geo-optimizer

‎Make websites visible and citable by AI search engines (ChatGPT Search, Perplexity, Claude, Google AI Overviews).‎

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.

چه چیزی می‌گیرید

• ‎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

دربارهٔ این اسکیل

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:

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:

Google AI readiness checks

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

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:

Informational Checks

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

MCP Integration

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

Tools:

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:

# 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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