# Amal Alexander - Complete Knowledge Catalog This plaintext file contains the full text catalog of all articles, projects, and guides from amalalexander.com. It is designed to allow LLM agents to download and reference everything in a single RAG context window. ========================================= BIO AND SUMMARY INFO ========================================= Amal Alexander is an SEO Architect, open-source developer, and AI agent researcher. - Web: https://amalalexander.com - GitHub: https://github.com/amal-alexander - Focus: Generative Engine Optimization (GEO/AEO), Headless Browser Agents, Model Context Protocol (MCP) servers. ========================================= SECTION 1: DETAILED TOOLS LISTING ========================================= --- Title: SEO Tech Audit Tool Category: SEO GitHub: https://github.com/amal-alexander/tech-audit Demo: https://tech-audit.streamlit.app The SEO Tech Audit Tool is a lightweight dashboard built on Streamlit that lets you run full domain crawls, validate internal links, and run Lighthouse audits. It outputs detailed tables and charts highlighting Core Web Vitals issues, broken links, and redirect loops. --- Title: Agentic Browsing Auditor Category: AI Agents GitHub: https://github.com/amal-alexander/agentic-browsing-auditor Demo: https://pypi.org/project/agentic-browsing-auditor/ The Agentic Browsing Auditor automates browser actions (clicking buttons, filling forms, selecting dropdowns) using an LLM to dynamically achieve auditing goals while logging visual screenshots, network calls, and speed indexes. --- Title: Search Console & Analytics MCP Server Category: MCP GitHub: https://github.com/amal-alexander/search-console-mcp Demo: N/A This MCP server links your database models and Google APIs directly into LLM developer environments. An agent can call tools like keyword clustering, analytics fetching, and security audit reports natively during conversation. ========================================= SECTION 2: COMPLETE BLOG POSTS ========================================= --- Title: How AI Visibility Tools Actually Collect Data: API vs UI Scraping Description: How AI visibility tools collect data: API-based collection vs real browser-based UI execution. A technical breakdown of their trade-offs. Published Date: 7/2/2026 Category: RESEARCH Tags: SCRAPING, AI AGENTS, DATA # API vs UI: The AI Visibility Battleground AI engines (like Perplexity or ChatGPT search) read webpages in different ways. Some utilize raw JSON API calls, while others spin up fully headless browsers to parse rendered layout states. Here is a technical breakdown of how visibility auditing tools capture these differences. --- Title: Best AEO & GEO Agencies in 2026: 30 Agencies Compared Description: All 30 AEO and GEO agencies compared side-by-side, evaluating their LLM citation performance, pricing, and optimization strategies. Published Date: 7/2/2026 Category: GUIDES Tags: AEO, GEO, LLM SEARCH # Comparing AEO and GEO Agencies Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) have taken center stage in 2026. We compared 30 agencies to see which one delivers the highest citation increase in Perplexity, Claude, and Gemini searches. --- Title: How to Track Your Brand Visibility in AI Search Description: Step-by-step guide to tracking brand visibility in AI search with specialized agents, evaluating citation shares and brand mentions. Published Date: 7/1/2026 Category: GUIDES Tags: AEO, BRAND TRACKING, SEO # Tracking Brand Visibility in AI Searches To measure how well your brand is represented in LLM outputs, you need custom agent architectures that query search endpoints and parse references. This guide breaks down the core pipeline for tracking citation shares. --- Title: I Crawled 65,000 Pages of My Own Site Without Parsing a Single Sitemap Description: Somewhere between talks on day one, I think it was during a hallway chat that I decided to run an aggressive crawl experiment using direct link scraping and BFS. Published Date: 5/4/2026 Category: RESEARCH Tags: SEO, CRAWLING, EXPERIMENT # The Crawling Experiment I wanted to see how efficient it is to crawl a dynamic domain of 65,000 pages without parsing a single sitemap file. By relying strictly on HTML parsing and breadth-first search (BFS) links extraction, we managed to audit the entire directory structure in record time. ## Key Findings 1. **Link Density**: Dynamic internal link systems provide 98% coverage compared to XML sitemaps. 2. **Speed Metrics**: A parallelized worker setup hit a consistent 80.95 requests per second (RPS). 3. **Orphan Pages**: We identified about 1,200 orphan pages that were present in search consoles but completely unlinked from the main layout. ```bash $ crawl --domain "amalalexander.com" --depth 5 --workers 10 [INFO] Crawl started... [STATS] Crawled 65,000 pages, rps=80.95, errors=12 ``` By removing the reliance on sitemaps, we audit what a search bot *actually* discovers by crawling link relationships. --- Title: How I Turned Meta AI's Brain Scanner Model Into a Free SEO Tool Description: I took Meta AI's TRIBE v2 model and turned it into an automated image-to-intent classifier, enabling highly precise visual SEO auditing. Published Date: 3/30/2026 Category: EXPERIMENT Tags: AI, SEO, META AI # Turning Brain Scans into SEO Signals Meta AI's brain-scanning research model parses visual triggers and links them to semantic intents. By adopting their weight matrices for web screenshots, we can classify what a user *feels* when landing on a page. ## How it works 1. **Screenshot Capture**: The agent snaps full-page viewports. 2. **Feature Extraction**: High-dimensional features are mapped through the visual encoder. 3. **Intent Categorization**: We translate the brain-scanner classification layer to standard commercial/transactional/informational intent buckets. This opens up a new paradigm of visual intent matching, ensuring images aren't just optimized for alt tags, but for cognitive relevance. --- Title: I Turned 16 Months of Google Search Console Data Into a Vector DB Description: I had a simple question: what if I could talk directly to my search metrics? Here is how I loaded 16 months of queries and clicks into ChromaDB. Published Date: 3/18/2026 Category: TOOLS Tags: VECTOR DB, SEARCH CONSOLE, LLM # Talking to Google Search Console Data Storing raw search keywords in a spreadsheet is old school. By embedding 16 months of search performance data (queries, impressions, CTR, positions) into a vector database, we can perform semantic querying and automated clustering. ## The Architecture - **Data Source**: Google Search Console API. - **Embeddings Model**: `text-embedding-3-small` (OpenAI). - **Vector Database**: ChromaDB (running locally). - **Agent Interface**: Streamlit + LangChain. Now, queries like *"What are my rising transactional intents that aren't ranking on page 1?"* return immediately with full context, bypassing complex Excel filters.