Koray Tugberk GUBUR's 48 AI Agents: The Complete Semantic SEO Toolkit
Koray Tugberk GUBUR has created an extensive collection of 48 AI agents—custom ChatGPT tools designed for semantic SEO professionals.
📊 Quick Stats: 48 AI Agents | 9 Categories | All Free with ChatGPT Plus
📌 Overview: The 9 Categories
These agents cover linguistic analysis, semantic extraction, entity mapping, topicality, sentiment processing, SEO auditing, data analysis, summarization, and technical generators.
📝 1. Linguistic & Syntactic Analysis (8 Agents)
These agents analyze text at the grammatical and structural level.
Algorithmic Authorship (Sentence Filterer)
What it does: Scans documents and identifies sentences starting with 'If', unnecessary words, or unclear nested statements.
Required Input: A document, article, or text block (minimum 500 words).
Use Cases: Editing, Proofreading, Document quality checks, Academic writing
Tokenizer, Lemmatizer and Stemmer
What it does: Parses short texts into a detailed NLP table with lemmas, stems, POS tags, and dependency relations.
Required Input: Short text paragraph or sentence (up to 500 words).
Use Cases: SEO/content analysis, NLP debugging, Keyword research
Syntax Tree Creator
What it does: Reveals grammatical structure by parsing paragraphs into sentences, clauses, and phrases with syntax trees.
Required Input: A paragraph or multiple sentences of well-formed English prose.
Use Cases: Syntax learning, SEO content refinement, Translation QA
Contextless Word Remover
What it does: Removes stop words, vague fillers, and redundant phrases.
Required Input: Text content (article, paragraph, or document). Works best with 200+ words.
Use Cases: SEO editing, Conversion copy optimization
Vocabulary Richness Auditor
What it does: Measures complex, varied language metrics like type/token ratio and syllable patterns.
Required Input: Text content (minimum 300 words for statistical significance).
Use Cases: SEO content optimization, Content quality auditing
Metadiscourse Markers Auditor
What it does: Identifies markers that organize discourse, including frame markers and result markers.
Required Input: Article, essay, or long-form content with multiple paragraphs.
Use Cases: SEO content optimization, Content clarity & flow
Question Logic Analyzer
What it does: Maps logical relationships between entities in questions.
Required Input: A question or set of questions. Can include FAQ lists.
Use Cases: Entity relationship analysis, SEO strategy, Content brief creation
Translator (Context-based)
What it does: Translates English to Turkish while preserving SEO context and topical relevance.
Required Input: English source text with SEO context.
Use Cases: SEO translation, Content localization, Metadata optimization
🧠 2. Semantic Analysis & Meaning Extraction (9 Agents)
These agents extract meaning, semantic roles, and conceptual relationships from text.
Frame Semantics Analyzer
What it does: Identifies predicates and frame elements to show how syntax connects to semantic roles.
Required Input: Paragraph or article text with action-oriented content.
Use Cases: SEO content analysis, Content optimization, SERP analysis
Semantic Role Labeler
What it does: Marks AGENT, PATIENT, EXPERIENCER, and other roles to show 'who did what'.
Required Input: Sentences or paragraphs with clear action statements.
Use Cases: SEO content analysis, On-page optimization
Word Meaning Extractor
What it does: Identifies all possible meanings of a word and highlights the one used in context.
Required Input: Target word plus surrounding context (sentence or paragraph).
Use Cases: Word-sense disambiguation, SEO keyword clarity
Semantic Emphasizer
What it does: Highlights semantically important concepts with relevance and importance scores.
Required Input: Text content with clear topical focus.
Use Cases: SEO content analysis, Topical authority mapping
Lexical Path Analyzer
What it does: Traces multi-step lexical paths like synonyms, antonyms, and hypernyms.
Required Input: Two words/concepts to find paths between.
Use Cases: Topical mapping, Content clustering, Internal linking
Triple Generator
What it does: Converts paragraphs into subject-predicate-object triples with prominence scores.
Required Input: Paragraph or article text. Factual content yields best results.
Use Cases: Knowledge graph building, Topical authority mapping, Schema planning
Microsemantics – Relevant Item Finder
What it does: Pinpoints the single most topically relevant content unit for a given phrase.
Required Input: Target keyword/phrase plus content to analyze.
Use Cases: Keyword targeting, Internal linking, Topical alignment
Knowledge Domain Terms Extractor
What it does: Generates a 100+ term glossary for any topic with definitions and importance scores.
Required Input: Topic or domain name. Can include seed terms.
Use Cases: Topical map design, Entity-first SEO, Content briefing
Entity Type Root, Rare, Unique Attribute Extractor
What it does: Extracts structured attributes (Root, Rare, Unique) for any entity type.
Required Input: Entity type (e.g., 'smartphone', 'lawyer', 'recipe').
Use Cases: Entity schema design, Faceted navigation, Taxonomy building
🔗 3. Entity & Knowledge Graph Oriented (8 Agents)
These agents focus on named entities and knowledge graph construction.
Named Entity Inserter
What it does: Enriches paragraphs by inserting missing but topically related entities.
Required Input: Text paragraph plus target topic.
Use Cases: Content enrichment, Topical authority building
Named Entity Suggester
What it does: Uncovers contextually relevant entities and ranks them by prominence.
Required Input: Topic or content paragraph.
Use Cases: Content expansion, Topical authority, Entity gap analysis
Named Entity Suggester (Person Type)
What it does: Builds shared contextual domains between subjects focusing on people and experts.
Required Input: Two topics to find connecting people.
Use Cases: Topical mapping, Content strategy, Digital PR, E-E-A-T building
Which-Agent
What it does: Provides 'which one and why' comparisons for tools, assets, or concepts.
Required Input: Two or more options to compare plus context.
Use Cases: Product comparison, Tech choice, Strategy selection
Who-Agent
What it does: Creates context-rich profiles of people linked to historical and professional landscapes.
Required Input: Person name plus optional context.
Use Cases: Biographical lookup, Expert profiling, Thought leadership mapping
What-Agent
What it does: Creates consistent 8-sentence explanations for 'What is X?' questions.
Required Input: Concept, term, or entity name to define.
Use Cases: Concept glossaries, Training, Documentation, Featured snippets
Information Graph Creator (Legal)
What it does: Builds entity-relationship maps for legal documents.
Required Input: Legal document, case summary, or structured legal data.
Use Cases: Content modeling, Information architecture, Legal analysis
Irrelevant Attribute Auditor
What it does: Scans entities and flags sensitive, irrelevant, or unnecessary attributes.
Required Input: Entity list with attributes (CSV/JSON format).
Use Cases: Data cleaning, Bias auditing, ML feature review
🎯 4. Topicality & Coverage Analysis (7 Agents)
These agents measure and optimize topical relevance and coverage gaps.
Topicality Scorer
What it does: Evaluates paragraph relevance to different topics with contextual scores (0-100).
Required Input: Paragraph or article plus target topic(s).
Use Cases: SEO content auditing, Competitor analysis, Brief validation
Bridge Topic Suggester
What it does: Uncovers topical gaps in title tags and URLs to propose new topics.
Required Input: List of existing title tags, URLs, or topics (CSV preferred).
Use Cases: Topical gap analysis, Site architecture, Content roadmap
Topic Clusterer
What it does: Automatically builds topical clusters from keyword lists using semantic similarity.
Required Input: Keyword list (CSV preferred). Include search volumes for prioritization.
Use Cases: Keyword research organization, Content silo structure
Query Term Weight Calculator
What it does: Computes importance of query terms using lexical and BERT-based methods.
Required Input: Search query or list of queries.
Use Cases: Query intent analysis, SEO prioritization, Title optimization
Title-Query Coverage Ratio Auditor
What it does: Measures how well page titles cover target queries.
Required Input: Spreadsheet/CSV with Page Title and Target Query columns.
Use Cases: Content audits, Migration QA, Metadata cleanup
Contextual Vector Sharpener and Aligner
What it does: Rewrites context paragraphs to maximize semantic relevance to target queries.
Required Input: Current intro/context paragraph plus target query.
Use Cases: SEO intro optimization, Landing page relevance
Context Paragraph Refresher
What it does: Revises existing text to be more context-rich with definitions and expert quotes.
Required Input: Existing paragraph plus topic focus.
Use Cases: SEO content refinement, B2B content, E-E-A-T enhancement
💭 5. Sentiment & Processing (2 Agents)
These agents analyze and optimize sentiment in customer feedback.
Comment Creator (Pros, Cons)
What it does: Generates structured sentiment summaries with recurring themes from customer comments.
Required Input: Customer reviews or comments.
Use Cases: Review mining, E-commerce optimization, Voice of customer
Comment Sentiment Optimizer
What it does: Softens emotional extremes and amplifies constructive positivity in reviews.
Required Input: Review or comment text to optimize.
Use Cases: Reputation management, Comment polishing, Support scripts
🔍 6. SEO & Search Quality Auditing (5 Agents)
These agents help diagnose ranking issues and audit content quality.
HCU Auditor
What it does: Evaluates content against Google's helpfulness, quality, and trust criteria.
Required Input: Article or page content. Include URL if available.
Use Cases: Quality checks, AI-content screening, HCU recovery
Quality Update Auditor
What it does: Maps traffic data to specific Google updates with impact visualizations.
Required Input: Traffic data export (GA/GSC CSV) covering multiple updates.
Use Cases: Traffic forensics, Recovery diagnostics, Client reporting
Spam Hit Detector
What it does: Detects if a website was hit by Google spam or link spam updates.
Required Input: Traffic/ranking CSV data with dates. Include backlink data if available.
Use Cases: Ranking loss analysis, Penalty diagnosis, Recovery tracking
Publication Frequency Auditor
What it does: Analyzes publication speed and URL structure from sitemap files.
Required Input: Sitemap export or URL list with dates (CSV format).
Use Cases: Editorial planning, Site architecture, Competitor research
Image Auditor
What it does: Evaluates how well images match textual concepts and topical scores.
Required Input: Image file or URL plus associated text/topic.
Use Cases: Ad creatives, E-commerce, Image-text alignment
📊 7. Data, Logs & Performance Analysis (4 Agents)
These agents process log files, keyword datasets, and competitive metrics.
Data Analyzer (Unique Queries)
What it does: Identifies patterns in keyword datasets like query lengths and company names.
Required Input: Keyword/query dataset in CSV format.
Use Cases: Keyword research, Intent mapping, FAQ discovery
Log File Analyzer
What it does: Processes crawl log files to show Googlebot discovery patterns.
Required Input: Server log file or crawl log export.
Use Cases: Crawl behavior analysis, Internal linking evaluation
Outranking Cost Calculator
What it does: Visualizes the difficulty and cost of outranking competitors.
Required Input: Target keyword plus competitor URLs.
Use Cases: SEO budgeting, Opportunity mapping, Client pitches
Backlink Analyzer
What it does: Compares two websites' backlink profiles with DR bin visualizations.
Required Input: Two domain names or backlink exports for comparison.
Use Cases: Competitor link analysis, Authority growth tracking
📋 8. Content Structure & Summarization (3 Agents)
These agents extract key facts, generate safe content, and optimize site structure.
Key Fact Summarizer
What it does: Extracts critical information and ranks factual statements by prominence.
Required Input: Article, report, or document with factual content.
Use Cases: Entity & attribute mapping, Content briefing
Safe Answer Generator
What it does: Analyzes questions from multiple expert angles with safe explanations.
Required Input: Question or topic to analyze.
Use Cases: Multi-angle SEO analysis, Strategy decision, Safe content
Footer Link Suggester
What it does: Proposes SEO-friendly footer structures with 5 columns and contextual anchors.
Required Input: Site structure overview or existing navigation.
Use Cases: Footer architecture, Internal linking, Site navigation
⚙️ 9. Technical Generators (2 Agents)
These agents generate semantic HTML and structured product data.
Semantic HTML Math Formula Creator
What it does: Converts math formulas into semantic <math> elements.
Required Input: Math formula (LaTeX, plain text, or image).
Use Cases: Equation publishing, Accessibility, Technical documentation
Product Specs Generator
What it does: Transforms product info into structured lists of 40+ ordered specifications.
Required Input: Product name, description, or existing specs.
Use Cases: Buyer guides, E-commerce optimization, Comparison pages
🚀 How to Access and Use These AI Agents
- ChatGPT Plus subscription – All agents are custom GPTs requiring Plus access
- Click the agent link – Opens directly in ChatGPT
- Provide the required input – Each agent specifies what data to provide
- Review and apply results – Use the output for your SEO workflow
❓ Frequently Asked Questions
The agents themselves are free, but you need a ChatGPT Plus subscription ($20/month) to access custom GPTs.
For content optimization, start with the Vocabulary Richness Auditor and Semantic Emphasizer. For topical mapping, try the Knowledge Domain Terms Extractor and Topic Clusterer.