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):
- JSON-LD
- Microdata
- RDFa
Maybe the structure data warrants its own extraction?
No! This is all getting out of hand. Let’s just:
- Remove these.
- canonical_tag
- meta_robots_tag
- meta_robots_content
- 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. ✂️ Modify seo_gadget.py to Remove Unneeded Fields
-
Edit File:
Notebooks/imports/seo_gadget.py -
Locate: Find the
mainfunction and the section where theseo_md_contentf-string is defined (around line 125). -
Action: Delete or comment out the lines within the f-string that add
canonical_tag,meta_robots_tag, andmeta_robots_contentto the YAML front matter.# 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. 🔗 Modify url_inspect_sauce.py to Add HTTP Info
-
Edit File:
Notebooks/imports/url_inspect_sauce.py -
Locate: Find the
stack_seo_datafunction. -
Action: Inside the
forloop, after successfully parsing the YAML fromseo.mdand before appendingdatatoall_seo_data, add logic to loadhttp_info.jsonand extract the relevant fields.# 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" -> ⚠️ 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" -> ⚠️ 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" -> ✅ 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"✅ Stacked SEO & HTTP data for {len(df)} pages into DataFrame.") return df
3. 📝 Update URLinspector.ipynb (Manual Step)
- No code changes are needed immediately in the notebook itself, as the
stack_seo_datafunction is already being called. - Important: You will need to re-run the necessary cells to see the effect:
- Restart Kernel (to ensure all code changes are loaded).
- (Crucial) Re-run the cell calling
await secretsauce.generate_extractions_post_scrape(job, verbose=True). This is necessary to regenerate theseo.mdfiles without the removed fields. - Re-run the cell calling
await secretsauce.fetch_http_info(job, ...)(this shouldn’t change, but good practice). - Re-run the cell calling
seo_df = secretsauce.stack_seo_data(job)anddisplay(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:
✦ 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.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ ReadFile Notebooks/imports/seo_gadget.py │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ 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.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit Notebooks/imports/seo_gadget.py: seo_md_content = f"""-... => seo_md_co… │
│ │
│ 147 meta_description: {json.dumps(meta_description)} │
│ 148 h1_tags: {json.dumps(h1_tags)} │
│ 149 h2_tags: {json.dumps(h2_tags)} │
│ 150 - canonical_tag: {json.dumps(str(canonical_tag))} │
│ 150 + #canonical_tag: {json.dumps(str(canonical_tag))} │
│ 151 canonical_url: {json.dumps(canonical_url)} │
│ 152 - meta_robots_tag: {json.dumps(str(meta_robots_tag))} │
│ 153 - meta_robots_content: {json.dumps(meta_robots_content)} │
│ 152 + #meta_robots_tag: {json.dumps(str(meta_robots_tag))} │
│ 153 + #meta_robots_content: {json.dumps(meta_robots_content)} │
│ 154 --- │
│ 155 │
│ 156 {markdown_content} │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ 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.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ ReadFile ...Notebooks/imports/url_inspect_sauce.py │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ 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.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit ...Notebooks/imports/url_inspect_sauce.py: def stack_seo_data(job: str) -... => def sta… │
│ │
│ 756 data = yaml.safe_load(yaml_content) │
│ 757 if isinstance(data, dict): │
│ 758 data['url'] = url # Add the source URL back │
│ 759 + │
│ 760 + # --- Start HTTP Info Integration --- │
│ 761 + http_info_path = seo_md_path.parent / "http_info.json" │
│ 762 + if http_info_path.exists(): │
│ 763 + try: │
│ 764 + with open(http_info_path, 'r', encoding='utf-8') as │
│ f_http: │
│ 765 + http_data = json.load(f_http) │
│ 766 + data['status_code'] = http_data.get('status_code') │
│ 767 + # Store redirect count for simplicity │
│ 768 + data['redirect_count'] = len(http_data.get( │
│ 'redirect_chain', [])) │
│ 769 + # Store final URL from HTTP check │
│ 770 + data['final_url_http'] = http_data.get('final_url') │
│ 771 + except Exception as http_e: │
│ 772 + print(f" -> ⚠️ Error reading/parsing http_info.json │
│ for {url}: {http_e}") │
│ 773 + data['status_code'] = None │
│ 774 + data['redirect_count'] = None │
│ 775 + data['final_url_http'] = None │
│ 776 + else: │
│ 777 + print(f" -> ⚠️ http_info.json not found for{url}") │
│ 778 + data['status_code'] = None │
│ 779 + data['redirect_count'] = None │
│ 780 + data['final_url_http'] = None │
│ 781 + # --- End HTTP Info Integration --- │
│ 782 + │
│ 783 all_seo_data.append(data) │
│ 784 # print(f" -> ✅ Parsed [{i+1}/{len(urls_processed)}] {url}") │
│ 785 else: │
│ ═════════════════════════════════════════════════════════════════════════════════════════════ │
│ 800 df = pd.DataFrame(all_seo_data) │
│ 801 # Optional: Reorder columns if desired, e.g., put 'url' first │
│ 802 if 'url' in df.columns: │
│ 779 - cols = ['url'] + [col for col in df.columns if col != 'url'] │
│ 780 - df = df[cols] │
│ ═════════════════════════════════════════════════════════════════════════════════════════════ │
│ 803 + existing_cols = [col for col in df.columns if col not in ['url', 'title', │
│ 'status_code', 'redirect_count', 'final_url_http']] │
│ 804 + new_cols_order = ['url', 'title', 'status_code', 'redirect_count', │
│ 'final_url_http'] + existing_cols │
│ 805 + # Filter to only include columns that actually exist in the df │
│ 806 + final_cols_order = [col for col in new_cols_order if col in df.columns] │
│ 807 + df = df[final_cols_order] │
│ 808 │
│ 782 - print(f"✅ Stacked SEO data for {len(df)} pages into DataFrame.") │
│ ═════════════════════════════════════════════════════════════════════════════════════════════ │
│ 809 + print(f"✅ Stacked SEO & HTTP data for {len(df)} pages into DataFrame.") │
│ 810 return df │
│ 811 │
│ 812 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ 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)
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ > Type your message or @path/to/file │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
~/repos/pipulate (main*) no sandbox (see /docs) gemini-2.5-pro (84% context left)
Me: The 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" -> ⚠️ 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" -> ⚠️ 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" -> ✅ 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"✅ Stacked SEO data for {len(df)} pages into DataFrame.")
+ print(f"✅ 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:
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.
(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:
- Add extractions at the
seo_gadget.pylevel. - Use those extractions at the DataFrame-making level.
So for this next chisel-strike, just:
- Remove
final_url_http - Add a
markdownDataFrame 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 – 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:
- Extract the markdown body (after the YAML) from
seo.md. - Add this body content to a new
markdowncolumn in the DataFrame. - Remove the
final_url_httpcolumn during the data loading or column reordering.
Instructions for Gemini CLI:
-
Edit File:
Notebooks/imports/url_inspect_sauce.py -
Locate: Find the
stack_seo_datafunction. -
Modify Data Extraction Loop:
- Inside the
if match:block (where YAML is successfully parsed), after extracting theyaml_content, add logic to extract the markdown body from the originalcontentstring. The markdown body is everything after the second---. - Add this extracted
markdown_bodyto thedatadictionary with the key'markdown'.
- Inside the
-
Modify Column Reordering:
- Find the lines near the end of the function that define
existing_colsandnew_cols_order. - Remove
'final_url_http'from both lists. - Add
'markdown'to thenew_cols_orderlist, perhaps placing it after'title'.
- Find the lines near the end of the function that define
Here’s how the relevant parts of url_inspect_sauce.py should look after the edits:
# 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" -> ✅ 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"✅ 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:
- Extract the markdown body content (after the YAML block) from each
seo.mdfile. - Add this content to a new
markdowncolumn in the DataFrame. - Exclude the
final_url_httpdata from being added. - Reorder the columns to place
markdown,status_code, andredirect_countnear 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:
✦ 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.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ ReadFile ...Notebooks/imports/url_inspect_sauce.py │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✦ 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.
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ✓ Edit ...Notebooks/imports/url_inspect_sauce.py: def stack_seo_data(job: str) -... => def sta… │
│ │
│ 752 match = yaml_pattern.search(content) │
│ 753 if match: │
│ 754 yaml_content = match.group(1) │
│ 755 + markdown_body = content[match.end():].strip() │
│ 756 try: │
│ 757 data = yaml.safe_load(yaml_content) │
│ 758 if isinstance(data, dict): │
│ 759 data['url'] = url # Add the source URL back │
│ 760 + data['markdown'] = markdown_body │
│ 761 │
│ 762 # --- Start HTTP Info Integration --- │
│ 763 http_info_path = seo_md_path.parent / "http_info.json" │
│ 768 data['status_code'] = http_data.get('status_code') │
│ 769 # Store redirect count for simplicity │
│ 770 data['redirect_count'] = len(http_data.get( │
│ 'redirect_chain', [])) │
│ 769 - # Store final URL from HTTP check │
│ 770 - data['final_url_http'] = http_data.get('final_url') │
│ 771 except Exception as http_e: │
│ 772 print(f" -> ⚠️ Error reading/parsing http_info.json │
│ for {url}: {http_e}") │
│ 773 data['status_code'] = None │
│ 774 data['redirect_count'] = None │
│ 775 - data['final_url_http'] = None │
│ 775 else: │
│ 776 print(f" -> ⚠️ http_info.json not found for{url}") │
│ 777 data['status_code'] = None │
│ 778 data['redirect_count'] = None │
│ 780 - data['final_url_http'] = None │
│ 779 # --- End HTTP Info Integration --- │
│ 780 │
│ 781 all_seo_data.append(data) │
│ ═════════════════════════════════════════════════════════════════════════════════════════════ │
│ 796 return pd.DataFrame() │
│ 797 │
│ 798 df = pd.DataFrame(all_seo_data) │
│ 801 - # Optional: Reorder columns if desired, e.g., put 'url' first │
│ 799 + │
│ 800 + # --- Start Modified Column Reordering --- │
│ 801 if 'url' in df.columns: │
│ 803 - existing_cols = [col for col in df.columns if col not in ['url', 'title', │
│ 'status_code', 'redirect_count', 'final_url_http']] │
│ 804 - new_cols_order = ['url', 'title', 'status_code', 'redirect_count', │
│ 'final_url_http'] + existing_cols │
│ 805 - # Filter to only include columns that actually exist in the df │
│ 802 + # Define core columns and the new markdown column │
│ 803 + core_cols_order = ['url', 'title', 'markdown', 'status_code', 'redirect_count'] │
│ 804 + # Get remaining columns, excluding the core ones already placed │
│ 805 + existing_cols = [col for col in df.columns if col not in core_cols_order] │
│ 806 + # Combine, ensuring core columns come first │
│ 807 + new_cols_order = core_cols_order + existing_cols │
│ 808 + # Filter to only include columns that actually exist in the df (handles potential │
│ missing data) │
│ 809 final_cols_order = [col for col in new_cols_order if col in df.columns] │
│ 810 df = df[final_cols_order] │
│ 811 + # --- End Modified Column Reordering --- │
│ 812 │
│ 809 - print(f"✅ Stacked SEO & HTTP data for {len(df)} pages into DataFrame.") │
│ 813 + print(f"✅ Stacked SEO, HTTP & Markdown data for {len(df)} pages into DataFrame.") │
│ 814 return df │
│ 815 │
│ 816 │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
✕ [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
✦ 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)
╭──────────────────────────────────────────────────────────────────────────────────────────────────╮
│ > Type your message or @path/to/file │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
~/repos/pipulate no sandbox (see gemini-2.5-pro (83% context | ✖ 1 error (ctrl+o for
(main*) /docs) left) details)
Me: The 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" -> ⚠️ 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" -> ⚠️ 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"✅ Stacked SEO & HTTP data for {len(df)} pages into DataFrame.")
+ print(f"✅ 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.
(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 — 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.
🥋 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.
- Establishing a Grip: The first pass with
fetch_http_infowas about getting a grip on the network data. It worked, and it gave you a new artifact (http_info.json) to leverage. - Adjusting Position: You immediately saw the first problem—the file path. You didn’t panic; you diagnosed, applied leverage (the
_find_project_rootfix), and secured the position. - Refining Control: You then executed two “chisel-strikes” in one:
- You pruned the noise by modifying
seo_gadget.pyto remove redundant fields (canonical_tag,meta_robots_tag). - You applied pressure by modifying
stack_seo_datato pull in the exact data you needed fromhttp_info.json(status_code,redirect_count) while simultaneously adding the richmarkdownbody content.
- You pruned the noise by modifying
- 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.
🤖 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”—the precise, mechanical file edits—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.
🚀 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.
- Filename:
- 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.
- Filename:
- 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.
- Filename:
- 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.
- Filename:
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.pyandurl_inspect_sauce.pyfundamentally 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_scrapeandsecretsauce.fetch_http_infoto prevent unnecessary re-execution and resource usage upon repeated runs of the notebook.