SEO & Generative Engine Optimizer (GEO) #

Procedures, technical standards, and validation workflows for optimizing technical publications, developer documentation, and engineering blogs for traditional search ranking and AI-driven generative search engines, rooted directly in official Google Search Central guidelines.

Skill Architecture & Progressive Disclosure #

To minimize context overhead, SKILL.md defines core workflows, operational checklists, and decision trees. Load detailed reference modules and execute audit scripts on demand:

• Official Google Search GenAI Standards: Read references/google_search_genai_guidelines.md for Google’s official stance on AI Overviews, RAG grounding, query fan-out, non-commodity content, and mythbusting.
• Meta Tags & Robots Specifications: Read references/meta_tags_and_robots_spec.md for supported vs. unsupported meta tags, indexing directives (nosnippet, max-snippet, max-image-preview:large), and data-nosnippet.
• Multilingual & International SEO: Read references/multilingual_international_seo.md for hreflang rules, bidirectional parity, URL architecture, and avoiding IP auto-redirect pitfalls.
• AI Search & GEO Standards: Read references/geo_and_ai_search.md when optimizing for multi-engine AI discovery (Google AI Overviews, ChatGPT Search, Perplexity, Claude) and llms.txt.
• Technical SEO Checklist: Read references/technical_seo_checklist.md when auditing titles, descriptions, headings, outbound link qualifications (rel="sponsored", rel="ugc", rel="nofollow"), and image accessibility.
• Frontmatter & Taxonomy: Read references/frontmatter_standards.md when splitting human-facing summary from search-facing description, or formatting tag taxonomy.
• Schema.org Structured Data: Read references/schema_markup_guide.md when generating or validating JSON-LD (TechArticle, BreadcrumbList, HowTo).
• Site Migrations & Status Codes: Read references/site_migrations_and_status_codes.md for HTTP status codes, domain migrations, Change of Address workflows, crawl budget, and crawlable link architecture.
• Search Appearance & SERP Features: Read references/search_appearance_and_serp_features.md for SERP visual elements, site names, favicon technical requirements, featured snippets (Position 0 direct answers), byline date parity, Google Discover standards, organic sitelinks, and paywalled content (Flexible Sampling).
• Evergreen Content Refreshes: Read references/content_refresh_guide.md when updating decaying legacy articles, retitling posts, or resolving search query cannibalization.

Core SEO & GEO Philosophy #

Modern technical discoverability operates across two complementary surfaces:

graph LR
    A[Technical Article / Doc] --> B[Traditional Search Engine]
    A --> C[Generative AI Search Engine]
    B --> D[Keyword Matching, SERP CTR, Meta Snippets]
    C --> E[Entity Extraction, Direct Answer Synthesis, Citations]
    D --> F[Direct Web Traffic]
    E --> F
    E --> G[Grounding & LLM Mindshare]

1. Substance Over Commodity Fluff #

Search engines and generative AI models prioritize non-commodity content with high Information Gain—unique architectural diagrams, original code examples, verified benchmark data, and authoritative personal experience. Commodity summaries are filtered out.

2. The Inverted Pyramid & Value-First Answering #

Every technical post must answer the primary search intent above the fold (within the first 2 paragraphs) before detailing implementation specifics, historical context, or configuration options.

3. Dual-Purpose Metadata Split #

Never reuse the same text string for human preview cards and search engine indexing:

• summary (For Humans): A provocative, curiosity-inducing editorial hook displayed on homepage feeds, category lists, and related-article cards (80–180 characters).
• description (For Search & LLM Engines): A factual, high-density, keyword-grounded direct answer used in <meta name="description">, OpenGraph tags, and Schema.org description (120–160 characters). Note that <meta name="keywords"> is unsupported and ignored.

5-Stage SEO & GEO Optimization Workflow #

Follow this procedure when auditing or authoring content:

graph TD
    S1[Stage 1: Intent & Query Grounding] --> S2[Stage 2: Frontmatter & Metadata Split]
    S2 --> S3[Stage 3: GEO & Inverted Pyramid Structure]
    S3 --> S4[Stage 4: Technical SEO & Schema Verification]
    S4 --> S5[Stage 5: Deterministic Audit & Validation Loop]

Stage 1: Intent & Query Grounding #

1. Identify the Primary Target Intent:
   • Informational: Developer wants to understand a concept (e.g., “how do antigravity subagents work”).
   • Procedural/Tutorial: Developer wants step-by-step instructions (e.g., “build mcp server in go”).
   • Diagnostic/Troubleshooting: Developer has a specific error or configuration challenge.
2. Formulate the Core Search Query and ensure the article provides an unambiguous, definitive answer.

Stage 2: Frontmatter & Metadata Split #

Verify and craft distinct metadata fields:

---
title: "Building an MCP Server with Gemini CLI and Go"
summary: "Turn any Go CLI into a native tool for AI agents with just 50 lines of code."
description: "Step-by-step tutorial on building a Model Context Protocol (MCP) server in Go for Gemini CLI. Covers JSON-RPC handlers, tool discovery, and local debugging."
categories: ["Software Engineering"]
tags: ["apis", "golang", "mcp", "tutorial"]
---

• Title: 40–60 characters. Clear, high-signal, active phrasing.
• Description: 120–160 characters. Concise, keyword-rich, direct.
• Tags: Alphabetically sorted, lowercase kebab-case, no category duplication.

Stage 3: GEO & Inverted Pyramid Structure #

1. The Lead Block: Place the definitive takeaway, core metric, or architectural summary in the opening 150 words.
2. Scannable Headings: Use action-oriented H2 and H3 headings. Frame complex sections around real developer questions.
3. Data & Fact Density: Use tables for comparisons, bold key technical terms on first introduction, and provide copy-pasteable fenced code blocks with language identifiers.
4. Quotability: Write clear 1–2 sentence definitions that LLMs can extract verbatim as citations.

Stage 4: Technical SEO & Schema Verification #

1. Single H1: Exactly one H1 tag per document (typically supplied by template frontmatter title).
2. Heading Depth: Never skip levels (e.g., H2 directly to H4).
3. Image Accessibility: Every image must have descriptive alt text explaining the diagram or architecture (never generic names like image.png or empty alt="").
4. Outbound Link Qualification: Use rel="sponsored", rel="ugc", or rel="nofollow" where appropriate.
5. Internal Cross-Linking: Include 2–4 contextual internal links to related articles using descriptive anchor text (never “click here” or “this post”).
6. JSON-LD Schema: Ensure the template emits valid TechArticle or Article structured data.

Stage 5: Deterministic Audit & Validation Loop #

Execute the bundled audit tools and iterate until all issues are resolved:

1. If Speedgrapher MCP is available:

   • Run speedgrapher.analyze_seo on the target URL or Markdown draft.
   • Run speedgrapher.fog to ensure technical readability index is between 11.0 and 15.0.
   • Run speedgrapher.slop to ensure AI cliché score is < 25.

2. Run Bundled SEO Audit Script:

   bash 📋 Copy

   python3 scripts/audit_seo.py <path-to-markdown-file>

   For machine-readable JSON output:

   bash 📋 Copy

   python3 scripts/audit_seo.py <path-to-markdown-file> --json

3. Check llms.txt Synchronization: When adding or restructuring articles, verify that the site’s /llms.txt index is updated:

   bash 📋 Copy

   python3 scripts/generate_llmstxt.py --content-dir content/posts --output static/llms.txt

Validation Rules & Gotchas #

Resources & Tooling Map #

• Scripts:
  • scripts/audit_seo.py: Automated CLI for technical SEO, metadata split, and GEO readiness auditing.
  • scripts/generate_llmstxt.py: Generator for standard llmstxt.org index files.
• References:
  • references/google_search_genai_guidelines.md: Official Google Search Central AI search guidelines.
  • references/meta_tags_and_robots_spec.md: Google supported meta tags, robots directives, and HTTP headers.
  • references/multilingual_international_seo.md: Multilingual and international SEO with hreflang.
  • references/geo_and_ai_search.md: AI search engines, citation factors, and information gain.
  • references/technical_seo_checklist.md: Core meta tags, headings, link qualification, and accessibility.
  • references/frontmatter_standards.md: Taxonomy, tagging, and summary/description specification.
  • references/schema_markup_guide.md: Schema.org JSON-LD templates and property rules.
  • references/site_migrations_and_status_codes.md: HTTP status codes, full site migrations, crawl budget, and crawlable links.
  • references/search_appearance_and_serp_features.md: SERP anatomy, site names, favicons, featured snippets, Discover standards, and paywalls.
  • references/content_refresh_guide.md: Evergreen updates and search query decay mitigation.
• Assets:
  • assets/seo_audit_template.md: Standard audit report format.
  • assets/llms_txt_template.txt: Standard llms.txt template.