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 citationsPerplexityBot— Perplexity answer citationsClaudeBot— Claude web citationsGoogle-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) |
--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 | 12,00 |