GEO Optimizer

Make websites visible and citable by AI search engines (ChatGPT Search, Perplexity, Claude, Gemini AI Overviews). Implements the GEO audit framework plus a 47-method citability engine based on Princeton KDD 2024 research.

Workflow

Step 1 — Audit the site

Run geo audit first. It scores the site 0–100 across 8 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)

Ensure AI citation bots can reach the site. Critical bots that must never be blocked:

• OAI-SearchBot — ChatGPT Search citations
• PerplexityBot — Perplexity answer citations
• ClaudeBot — Claude web citations
• Google-Extended — Gemini AI Overviews

To allow citations while blocking training: Disallow: / for GPTBot and anthropic-ai, but keep Allow: / for OAI-SearchBot, ClaudeBot, PerplexityBot.

Step 3 — Generate llms.txt

/llms.txt tells AI crawlers what the site is about and which pages matter.

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.

Step 5 — Optimize content (Princeton GEO methods)

Apply evidence-based improvements ordered by measured impact:

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 (10,000 queries on Perplexity.ai). Extended by AutoGEO ICLR 2026, SE Ranking 2025, Growth Marshal 2026 to 47 total methods.

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

8 categories, 100 points total:

Category	What it measures
robots	AI bot access via robots.txt
llms	llms.txt presence, structure, depth
schema	JSON-LD types, richness, sameAs
meta	title, description, canonical, Open Graph
content	H1, word count, numbers, links, structure
signals	lang attribute, RSS feed, freshness
ai_discovery	.well-known/ai.txt, /ai/summary.json, /ai/faq.json
brand_entity	Name consistency, Knowledge Graph, about/contact

CLI Commands

11 commands covering audit, remediation, analysis, and monitoring:

# ── Primary ──
geo audit    --url URL [--format text|json|rich|html|github|ci|pdf] [--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 [--inject FILE]

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

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

# ── Utility ──
geo logs       --path LOGFILE
geo snapshots  --url URL [--save | --compare SNAPSHOT_ID]

Output Formats

7 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
ci	--format ci	CI/CD systems (structured annotations)
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 methods)
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