GEO网站优化工具包

Zero install

uvx --from geo-optimizer-skill geo audit --url https://yoursite.com

Or install it

pip install -U geo-optimizer-skill

geo audit --url https://yoursite.com # score 0–100 + prioritized fixes geo fix --url https://yoursite.com # preview robots.txt, llms.txt, schema, meta (--apply to write) geo audit --url https://yoursite.com --threshold 70 # exit 1 below 70 — use it as a CI gate geo citations --brand "Acme" --domain acme.com # does AI cite you? (bring your own API key)

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

Treat AI visibility like test coverage. The GitHub Action scores your site, fails the build below a threshold, and uploads findings to the Security tab:

```yaml

Quick Start

```bash

AI answers vary run to run — sample each query 5x for a confidence interval.

geo citations --brand "YourBrand" --domain yoursite.com --topic "your product category" --runs 5

Python API

from geo_optimizer import audit            # or audit_async

result = audit("https://example.com")
result.score                    # int, 0–100
result.band                     # "critical" | "foundation" | "good" | "excellent"
result.citability.total_score   # int, 0–100
result.score_breakdown          # {"robots": ..., "llms": ..., ...}
result.recommendations          # list of prioritized fixes

Integrations

.github/workflows/geo.yml

- uses: Auriti-Labs/geo-optimizer-skill@v4.19.0 with: url: https://yoursite.com min-score: 70 # fail the build below 70 format: sarif # findings in the GitHub Security tab ```

Any CI that runs Python works (GitLab, Jenkins, CircleCI). For longitudinal checks: geo drift --url … --fail-on warning. Output formats: text, json, rich, html, pdf, sarif, junit, github. The JSON format is the stable integration contract — see docs/json-contract.md.

--format html writes a self-contained report you can hand to a client. This one is the real report for geoready.dev:

<img src="assets/html-report.png" alt="GEO Optimizer HTML report for geoready.dev — score 91/100, per-category results, and recommendations" width="560"/>

Astro Integration

Generates llms.txt, /.well-known/ai.txt, and /ai/summary.json from your built routes at build time, and never overwrites hand-curated files. geoready.dev builds with it. Not yet published to npm — copy integrations/astro-geoready/ to integrations/astro-geoready/ in your project root:

// astro.config.mjs
import geoReady from './integrations/astro-geoready/index.mjs';
export default defineConfig({
  site: 'https://yoursite.com',
  integrations: [geoReady({ siteName: 'Your Site' })],
});

Compare before/after versions of a page

geo diff --before https://yoursite.com/page-old --after https://yoursite.com/page-new

FAQ

What is Answer Engine Optimization (AEO)? AEO is the practice of structuring a website so AI answer engines — ChatGPT, Perplexity, Google AI Overviews, Gemini, Claude — can find, understand, and cite it as the direct answer to a user's question. It is also called Generative Engine Optimization (GEO), AI SEO, LLM SEO, or AI Search Optimization.

How do I check if ChatGPT or Perplexity cites my website? Run geo citations --brand "Your Brand" --domain yoursite.com with a Perplexity or OpenAI API key. It asks the engine customer-style questions and reports whether your brand is mentioned, whether your domain is cited as a source, and which competitors are cited instead. Perplexity returns real source URLs; OpenAI and Anthropic reveal what the model knows about your brand. No account needed for the free web version.

Does GEO Optimizer guarantee that AI engines will cite my site? No. It measures the signals that make a site reachable, parseable, and quotable, and it tells you which ones are missing. Whether an engine cites you also depends on the question, the competition, and the engine itself — and answers change from run to run, which is why geo citations has --runs.

Is this an llms.txt generator and checker? Yes. geo llms generates an llms.txt from your sitemap, --check-drift flags stale URLs, the audit scores its presence and depth, and the Astro integration creates one at build time. There's also a free online llms.txt generator.

What is llms.txt, and does it improve AI citations? llms.txt is a Markdown file at the root of a site (/llms.txt) that gives language models a curated index of its most important pages. It is an organizational signal, not a proven ranking factor: no engine has documented that it improves citations. GEO Optimizer scores it because it is cheap to add and is intended to help AI systems read a site's structure.

Is it free and open source? Yes — MIT licensed, free forever via pip install geo-optimizer-skill or uvx. The hosted GeoReady platform runs the same engine and adds monitoring, score history, and team features.

Which AI engines does it cover? 27 AI crawlers, including ChatGPT (GPTBot, OAI-SearchBot), Perplexity (PerplexityBot), Claude (ClaudeBot, Claude-SearchBot), and Google (Googlebot, Google-Extended), plus per-platform readiness profiles for ChatGPT, Perplexity, and Google AI Overviews.

Is it free for commercial use? Yes. The MIT license permits commercial use, modification, and redistribution with no fee and no attribution beyond the license notice. Agencies can run client audits and CI gates with the CLI without any paid plan. The hosted platform is optional and separate.

Does the CLI send my data anywhere? No. The CLI fetches only the URL you audit and any AI provider you configure with your own API key (geo citations). There is no telemetry, no phone-home, and no account requirement — the audit runs entirely on your machine.

How long does an audit take? A single-URL audit runs in a few seconds. A sitemap batch depends on size and the --max-urls cap (default 25). The full test suite of 2,000+ tests runs in CI on every push across Python 3.9–3.13.

Can I use it in CI/CD? Yes. The GitHub Action fails the build below a score threshold and can emit SARIF findings for the GitHub Security tab. Any CI that runs Python works (GitLab, Jenkins, CircleCI), and geo drift --fail-on warning catches regressions between deploys.

What is the difference between GEO and AEO? Nothing in practice — they are two names for the same discipline. AEO (Answer Engine Optimization) emphasizes the answer engine as the target; GEO (Generative Engine Optimization) is the term used in the peer-reviewed research (KDD 2024). This tool uses both terms because its audience searches for both.

How is it different from a traditional SEO tool? Traditional SEO tools optimize for Google rankings and backlinks. GEO Optimizer measures AI citation readiness — whether answer engines can crawl, parse, and quote your content — using signals (llms.txt, AI-bot access, schema richness, citability, topical authority) that classic SEO tools don't score. Standard technical SEO checks (meta tags, canonical URLs, Open Graph) are audited alongside them.

What research is GEO Optimizer based on? Three peer-reviewed papers — GEO: Generative Engine Optimization (KDD 2024), AutoGEO (ICLR 2026), and C-SEO Bench (NeurIPS Datasets & Benchmarks 2025) — plus industry studies for the remaining content checks. Every check lists its source in docs/geo-methods.md.

Is there a book on Generative Engine Optimization? Yes. AI Search Engineering: A Technical Guide to Generative Engine Optimization (GEO), AI Citations, and Agent-Ready Websites is a 992-page book by Juan Camilo Auriti, the author of GEO Optimizer, published 10 September 2026 (ISBN 979-8172934568). The scoring model it describes is the one implemented in this engine. See the book.

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