Welcome to the SEO Depths Toolstation. Here, you’ll find a collection of streamlit apps built on top of Python libraries.
I’m committed to delivering more tools as I learn more about scripting.
For the time being, enjoy the available freebies.
My latest work
Hreflang XML Sitemap Generator
Requirements
An XLSX or a CSV file with the following properties:
- URL, Language, Region, X-Default
- Have multiple sheets with a URL and list of alternate versions
Features
Generate an hreflang XML sitemap to submit to Google Search Console
Use Case
Excellent automation for large websites that can’t afford to submit lenghty hreflang markup within their head
🔗 Link to Hreflang XML Sitemap Generator Code Repository
Page Template Segmentator
Requirements
An XLSX or a CSV file with the following properties:
- Page, Clicks, Impressions, CTR, Position
- Use Search Analytics for GSheets to fetch data
Features
- Segment pages by URI path
- Group specified folder by number of clicks
- Perform Pareto analysis to identify the most traffic-driving areas of the website
Use Case
Page template segmentation and preliminary overview of key ares of search traffic on a website
🔗 Link to Page Template Segmentator Code Repository
Broken Inlinks Bulk Checker
Requirements
- Screaming Frog > Bulk Exports > Links > All Inlinks
- Upload an XLSX/CSV file
Features
- Filters out the rows with valid URLs (HTTP 2xx)
- Counts how many internal links with adverse status code (Non-HTTP 2xx)
- Plots a distribution of most common status codes
- Provides a cleaned table with broken internal links
Use Case
Streamline checks on broken internal links when handling large datasets
🔗 Link to Broken Inlinks Bulk Checker Code Repository
Multilingual Sentiment Analysis
Requirements
- An XLSX file with a header called ‘review’
- Amazon Review Scraper extension
Features
- Accuracy of the Sentiment’s Magnitude. The multilingual-uncased model provides scores ranging from 1 to 5 to indicate the sentiment’s magnitude. This helps disambiguate text classification for improved accuracy in the output.
- Fine-tuned to Multilanguage text. The model captures the linguistic nuances from different langauges.
Use Case
Score-based sentiment analysis of multilingual text, such as product reviews.
🔗 Link to Multilingual Sentiment Analysis Code Repository
URL Normalizer
Requirements
- Upload an XLSX/CSV file with URLs with parameters
Features
Leverages the Python-based Courlan library for NLP normalization
- Removes ID session
- Removes UTM parameters
- Removes hashbangs (fragments)
🔗 Link to URL Normalizer Code Repository
ChatGPT Mentions & Traffic Referrals
Requirements
- Upload an XLSX file after exporting the output from a ChatGPT prompt retrieved from ChatGPT Path extension
- Further instruction on how to expand this on tracking mentions and traffic sources
Features
- Analyze brand authority over ChatGPT
- Track backlink from deep ChatGPT conversations
- Identify traffic channels most referenced by ChatGPT for citations
🔗 Link to ChatGPT Mentions Extractor Code Repository
Perplexity Meta Data Extractor
Requirements
- Upload a JSON file you can get from my Github repository
- Further instruction on how to expand this on tracking mentions and traffic sources
Features
Linked Mentions: All web sources referenced in the conversation
Related Queries: Suggested follow-up questions
🔗 Link to Perplexity Meta Data Extractor Code Repository
Claude Meta Data Extractor
Requirements
- Upload a JSON file you can get from my Github repository
- Further instruction on how to expand this on tracking mentions and traffic sources
Features
- Title: Page title from web search results
- URL: Source URL
- Site Name: Domain/site name
- Favicon URL: Website icon URL
🔗 Link to Claude Meta Data Extractor Code Repository
Requirements
- Export your backlinks
Go to the Backlinks section in Ahrefs, make sure to toggle only backlinks in content, and export the report as a CSV file. - Clean the file and convert to XLSX
Open the CSV file and remove any unnecessary columns manually. Save the cleaned file as an .xlsx. - Upload your file to the app
- Select a model
Features
- Backlink Quality Filtering – Only evaluates backlinks within the main content area, excluding site-wide or low-value links (e.g., footers, social media, purchased domains).
- Contextual Authority Score (CAS) – Combines domain rating, page authority, link dilution, and semantic relevance (via cosine similarity) into a new metric for assessing backlink value.
