geo-optimizer-skill

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 | 12,00