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GEO Optimizer

开源的生成式引擎优化（GEO）与答案引擎优化（AEO）工具包

PyPI Python 3.9+ License: MIT MCP

🌐 English · 简体中文

一条命令，为你的网站打出 0–100 的 AI 可见度分数，并告诉你该怎么修。

🌐 English

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What is GEO Optimizer?

GEO Optimizer is a free, open-source CLI (also a Python library, MCP server, and Astro integration) that measures how visible your website is to AI answer engines — ChatGPT, Perplexity, Google AI Overviews, Gemini, and Claude — and tells you how to get cited by them.

This discipline goes by several names: AEO (Answer Engine Optimization), GEO (Generative Engine Optimization), AI SEO, LLM SEO, and AI visibility monitoring. This tool covers all of them.

# Zero install — score any site against 8 AI-readiness categories
uvx --from geo-optimizer-skill geo audit --url https://yoursite.com

It scores your site against 47 research-backed methods (Princeton KDD 2024, AutoGEO ICLR 2026) and generates the exact fixes — robots.txt bot rules, llms.txt, JSON-LD schema, and the content patterns models quote.

Why AI visibility matters

AI search engines give one synthesized answer and cite a handful of sources. If your site isn't one of them, you're invisible — even if you rank #1 on Google.

  • 28.3% of ChatGPT's most-cited pages have zero organic visibility on Google (Ahrefs) — AI engines reward different signals than classic SEO.

  • Proper JSON-LD schema lifts LLM extraction accuracy from 16% to 54% (Semrush test on GPT-4).

Install

pip install geo-optimizer-skill

Quick Start

# Audit any site — score 0-100 with actionable recommendations
geo audit --url https://yoursite.com

# Audit a full sitemap, 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

# What changed since the last snapshot? (CI: --fail-on warning)
geo drift --url https://yoursite.com

# Ask real AI engines: is my brand mentioned? Is my domain cited?
geo citations --brand "YourBrand" --domain yoursite.com --topic "your category"

# 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. Citation bots explicitly allowed?

llms.txt

/18

Present, 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, front-loading?

Brand & Entity

/10

Brand coherence, Knowledge Graph links, about page, topic authority

Signals

/6

<html lang>, RSS/Atom feed, dateModified freshness?

AI Discovery

/6

.well-known/ai.txt, /ai/summary.json, /ai/faq.json?

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

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 --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

CI/CD — fail the build when AI-readiness drops

# .github/workflows/geo.yml
- uses: skywalkerqqq/geo-optimizer-skill@v4.17.1
  with:
    url: https://yoursite.com
    min-score: 70        # Fail the build if the GEO score drops below 70
    format: sarif        # Upload findings to the GitHub Security tab

MCP Server

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

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

Python API

from geo_optimizer import audit

result = audit("https://example.com")
print(result.score)          # 85
print(result.band)           # "good"
print(result.recommendations)

Research foundation

Paper

Venue

Key finding

GEO: Generative Engine Optimization

KDD 2024

9 methods 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

Scope and limits

GEO Optimizer scores input-side readiness — the technical and content signals that make a site reachable, parseable, and citable. It does not observe what AI engines actually say about a brand outside its own geo citations LLM check.

A high score means the signals are in place. It does not guarantee citation: that also depends on the question, the competition, and the engine.

License

MIT © skywalkerqqq — see LICENSE.

Related MCP server: maxaeo-ai-visibility-mcp

🌐 简体中文

返回 English

什么是 GEO Optimizer？

GEO Optimizer 是一个免费的开源命令行工具（同时可作为 Python 库、MCP Server 和 Astro 集成使用），用于衡量你的网站在 AI 答案引擎（ChatGPT、Perplexity、Google AI Overviews、Gemini、Claude）中的可见度，并告诉你如何让它引用你。

这个领域有多种叫法：AEO（答案引擎优化）、GEO（生成式引擎优化）、AI SEO、LLM SEO、AI 可见度监测。本工具覆盖全部含义。

# 无需安装 —— 一条命令给任意网站打出 8 大维度的 AI 就绪度分数
uvx --from geo-optimizer-skill geo audit --url https://yoursite.com

它依据 47 项研究方法（Princeton KDD 2024、AutoGEO ICLR 2026）为站点评分，并直接生成修复内容——robots.txt 机器人规则、llms.txt、JSON-LD 结构化数据，以及模型最爱引用的内容写法。

为什么 AI 可见度重要

AI 搜索引擎只给一个综合答案，并只引用少数几个来源。如果你的站点不在其中，即使你在 Google 排名第一，也等于隐形。

  • ChatGPT 最常引用的页面中，28.3% 在 Google 上零自然流量（Ahrefs）——AI 引擎的信号偏好与传统 SEO 不同。

  • 完善的 JSON-LD 结构化数据可将 LLM 抽取准确率 从 16% 提升到 54%（Semrush 在 GPT-4 上的测试）。

安装

pip install geo-optimizer-skill

快速开始

# 审计任意站点 —— 0-100 分 + 可执行建议
geo audit --url https://yoursite.com

# 审计整个 sitemap，优先暴露最弱的页面
geo audit --sitemap https://yoursite.com/sitemap.xml --max-urls 25

# 对比页面改版前后
geo diff --before https://yoursite.com/page-old --after https://yoursite.com/page-new

# 保存历史并检测分数回退
geo audit --url https://yoursite.com --save-history --regression

# 距上次快照发生了什么变化？（CI 可用 --fail-on warning）
geo drift --url https://yoursite.com

# 直接问 AI 引擎：我的品牌被提及了吗？我的域名被引用了吗？
geo citations --brand "你的品牌" --domain yoursite.com --topic "你的品类"

# 一次性生成所有缺失文件（robots.txt、llms.txt、schema、meta）
geo fix --url https://yoursite.com --apply

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

# 生成 JSON-LD 结构化数据
geo schema --type faq --url https://yoursite.com

评分维度

维度

分值

检查内容

Robots.txt

/18

27 个 AI 爬虫分 3 层。引用型机器人是否显式放行？

llms.txt

/18

是否存在、H1 + 引用块、分节、链接、深度。有 llms-full.txt 吗？

Schema JSON-LD

/16

WebSite、Organization、FAQPage、Article。丰富度（5+ 属性）？

Meta 标签

/14

title、description、canonical、Open Graph 是否完整？

内容

/12

H1、数据、外部引用、标题层级、结论前置？

品牌与实体

/10

品牌一致性、知识图谱链接、关于页、主题权威度

信号

/6

<html lang>、RSS/Atom、dateModified 新鲜度？

AI 发现

/6

.well-known/ai.txt、/ai/summary.json、/ai/faq.json？

分数区间： 86-100 优秀 · 68-85 良好 · 36-67 基础 · 0-35 危急

另有独立的可被引述性得分（0-100），覆盖 47 项方法： 引用 +41% · 数据 +33% · 流畅度 +29% · 引用来源 +27% 等。

输出格式

geo audit --url https://example.com --format text   # 终端可读（默认）
geo audit --url https://example.com --format json   # 机器可读
geo audit --url https://example.com --format rich   # 彩色终端
geo audit --url https://example.com --format html   # 自包含 HTML 报告
geo audit --url https://example.com --format sarif  # GitHub Code Scanning
geo audit --url https://example.com --format junit  # Jenkins、GitLab CI

CI/CD —— AI 就绪度下降就构建失败

# .github/workflows/geo.yml
- uses: skywalkerqqq/geo-optimizer-skill@v4.17.1
  with:
    url: https://yoursite.com
    min-score: 70        # 分数低于 70 则构建失败
    format: sarif        # 结果上传到 GitHub Security 标签页

MCP Server

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

然后直接问："审计我的站点并修复缺失项"

Python API

from geo_optimizer import audit

result = audit("https://example.com")
print(result.score)          # 85
print(result.band)           # "good"
print(result.recommendations)

研究基础

论文

会议

核心发现

GEO: Generative Engine Optimization

KDD 2024

1 万条查询测试 9 种方法。引用来源 +115%、数据 +40%

AutoGEO

ICLR 2026

自动规则抽取，较 Princeton 基线 +50.99%

C-SEO Bench

2025

多数内容操纵无效，基础设施才是关键

能力边界

GEO Optimizer 评估的是输入侧就绪度——让站点可被抓取、可被解析、可被引用的技术与内容信号。它不监测 AI 引擎实际如何提及你的品牌（除 geo citations 自带的 LLM 检查外）。

高分代表信号已到位，不等于保证被引——最终还取决于提问方式、竞争对手和引擎本身。

许可

MIT © skywalkerqqq — 详见 LICENSE。

MIT License · Built and maintained by skywalkerqqq

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