GEO Optimizer

Make websites visible and citable by AI search engines (ChatGPT Search, Perplexity, Claude, Google AI Overviews). Implements the GEO audit framework plus a citability engine of 47 content checks, 9 of them taken directly from the methods tested in Princeton KDD 2024 research.

Workflow

Step 1 — Audit the site

Run geo audit first. It scores the site 0–100 across 9 categories and generates a prioritized action list.

geo audit --url https://yoursite.com
geo audit --url https://yoursite.com --format json
geo audit --sitemap https://yoursite.com/sitemap.xml --max-urls 25

Score bands: 0–35 critical · 36–67 foundation · 68–85 good · 86–100 excellent.

Step 2 — Fix AI crawler access (robots.txt)

AI crawlers fall into three tiers: training, search, and user-triggered fetch. To stay eligible to appear in AI search answers, do not block the search-tier bots (this makes the site eligible; it does not guarantee placement or citation):

• OAI-SearchBot — ChatGPT search
• PerplexityBot — Perplexity search index
• Claude-SearchBot — Claude search
• Googlebot — Google Search, which also feeds AI Overviews and AI Mode. These are governed by Googlebot access and snippet controls (nosnippet, max-snippet), not by Google-Extended

Google-Extended is a robots.txt token, not a separate crawler. It controls whether Google may use content for Gemini/Vertex training and grounding. It does not affect Search or AI Overviews.

To opt out of training while staying open to AI search: Disallow: / for the training bots (GPTBot, ClaudeBot, Google-Extended), and keep Allow: / for OAI-SearchBot, Claude-SearchBot, PerplexityBot, and Googlebot. Whether to block training is a policy choice; vendors treat these settings as independent.

Step 3 — Generate llms.txt

/llms.txt is a proposed convention that gives AI agents a short, machine-readable map of the site. Support varies by system and it does not guarantee AI visibility or citation.

geo llms --base-url https://yoursite.com --site-name "Site Name" --description "One-sentence description." --output ./public/llms.txt

Required structure: H1 (site name) → blockquote (description) → H2 sections with descriptive links. Keep under 200 lines. Full spec: https://llmstxt.org

Step 4 — Inject JSON-LD schema

Add structured data so AI engines understand page types:

geo schema --type website --url https://yoursite.com
geo schema --type faq --url https://yoursite.com/faq
geo schema --type webapp --url https://yoursite.com/tool

Types: website, webapp, faq, article, organization, breadcrumb, howto, review, product.

Structured data helps machines parse the page; it does not guarantee citation.

Step 5 — Optimize content (Princeton GEO methods)

Apply content improvements ordered by the effect reported in the paper's benchmark:

Priority	Method	Impact	Action
🔴 1	Cite Sources	+30–115%	Add authoritative external links
🔴 2	Add Statistics	+40%	Include concrete numbers, percentages, dates
🟠 3	Quotation Addition	+30–40%	Expert quotes: "Text" — Name, Role, Org, Year
🟠 4	Authoritative Tone	+6–12%	Confident, expert framing
🟡 5	Fluency Optimization	+15–30%	Clear, direct language
🟡 6	Easy-to-Understand	+8–15%	Define terms, use analogies
🟢 7	Technical Terms	+5–10%	Correct industry terminology
🟢 8	Unique Words	+5–8%	Vary vocabulary deliberately
❌ 9	Keyword Stuffing	~0% ⚠️	Do NOT apply — neutral to negative

Source: Princeton KDD 2024 (GEO-bench, 10,000 queries; the authors also report tests on Perplexity.ai). The table lists the nine methods tested in the paper. Impact figures are relative changes in the paper's visibility metrics, not citation rates; effects varied by domain and engines have changed since. GEO Optimizer's citability score uses 47 content checks, 9 of which come from these methods. Related research: AutoGEO (arXiv preprint 2510.11438, 2025; no publication venue listed).

Step 6 — Auto-fix all gaps

Generate all missing files at once:

geo fix --url https://yoursite.com --apply
geo fix --url https://yoursite.com --only robots,llms,schema

Creates robots.txt entries, llms.txt, JSON-LD schema, meta tags, and AI discovery endpoints based on audit results.

Scoring

9 categories, 100 points total in rubric v2:

Category	What it measures	Points (v2)
google_ai	Google AI readiness checks	20
robots	Robots.txt availability and citation-bot access	14
schema	JSON-LD validity, richness, types, and visible-name match	14
content	Headings, evidence, links, structure, and image alt coverage	14
brand_entity	Brand coherence, Knowledge Graph, about/contact, identity, authority	12
meta	Title, description, and Open Graph	11
llms	llms.txt presence, sections, links, and llms-full.txt	6
signals	Language, RSS, and freshness	6
ai_discovery	ai.txt, summary.json, and markdown negotiation	3

Google AI readiness checks

Each check includes its Google source URL in audit JSON and recommendations:

• G-INDEX (5): final status 200; no meta robots, googlebot, or X-Robots-Tag noindex; Googlebot is not blocked by robots.txt. Source: https://developers.google.com/search/docs/essentials/technical
• G-SNIPPET (5): no nosnippet, max-snippet:0, or max-snippet below 50; max-image-preview:none warns; data-nosnippet over 50% fails and over 10% warns. Source: https://developers.google.com/search/docs/crawling-indexing/robots-meta-tag
• G-CANONICAL (3): exactly one absolute canonical in <head>, matching the final URL. Source: https://developers.google.com/search/docs/crawling-indexing/consolidate-duplicate-urls
• G-DATES (2): visible date agrees with datePublished/dateModified and no date is in the future. Source: https://developers.google.com/search/docs/appearance/publication-dates
• G-BYLINE (2): visible author or JSON-LD author, ideally linked to an author page. Source: https://developers.google.com/search/docs/fundamentals/creating-helpful-content
• G-LINKS (1): internal links use crawlable <a href> elements and no #/ routes. Source: https://developers.google.com/search/docs/crawling-indexing/javascript/javascript-seo-basics
• G-VIEWPORT (1): a meta viewport is present. Source: https://developers.google.com/search/docs/appearance/page-experience
• G-SITEMAP (1): a Sitemap directive is declared in robots.txt. Source: https://developers.google.com/search/docs/crawling-indexing/sitemaps/build-sitemap
• G-SD-VISIBLE (2 in schema): JSON-LD name or headline appears in visible text. Source: https://developers.google.com/search/docs/appearance/structured-data/sd-policies
• G-GENAI-CONTROL (0, manual): check Search Console generative-AI opt-out. Source: https://developers.google.com/search/docs/fundamentals/ai-optimization-guide

llms.txt is useful to other AI agents but is ignored by Google Search. The score is versioned: JSON exposes score_version and per-category score_max; use geo audit --score-version 1 for legacy v1 comparisons. History deltas and regressions compare snapshots only when their score_version matches.

CLI Commands

Main commands covering audit, remediation, analysis, and monitoring (run geo --help for all 16, including access, authority, citations, drift, and perception):

# ── Primary ──
geo audit    --url URL [--format text|json|rich|html|pdf|github|sarif|junit] [--sitemap URL]
geo fix      --url URL [--apply] [--only robots,llms,schema,meta,ai_discovery,content]
geo llms     --base-url URL --site-name NAME --description DESC --output FILE
geo schema   --type TYPE --url URL [--file FILE --inject]

# ── Analysis ──
geo diff       --before URL --after URL
geo history    --url URL
geo coherence  --sitemap URL

# ── Monitoring ──
geo monitor  --domain DOMAIN
geo track    --url URL [--report | --history] [--output FILE]

# ── Utility ──
geo logs       --file LOGFILE
geo snapshots  [--query TEXT --answer-file FILE] [--quality --snapshot-id ID]

Output Formats

8 formats for different workflows:

Format	Flag	Use case
text	--format text	Terminal (default)
json	--format json	Programmatic consumption, CI pipelines
rich	--format rich	Colored terminal with ASCII dashboard
html	--format html	Self-contained HTML report
github	--format github	GitHub Actions annotations
sarif	--format sarif	Code-scanning tools (SARIF)
junit	--format junit	CI test reports (JUnit XML)
pdf	--format pdf	Client-facing reports

Informational Checks

10 non-scoring checks that provide deeper analysis beyond the 0–100 score:

Check	What it detects
WebMCP Readiness	SearchAction, labeled forms, tool attributes for AI agents
Negative Signals	CTA overload, thin content, keyword stuffing, boilerplate
Prompt Injection	LLM instructions in content, HTML comment injection, hidden text
Trust Stack	5-layer trust score (technical, identity, social, academic, consistency)
RAG Chunk Readiness	Content structure optimized for retrieval-augmented generation
Embedding Proximity	Semantic alignment between title, headings, and body content
Content Decay	Temporal signals indicating stale or outdated content
Platform Citation	Per-platform citation profile (ChatGPT vs Perplexity vs Gemini)
Context Window	Content length optimization for LLM context windows
Instruction Readiness	Content structure that helps LLMs follow extraction patterns

MCP Integration

12 tools and 5 resources for Claude Code, Cursor, Windsurf, and any MCP client:

Tools:

Tool	Description
geo_audit	Full GEO audit (score 0–100)
geo_fix	Generate automatic fixes
geo_llms_generate	Generate llms.txt from sitemap
geo_citability	Citability score (47 content checks)
geo_schema_validate	Validate JSON-LD schema
geo_compare	Compare GEO scores across sites (max 5)
geo_gap_analysis	Competitive gap analysis with priorities
geo_ai_discovery	Check AI discovery endpoints
geo_check_bots	Check AI bot access via robots.txt
geo_trust_score	Trust Stack Score (5-layer, grade A–F)
geo_negative_signals	Negative signals detection
geo_factual_accuracy	Factual claims and sourcing audit

Resources: geo://ai-bots · geo://score-bands · geo://methods · geo://changelog · geo://ai-discovery-spec

Plugin System

Extend the audit with custom checks via entry points:

# pyproject.toml
[project.entry-points."geo_optimizer.checks"]
my_check = "my_package:MyCheck"

Plugins implement the AuditCheck protocol (name, description, max_score, run()). Plugin results appear in the audit output but do not affect the base score.

Platform Context Files

Platform-optimized versions of this skill for different AI tools:

Platform	File	Size	Limit	How to use
Claude Projects	ai-context/claude-project.md	~11,700 chars	No limit	Project → Add as Knowledge
ChatGPT Custom GPT	ai-context/chatgpt-custom-gpt.md	~4,500 chars	8,000 chars	GPT Builder → System prompt
ChatGPT Instructions	ai-context/chatgpt-instructions.md	~800 chars	1,500 chars/field	Settings → Custom Instructions
Cursor	ai-context/cursor.mdc	~4,200 chars	No limit	Copy to .cursor/rules/geo-optimizer.mdc
Windsurf	ai-context/windsurf.md	~4,500 chars	12,000 chars	Copy to .windsurf/rules/geo-optimizer.md
Kiro	ai-context/kiro-steering.md	~3,300 chars	No limit	Copy to .kiro/steering/geo-optimizer.md

# Quick copy commands
mkdir -p .cursor/rules && cp ai-context/cursor.mdc .cursor/rules/geo-optimizer.mdc
mkdir -p .windsurf/rules && cp ai-context/windsurf.md .windsurf/rules/geo-optimizer.md
mkdir -p .kiro/steering && cp ai-context/kiro-steering.md .kiro/steering/geo-optimizer.md

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GEO Optimizer by Juan Camilo Auriti — https://github.com/auriti-labs/geo-optimizer-skill