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Generative Engine Optimization toolkit — make websites visible to AI search engines

Project description

GEO Optimizer

Make websites visible to AI search engines

PyPI Python 3.9+ CI codecov License: MIT MCP Compatible

Audit, fix, and optimize any website to be cited by ChatGPT, Perplexity, Claude, and Gemini.

Quick Start · Live Demo · Documentation · Changelog


Why this exists

AI search engines give direct answers and cite their sources. If your site isn't optimized, you're invisible — even if you rank #1 on Google.

User: "What's the best mortgage calculator?"

Perplexity: "According to [Competitor.com], the formula is..."
             ↑ They appear. You don't.

GEO Optimizer audits your site against 47 research-backed methods (Princeton KDD 2024, AutoGEO ICLR 2026) and generates the fixes.


Quick Start

pip install geo-optimizer-skill
# Audit any site — get a score 0-100 with actionable recommendations
geo audit --url https://yoursite.com

# Audit a full sitemap and surface weakest pages first
geo audit --sitemap https://yoursite.com/sitemap.xml --max-urls 25

# Compare before/after versions of a page
geo diff --before https://yoursite.com/page-old --after https://yoursite.com/page-new

# Save history and detect regressions over time
geo audit --url https://yoursite.com --save-history --regression

# Show the saved trend for a site
geo history --url https://yoursite.com

# Run recurring monitoring and generate an HTML trend report
geo track --url https://yoursite.com --report --output ./geo-track-report.html

# Auto-generate all missing files (robots.txt, llms.txt, schema, meta)
geo fix --url https://yoursite.com --apply

# Generate llms.txt from sitemap
geo llms --base-url https://yoursite.com --output ./public/llms.txt

# Generate JSON-LD schema
geo schema --type faq --url https://yoursite.com

What it checks

Area Points What GEO Optimizer looks for
Robots.txt /18 27 AI bots across 3 tiers (training, search, user). Citation bots explicitly allowed?
llms.txt /18 Present, has H1 + blockquote, sections, links, depth. Companion llms-full.txt?
Schema JSON-LD /16 WebSite, Organization, FAQPage, Article. Schema richness (5+ attributes)?
Meta Tags /14 Title, description, canonical, Open Graph complete?
Content /12 H1, statistics, external citations, heading hierarchy, lists/tables, front-loading?
Brand & Entity /10 Brand name coherence, Knowledge Graph links (Wikipedia/Wikidata/LinkedIn/Crunchbase), about page, geo signals, topic authority
Signals /6 <html lang>, RSS/Atom feed, dateModified freshness?
AI Discovery /6 .well-known/ai.txt, /ai/summary.json, /ai/faq.json, /ai/service.json?

Score bands: 86-100 Excellent · 68-85 Good · 36-67 Foundation · 0-35 Critical

Bonus checks (informational, do not affect score):

Check What it detects
CDN Crawler Access Does Cloudflare/Akamai/Vercel block GPTBot, ClaudeBot, PerplexityBot?
JS Rendering Is content accessible without JavaScript? SPA framework detection
WebMCP Readiness Chrome WebMCP support: registerTool(), toolname attributes, potentialAction schema
Negative Signals 8 anti-citation signals: CTA overload, popups, thin content, keyword stuffing, missing author, boilerplate ratio
Prompt Injection Detection 8 manipulation patterns: hidden text, invisible Unicode, LLM instructions, HTML comment injection, monochrome text, micro-font, data-attr injection, aria-hidden abuse
Trust Stack Score 5-layer trust aggregation (Technical, Identity, Social, Academic, Consistency) — composite grade A-F

Plus a separate Citability Score (0-100) measuring content quality across 47 methods: Quotation +41% · Statistics +33% · Fluency +29% · Cite Sources +27% · and 43 more.


Output formats

geo audit --url https://example.com --format text     # Human-readable (default)
geo audit --url https://example.com --format json      # Machine-readable
geo audit --sitemap https://example.com/sitemap.xml    # Batch sitemap audit (text)
geo audit --sitemap https://example.com/sitemap.xml --format json  # Batch sitemap audit (JSON)
geo audit --url https://example.com --format rich      # Colored terminal
geo audit --url https://example.com --format html      # Self-contained report
geo audit --url https://example.com --format sarif     # GitHub Code Scanning
geo audit --url https://example.com --format junit     # Jenkins, GitLab CI
geo audit --url https://example.com --format github    # GitHub Actions annotations
geo history --url https://example.com                  # Saved score trend
geo track --url https://example.com --report           # Monitoring HTML report

CI/CD Integration

# .github/workflows/geo.yml
- uses: Auriti-Labs/geo-optimizer-skill@v1
  with:
    url: https://yoursite.com
    min-score: 70        # Fail if score drops below 70
    format: sarif        # Upload to GitHub Security tab

Works with GitHub Actions, GitLab CI, Jenkins, CircleCI, and any CI that runs Python.

For longitudinal checks, persist snapshots and fail on regressions:

geo audit --url https://yoursite.com --save-history --regression

MCP Server

Use GEO Optimizer from Claude, Cursor, Windsurf, or any MCP client:

pip install geo-optimizer-skill[mcp]
claude mcp add geo-optimizer -- geo-mcp

Then ask: "audit my site and fix what's missing"

Tool Purpose
geo_audit Full audit with score + recommendations
geo_fix Generate fix files
geo_llms_generate Generate llms.txt
geo_citability Content citability analysis (47 methods)
geo_schema_validate Validate JSON-LD
geo_compare Compare multiple sites
geo_gap_analysis Explain the gap between two sites and prioritize fixes
geo_ai_discovery Check AI discovery endpoints
geo_check_bots Check bot access via robots.txt
geo_trust_score 5-layer trust signal aggregation
geo_negative_signals 8 anti-citation signal detection

Use as AI Context

Load the right file into your AI assistant for GEO expertise:

Platform File
Claude Projects ai-context/claude-project.md
ChatGPT Custom GPT ai-context/chatgpt-custom-gpt.md
Cursor ai-context/cursor.mdc
Windsurf ai-context/windsurf.md
Kiro ai-context/kiro-steering.md

Internal Skill System

The repository now includes a structured internal skill catalog for maintainers at src/geo_optimizer/skills/catalog/ plus validation rules and examples. See docs/skill-system.md for the v1 architecture.


Python API

from geo_optimizer import audit

result = audit("https://example.com")
print(result.score)                      # 85
print(result.band)                       # "good"
print(result.citability.total_score)     # 72
print(result.score_breakdown)            # {"robots": 18, "llms": 14, ...}
print(result.recommendations)            # ["Add FAQPage schema..."]

Async variant:

from geo_optimizer import audit_async
result = await audit_async("https://example.com")

Dynamic Badge

Show your GEO score in your README:

![GEO Score](https://geo-optimizer-web.onrender.com/badge?url=https://yoursite.com)

Colors: 86-100 green · 68-85 cyan · 36-67 yellow · 0-35 red. Cached 1h.


Plugin System

Extend the audit with custom checks via entry points:

[project.entry-points."geo_optimizer.checks"]
my_check = "mypackage:MyCheck"

See examples/example_plugin.py for a working example.


Research Foundation

Paper Venue Key Finding
GEO: Generative Engine Optimization KDD 2024 9 methods tested on 10k queries. Cite Sources: +115%, Statistics: +40%
AutoGEO ICLR 2026 Automatic rule extraction. +50.99% over Princeton baseline
C-SEO Bench 2025 Most content manipulation is ineffective. Infrastructure matters most

We focus on technical infrastructure (robots.txt, llms.txt, schema, meta) over content rewriting. The research confirms: if crawlers can't find and parse your content, prose optimization doesn't matter.


Security

All URL inputs are validated against private IP ranges (RFC 1918, loopback, link-local, cloud metadata) with DNS pinning before any request. See SECURITY.md for reporting vulnerabilities.


Contributing

git clone https://github.com/YOUR_USERNAME/geo-optimizer-skill.git
cd geo-optimizer-skill && pip install -e ".[dev]"
pytest tests/ -v   # 1120+ tests, all mocked

Bug reports · Feature requests · CONTRIBUTING.md


MIT License · Built by Auriti Labs

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