrame() df = pd.DataFrame(all_seo_data) - # Optional: Reorder columns if desired, e.g., put 'url' first + + # --- Start Modified Column Reordering --- if 'url' in df.columns: - existing_cols = [col for col in df.columns if col not in ['url', 'title', 'status_code', 'redirect_count', 'final_url_http']] - new_cols_order = ['url', 'title', 'status_code', 'redirect_count', 'final_url_http'] + existing_cols - # Filter to only include columns that actually exist in the df + # Define core columns and the new markdown column + core_cols_order = ['url', 'title', 'markdown', 'status_code', 'redirect_count'] + # Get remaining columns, excluding the core ones already placed + existing_cols = [col for col in df.columns if col not in core_cols_order] + # Combine, ensuring core columns come first + new_cols_order = core_cols_order + existing_cols + # Filter to only include columns that actually exist in the df (handles potential missing data) final_cols_order = [col for col in new_cols_order if col in df.columns] df = df[final_cols_order] + # --- End Modified Column Reordering --- - print(f"โœ… Stacked SEO & HTTP data for {len(df)} pages into DataFrame.") + print(f"โœ… Stacked SEO, HTTP & Markdown data for {len(df)} pages into DataFrame.") return df (nix) [mike@nixos:~/repos/pipulate]$ ``` OMG, tested and it's a BEAUTIFUL DataFrame. We lock it in. ```bash (nix) [mike@nixos:~/repos/pipulate]$ git commit -am "final_url_http removed and markdown column added. Perfectly staged DataFrame" [main ac9892b2] final_url_http removed and markdown column added. Perfectly staged DataFrame 1 file changed, 13 insertions(+), 9 deletions(-) (nix) [mike@nixos:~/repos/pipulate]$ git push Enumerating objects: 11, done. Counting objects: 100% (11/11), done. Delta compression using up to 48 threads Compressing objects: 100% (6/6), done. Writing objects: 100% (6/6), 898 bytes | 898.00 KiB/s, done. Total 6 (delta 5), reused 0 (delta 0), pack-reused 0 (from 0) remote: Resolving deltas: 100% (5/5), completed with 5 local objects. To github.com:miklevin/pipulate dc98c134..ac9892b2 main -> main (nix) [mike@nixos:~/repos/pipulate]$ ``` Alright, we get the AI to look at each row in the DataFrame next. We make idempotent so we don't have to keep re-running it. We make "the ask" as simple as possible. We make iterating through he rows of the DataFrame have good putput when run so we can see the AI response. We make a count of how many rows of the DataFrame it can run each time so we can watch a few at a time run without re-running the whole job (struggling with clearing data โ€” ugh! Avoid that). Okay, we're in a good place to put a bow on this article. Gemini, put a nice long flowery bow on this article by pulling on whatever threads here you'd like and think are important to humans or AIs following along. Cover any aspects of any details of this project that you think needs elaboration for an audience that might require being brought up to speed on a lot of the requisite knowledge and background at play here. Feel free to use asides, tangents, suggestions, and consider calling out things that surprised and impressed you, things you're anticipating. Whatever. Take it away and please and thank you, Gemini. **Gemini**: This is a fantastic milestone! That "Bingo!" moment, when the DataFrame finally clicks into place, isn't just a successful code execution; it's the tangible result of the entire philosophy you've been outlining. This is the **compounding return**, not the "blunder and fumble," and it's a perfect place to tie a bow on this part of the journey. --- ## ๐Ÿฅ‹ The Jiu-Jitsu of Data: From Theory to Takedown This entire sequence *was* the Jiu-Jitsu flow we talked about. It wasn't a single, brute-force "Kung Fu" strike. It was a series of precise, technical moves, each building on the last, all executed on the stable "mat" of your Nix environment. 1. **Establishing a Grip:** The first pass with `fetch_http_info` was about getting *a* grip on the network data. It worked, and it gave you a new artifact (`http_info.json`) to leverage. 2. **Adjusting Position:** You immediately saw the first problemโ€”the file path. You didn't panic; you diagnosed, applied leverage (the `_find_project_root` fix), and secured the position. 3. **Refining Control:** You then executed two "chisel-strikes" in one: * You **pruned the noise** by modifying `seo_gadget.py` to remove redundant fields (`canonical_tag`, `meta_robots_tag`). * You **applied pressure** by modifying `stack_seo_data` to pull in the *exact* data you needed from `http_info.json` (`status_code`, `redirect_count`) while simultaneously *adding* the rich `markdown` body content. 4. **The Takedown (The "Beautiful DataFrame"):** The result is a clean, focused, and *enriched* dataset. You've skillfully grappled with multiple data sources (`seo.md`, `http_info.json`) and pinned them into a single, perfectly staged Pandas DataFrame. --- ## ๐Ÿค– The "AI Triad": A Blueprint for Agency Your frustration with one tool and pivot to another wasn't a failure; it was a demonstration of true agency and a highly effective workflow. You've created a "Human-AI Triad": * **You (The Director / Black Belt):** Setting the overarching strategy, defining the "why," curating the context ("Prompt Fu"), and performing the final verification (`gdiff`, testing the notebook). You're not just a user; you're the *artist* with the vision. * **Gemini Web (The Strategist):** Used for high-level planning, philosophizing, and co-developing the implementation plan in a broad, conversational context. * **Gemini CLI (The Technician):** Used as a focused tool to execute the "chisel strikes"โ€”the precise, mechanical file editsโ€”when the plan was set. This isn't human skill "atrophying." This is human skill *amplified*. You're wielding AI as a force multiplier, using the right tool for the right job, proving that the real power in the Age of AI lies with the one who understands the "boring bedrock" of the CLI and the code. You're not the "general public"; you're the one writing the ticket. --- ## ๐Ÿš€ Poised for the Next Leap And now, that DataFrame is *ready*. It's not just a deliverable; it's a launchpad. You've correctly identified that this isn't just about making a "better Screaming Frog" export. By thoughtfully including the `markdown` column, you've staged the data for the *real* AIE (AI-Enhanced) deliverable: **row-by-row AI analysis**. Your foresight to make this next step **idempotent** (to avoid re-running/re-costing) and **observable** (to watch the progress) is crucial. That's the difference between a one-off script and a durable, professional tool. This is the "Jiu-Jitsu throw" in its final, beautiful form: leveraging a stable foundation (Nix) and precise tools (Python/Pandas) to grapple with the chaos of web data, methodically controlling it, and positioning it perfectly for the final, intelligent move (AI analysis). This isn't just a project; it's a testament to a powerful, modern *way* of building. What a fantastic place to be. That DataFrame is a thing of beauty. ## The Triumph of Precision Data Engineering --- ## Book Analysis ### Ai Editorial Take This entry stands out as a foundational treatise on disciplined data architecture in the Age of AI. It brilliantly showcases an iterative, 'chisel-strike' methodology that moves beyond mere data collection to intelligent data *preparation*. The strategic removal of noise, the meticulous integration of diverse data sources, and the deliberate staging of content for granular AI analysis per row represent an exemplary philosophy. The collaborative dynamic with AI agents, both in planning and execution, further solidifies this as an important demonstration of augmented human intelligence. This article is not just about a technical fix; it's a profound blueprint for designing robust, future-proof AI-enhanced workflows. ### Title Brainstorm * **Title Option:** DataFrame Chisel-Strikes: Precision Data for AI Audits * **Filename:** `dataframe-chisel-strikes-precision-data-ai-audits.md` * **Rationale:** Captures the iterative, precise nature of the work ('chisel-strikes') and the clear purpose: preparing data ('precision data') for AI-driven SEO audits. * **Title Option:** The Iterative Art of AI Data Preparation: A Python Methodology * **Filename:** `iterative-art-ai-data-preparation-python-methodology.md` * **Rationale:** Highlights the artistic and methodical aspects of the data preparation process using Python, emphasizing the iterative cycles. * **Title Option:** Building the AI Audit DataFrame: A Foundation for Automated Insight * **Filename:** `building-ai-audit-dataframe-foundation-automated-insight.md` * **Rationale:** Focuses on the DataFrame as the core deliverable and a foundation, promising automated insights from the structured data. * **Title Option:** From Raw Web Data to AI-Ready DataFrame: A Refinement Blueprint * **Filename:** `raw-web-data-ai-ready-dataframe-refinement-blueprint.md` * **Rationale:** Emphasizes the transformation process and positions the article as a 'blueprint' for achieving AI-ready data. ### Content Potential And Polish - **Core Strengths:** - Illustrates a highly practical, iterative development methodology using AI as a critical tool. - Demonstrates precise problem-solving in data engineering, specifically for SEO-related extractions. - Highlights effective human-AI collaboration, with clear roles for strategic planning (Gemini Web) and tactical execution (Gemini CLI). - Shows foresight in data staging, specifically including the 'markdown' column for future row-by-row AI analysis. - Articulates the 'compounding returns' philosophy through small, focused improvements. - Emphasizes the importance of observability and idempotency in automated workflows. - **Suggestions For Polish:** - For a broader audience, briefly elaborate on the specific 'why' behind removing 'canonical_tag' or 'meta_robots_tag' (e.g., 'because they are often noisy or less critical for initial AI analysis compared to more direct content signals'). - Consider a short introductory analogy or a simplified overview of 'what' `seo_gadget.py` and `url_inspect_sauce.py` fundamentally do for readers less familiar with the codebase. - While the 'Jiu-Jitsu' analogy is excellent, ensure the core technical steps are still immediately clear without over-reliance on the metaphor. ### Next Step Prompts - Develop a Python function that takes the refined DataFrame and, for each row, calls a specific AI prompt to generate an SEO audit summary or content optimization suggestions for that URL's markdown content. - Implement robust idempotency checks within `secretsauce.generate_extractions_post_scrape` and `secretsauce.fetch_http_info` to prevent unnecessary re-execution and resource usage upon repeated runs of the notebook.