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<img src="assets/logo.svg" alt="Geo Optimizer" width="540"/>

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**Optimize any website to be cited by ChatGPT, Perplexity, Claude, and Gemini.**
Research-backed. Script-powered. Works in 15 minutes.

[**Docs**](https://auritidesign.it/docs/geo-optimizer/) · [**Quick Start**](#quick-start) · [**How it works**](#what-is-geo) · [**Use with AI**](#use-as-ai-context) · [**Changelog**](CHANGELOG.md)

</div>

---

## The problem nobody is talking about

AI search engines don't show a list of links. They give a direct answer and **cite their sources**.

If your site isn't optimized for this, you don't appear — even if you rank #1 on Google.

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

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

This toolkit fixes that.

---

## What's inside

```
geo-optimizer/
├── 📄 SKILL.md                     ← Choose your platform — index of ai-context/ files
│
├── 🧠 ai-context/
│   ├── claude-project.md           ← Full context for Claude Projects
│   ├── chatgpt-custom-gpt.md       ← GPT Builder system prompt (<8k chars)
│   ├── chatgpt-instructions.md     ← Custom Instructions (<1.5k chars)
│   ├── cursor.mdc                  ← Cursor rules (YAML frontmatter)
│   ├── windsurf.md                 ← Windsurf rules
│   └── kiro-steering.md            ← Kiro steering file (inclusion: fileMatch)
│
├── 📚 references/
│   ├── princeton-geo-methods.md    ← The 9 research-backed methods (+40% AI visibility)
│   ├── ai-bots-list.md             ← 16 AI crawlers (3-tier) — ready-to-use robots.txt block
│   └── schema-templates.md         ← 8 JSON-LD templates (WebSite, FAQPage, WebApp...)
│
├── 📁 docs/                        ← Full documentation (9 pages)
├── 📁 examples/                    ← Plugin examples
├── ⚙️  install.sh / update.sh      ← One-line install, one-command update
└── 📦 pyproject.toml               ← Package config, dependencies, CLI entry point
```

The package ships four CLI commands — `geo audit`, `geo fix`, `geo llms`, `geo schema` — plus an MCP server (`geo-mcp`) and a web demo (`geo-web`). Everything is a proper installable Python package.

---

## ✅ Requirements

| | |
|---|---|
| **Python** | 3.9 or higher → [python.org](https://python.org) |
| **git** | any version → [git-scm.com](https://git-scm.com) |
| **Website** | publicly accessible URL |

---

## ⚡ Quick Start

**1. Install**

```bash
# From PyPI (recommended)
pip install geo-optimizer-skill

# Or from source
git clone https://github.com/auriti-labs/geo-optimizer-skill.git
cd geo-optimizer-skill
pip install -e ".[dev]"
```

**2. Audit your site**

```bash
geo audit --url https://yoursite.com

# JSON output for CI/CD integration
geo audit --url https://yoursite.com --format json --output report.json
```

**3. Fix what's missing**

```bash
# Preview all recommended fixes (dry-run, nothing written)
geo fix --url https://yoursite.com

# Write the fix files to disk
geo fix --url https://yoursite.com --apply

# Fix only specific categories
geo fix --url https://yoursite.com --only robots,llms

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

# Generate JSON-LD schema
geo schema --type website --name "MySite" --url https://yoursite.com
```

---

## What's New in v3.x

**`geo fix` — one command to generate all missing files.** Point it at any URL and it audits the site, then generates a robots.txt patch, llms.txt, JSON-LD schemas, and meta tag recommendations in one shot. Add `--apply` to write everything to disk, or `--only robots,llms` to target specific categories.

**MCP Server** — use all audit capabilities directly from Claude, Cursor, or any MCP-compatible client. Five tools and two resources, no API keys required. See [MCP Server](#mcp-server) below.

**Citability Score** — a separate 0–100 score based on 18 research-backed methods (Princeton KDD 2024 + AutoGEO ICLR 2026 + SE Ranking 2025), measuring how citable your content is (not just whether you have the right technical setup). Returned as `result.citability.total_score` in the library API.

**16 AI bots with 3-tier classification** — training, search, and user tiers. Bingbot (Microsoft Copilot) and Claude-SearchBot added in v3.4–3.5.

**7 output formats** — text, json, rich, html, github, sarif, junit. SARIF and JUnit are useful for CI pipelines that aggregate security/quality findings.

**Plugin system** — register custom audit checks via entry points. See `examples/example_plugin.py`.

See [CHANGELOG.md](CHANGELOG.md) for full details.

---

## 📊 Sample Output

```
🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍
  GEO AUDIT — https://yoursite.com
  github.com/auriti-labs/geo-optimizer-skill
🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍 🔍

⏳ Fetching homepage...
   Status: 200 | Size: 50,251 bytes

============================================================
  1. ROBOTS.TXT — AI Bot Access
============================================================
  ✅ GPTBot allowed ✓ (OpenAI (ChatGPT training))
  ✅ OAI-SearchBot allowed ✓ (OpenAI (ChatGPT search citations))
  ✅ ClaudeBot allowed ✓ (Anthropic (Claude citations))
  ✅ PerplexityBot allowed ✓ (Perplexity AI (index builder))
  ✅ Google-Extended allowed ✓ (Google (Gemini training))
  ✅ anthropic-ai allowed ✓ (Anthropic (Claude training))
  ✅ ChatGPT-User allowed ✓ (OpenAI (ChatGPT on-demand fetch))
  ⚠️  meta-externalagent not configured (Meta AI (Facebook/Instagram AI))
  ✅ All critical CITATION bots are correctly configured

============================================================
  2. LLMS.TXT — AI Index File
============================================================
  ✅ llms.txt found (200, 6517 bytes, ~46 words)
  ✅ H1 present: # Your Site Name
  ✅ Blockquote description present
  ✅ H2 sections present: 6 (Tools, Articles, Docs...)
  ✅ Links found: 46 links to site pages

============================================================
  3. SCHEMA JSON-LD — Structured Data
============================================================
  ✅ Found 2 JSON-LD blocks
  ✅ WebSite schema ✓ (url: https://yoursite.com)
  ✅ WebApplication schema ✓ (name: Your Tool)
  ⚠️  FAQPage schema missing — very useful for AI citations on questions

============================================================
  4. META TAGS — SEO & Open Graph
============================================================
  ✅ Title: Your Site — Best Tool for X
  ✅ Meta description (142 chars) ✓
  ✅ Canonical: https://yoursite.com
  ✅ og:title ✓
  ✅ og:description ✓
  ✅ og:image ✓

============================================================
  5. CONTENT QUALITY — GEO Best Practices
============================================================
  ✅ H1: Make AI cite your website
  ✅ Good heading structure: 31 headings (H1–H4)
  ✅ Numerical data present: 15 numbers/statistics found ✓
  ✅ Sufficient content: ~1,250 words
  ⚠️  No external source links — cite authoritative sources for +40% AI visibility

============================================================
  📊 FINAL GEO SCORE
============================================================

  [█████████████████░░░] 85/100
  ✅ GOOD — Core optimizations in place, fine-tune content and schema

  Score bands: 0–40 = critical | 41–70 = foundation | 71–90 = good | 91–100 = excellent

  📋 NEXT PRIORITY STEPS:
  4. Add FAQPage schema with frequently asked questions
  7. Cite authoritative sources with external links
```

---

## 🎯 What is GEO?

**GEO (Generative Engine Optimization)** is the practice of optimizing web content to be **cited** by AI search engines — not just ranked by Google.

| Engine | Bot | What it does |
|--------|-----|-------------|
| ChatGPT Search | `OAI-SearchBot` | Retrieves and cites sources in answers |
| Perplexity AI | `PerplexityBot` | Builds an index of trusted sources |
| Claude | `ClaudeBot`, `Claude-SearchBot` | Web citations in real-time answers |
| Gemini / AI Overviews | `Google-Extended` | Powers Google's AI answers |
| Microsoft Copilot | `Bingbot` | AI-assisted search |

**Proven results — Princeton KDD 2024 (10,000 real queries on Perplexity.ai):**

```
Cite Sources method    →  up to +115% visibility
Statistics method      →  +40% average
Fluency optimization   →  +15–30%
```

> Full paper: https://arxiv.org/abs/2311.09735

---

## CLI Reference

<details>
<summary><strong>geo audit</strong> — Full GEO audit, score 0–100</summary>

```bash
# Text output (default)
geo audit --url https://yoursite.com

# Rich colored output
geo audit --url https://yoursite.com --format rich

# JSON output for CI/CD pipelines
geo audit --url https://yoursite.com --format json --output report.json

# HTML report (self-contained, shareable)
geo audit --url https://yoursite.com --format html --output report.html

# GitHub Actions annotations
geo audit --url https://yoursite.com --format github

# SARIF (for GitHub Code Scanning, SonarQube, etc.)
geo audit --url https://yoursite.com --format sarif --output report.sarif

# JUnit XML (for Jenkins, GitLab CI, etc.)
geo audit --url https://yoursite.com --format junit --output report.xml
```

**Checks:**
- robots.txt — 16 AI bots across 3 tiers (training/search/user)?
- /llms.txt — present, structured, has links?
- JSON-LD — WebSite, WebApplication, FAQPage?
- Meta tags — description, canonical, Open Graph?
- Content — headings, statistics, external citations?

**CI/CD integration example:**

```bash
# Fail the build if GEO score drops below 70
geo audit --url https://yoursite.com --format sarif --output report.sarif
geo audit --url https://yoursite.com --format json --output report.json
SCORE=$(jq '.score' report.json)
if [ "$SCORE" -lt 70 ]; then
  echo "GEO score too low: $SCORE/100"
  exit 1
fi
```

**JSON Output Structure:**
```json
{
  "url": "https://example.com",
  "timestamp": "2026-02-21T12:52:18.983151Z",
  "score": 85,
  "band": "good",
  "checks": {
    "robots_txt": {
      "score": 20,
      "max": 20,
      "passed": true,
      "details": {
        "found": true,
        "citation_bots_ok": true,
        "bots_allowed": ["GPTBot", "ClaudeBot", "PerplexityBot"],
        "bots_blocked": [],
        "bots_missing": ["Applebot-Extended"]
      }
    },
    "llms_txt": {
      "score": 20,
      "max": 20,
      "passed": true,
      "details": {
        "found": true,
        "has_h1": true,
        "has_sections": true,
        "has_links": true,
        "word_count": 559
      }
    },
    "schema_jsonld": {
      "score": 10,
      "max": 25,
      "passed": true,
      "details": {
        "has_website": true,
        "has_webapp": false,
        "has_faq": false,
        "found_types": ["WebSite", "Organization"]
      }
    },
    "meta_tags": {
      "score": 20,
      "max": 20,
      "passed": true,
      "details": {
        "has_title": true,
        "has_description": true,
        "has_canonical": true,
        "has_og_title": true,
        "has_og_description": true,
        "has_og_image": true
      }
    },
    "content": {
      "score": 15,
      "max": 15,
      "passed": true,
      "details": {
        "has_h1": true,
        "heading_count": 31,
        "has_numbers": true,
        "has_links": true,
        "word_count": 538
      }
    }
  },
  "recommendations": [
    "Add FAQPage schema with frequently asked questions"
  ]
}
```

</details>

<details>
<summary><strong>geo fix</strong> — Generate all missing fix files in one shot</summary>

`geo fix` audits the target URL, then generates everything that's missing: a robots.txt patch, a ready-to-deploy llms.txt, JSON-LD schema blocks, and meta tag recommendations. By default it's a dry-run so you can review before writing anything.

```bash
# Preview what would be generated (dry-run)
geo fix --url https://yoursite.com

# Write fix files to disk
geo fix --url https://yoursite.com --apply

# Target only specific categories
geo fix --url https://yoursite.com --only robots,llms
geo fix --url https://yoursite.com --only schema,meta
```

**Generates:**
- `robots.txt` patch — adds missing AI bot entries
- `llms.txt` — structured AI index file for your site root
- JSON-LD schema blocks — WebSite, WebApplication, FAQPage as needed
- Meta tag recommendations — description, canonical, Open Graph

</details>

<details>
<summary><strong>geo llms</strong> — Auto-generate /llms.txt from sitemap</summary>

```bash
geo llms \
  --base-url https://yoursite.com \
  --site-name "MySite" \
  --description "Free calculators for finance and math" \
  --output ./public/llms.txt
```

**Features:** auto-detects sitemap · supports sitemap index · groups URLs by category · generates structured markdown

</details>

<details>
<summary><strong>geo schema</strong> — Generate & inject JSON-LD schema</summary>

```bash
# Analyze HTML file — see what's missing
geo schema --file index.html --analyze

# Generate WebSite schema
geo schema --type website --name "MySite" --url https://yoursite.com

# Inject FAQPage schema into a file
geo schema --file page.html --type faq --inject

# Generate Astro BaseLayout snippet
geo schema --astro --name "MySite" --url https://yoursite.com
```

**Schema types:** `website` · `webapp` · `faq` · `article` · `organization` · `breadcrumb`

</details>

---

## CI/CD Integration

Run GEO audits in your CI/CD pipeline with the official GitHub Action:

```yaml
- uses: Auriti-Labs/geo-optimizer-skill@v1
  with:
    url: https://yoursite.com
```

Enforce a minimum score (fails the job if below threshold):

```yaml
- uses: Auriti-Labs/geo-optimizer-skill@v1
  with:
    url: https://yoursite.com
    threshold: 70
```

Upload results to GitHub Security tab (SARIF):

```yaml
- uses: Auriti-Labs/geo-optimizer-skill@v1
  with:
    url: https://yoursite.com
    format: sarif
```

Works on `ubuntu-latest`, `macos-latest`, and `windows-latest`. Also works with GitLab CI, Jenkins, and any CI that supports Python.

👉 **[Full CI/CD documentation →](docs/ci-cd.md)**

---

## MCP Server

Use GEO Optimizer directly from Claude, Cursor, or any MCP-compatible client.

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

Five tools are available:

| Tool | What it does |
|------|-------------|
| `geo_audit` | Full audit, returns score + recommendations |
| `geo_fix` | Generate fix files for a URL |
| `geo_llms_generate` | Generate llms.txt content |
| `geo_citability` | Citability score (Princeton methods) |
| `geo_schema_validate` | Validate JSON-LD schema |

Two resources are exposed:

| Resource | Content |
|----------|---------|
| `geo://ai-bots` | Full list of 16 tracked AI bots with tier classification |
| `geo://score-bands` | Score band definitions and their meaning |

Once connected, you can ask your AI assistant things like: *"audit my site and fix what's missing"* or *"what's my citability score and how do I improve it?"*

---

## Citability Score

Separate from the main GEO audit score, the citability score measures how well your **content** is written to be cited by AI — not just whether the technical plumbing is in place.

It's based on 18 research-backed methods (Princeton KDD 2024 + AutoGEO ICLR 2026 + SE Ranking 2025), each with a measured impact:

| Method | Impact |
|--------|--------|
| Quotation Addition | +41% |
| Statistics | +33% |
| Fluency Optimization | +29% |
| Cite Sources | +27% |
| Authoritative Tone | +15% |
| Easy-to-Understand | +12% |
| Technical Terms | +9% |
| Unique Words | +7% |
| Keyword Stuffing | ~0% |

You get back a score from 0–100 and per-method recommendations. Available via `geo audit`, the MCP `geo_citability` tool, and the Python API.

---

## 🏅 Dynamic GEO Badge

Show your GEO score in your README — updates automatically.

**SVG Badge (self-hosted):**
```markdown
![GEO Score](https://geo-optimizer-web.onrender.com/badge?url=https://yoursite.com)
```

**Shields.io Badge (via endpoint):**
```markdown
![GEO Score](https://img.shields.io/endpoint?url=https%3A%2F%2Fgeo-optimizer-web.onrender.com%2Fbadge%2Fendpoint%3Furl%3Dhttps%3A%2F%2Fyoursite.com&style=flat-square)
```

**With custom label:**
```markdown
![My Site GEO](https://geo-optimizer-web.onrender.com/badge?url=https://yoursite.com&label=My%20Site%20GEO)
```

Badge colors: 🟢 86-100 (excellent) · 🟡 68-85 (good) · 🟠 36-67 (foundation) · 🔴 0-35 (critical)

> Results are cached for 1 hour. First request may take ~30s if the service is cold.

---

## Use as a Library

```python
from geo_optimizer import audit, CitabilityResult

result = audit("https://example.com")
print(result.score)                      # 85
print(result.citability.total_score)     # 72
print(result.recommendations)            # ["Add FAQPage schema..."]
```

The core `audit()` function returns a typed `AuditResult` dataclass. No side effects, no printing — compose it however you want. All result types are exported from the top-level package.

For async use:

```python
from geo_optimizer import audit_async

result = await audit_async("https://example.com")
```

---

## 🔬 The 18 GEO Methods

Apply in this order:

| Priority | Method | Impact |
|----------|--------|--------|
| 🔴 **1** | **Cite Sources** — link to authoritative external sources | +30–115% |
| 🔴 **2** | **Statistics** — add specific numbers, %, dates, measurements | +40% |
| 🟠 **3** | **Quotation Addition** — quote experts with attribution | +30–40% |
| 🟠 **4** | **Authoritative Tone** — expert language, precise terminology | +6–12% |
| 🟡 **5** | **Fluency Optimization** — clear sentences, logical flow | +15–30% |
| 🟡 **6** | **Easy-to-Understand** — define terms, use analogies | +8–15% |
| 🟢 **7** | **Technical Terms** — correct industry terminology | +5–10% |
| 🟢 **8** | **Unique Words** — vary vocabulary, avoid repetition | +5–8% |
| ❌ **9** | **Keyword Stuffing** — proven ineffective for GEO | ~0% |

> Full detail + domain-specific data: [`references/princeton-geo-methods.md`](references/princeton-geo-methods.md)

---

## 🧠 Use as AI Context

`SKILL.md` is the index. Pick the right file for your platform from `ai-context/`:

| Platform | File | Limit |
|----------|------|-------|
| **Claude Projects** | `ai-context/claude-project.md` | No limit |
| **ChatGPT Custom GPT** | `ai-context/chatgpt-custom-gpt.md` | 8,000 chars (paid) |
| **ChatGPT Custom Instructions** | `ai-context/chatgpt-instructions.md` | 1,500 chars |
| **Cursor** | `ai-context/cursor.mdc` → `.cursor/rules/` | No limit |
| **Windsurf** | `ai-context/windsurf.md` → `.windsurf/rules/` | Plain MD + activate via UI (Always On) |
| **Kiro** | `ai-context/kiro-steering.md` → `.kiro/steering/` | No limit |

Once loaded, just ask: *"audit my site"* · *"generate llms.txt"* · *"add FAQPage schema"*

> Full setup guide: [`docs/ai-context.md`](docs/ai-context.md)

---

## Plugin System

Custom audit checks can be registered via Python entry points. This lets you add domain-specific checks without modifying the package.

```toml
# pyproject.toml
[project.entry-points."geo_optimizer.checks"]
my_check = "mypackage.checks:MyAuditCheck"
```

See `examples/example_plugin.py` for a complete working example. Use `--no-plugins` when running `geo audit` to skip all registered plugins.

---

## 🤖 GEO Checklist

Before publishing any page:

- [ ] `robots.txt` — all AI bots with `Allow: /` → [`references/ai-bots-list.md`](references/ai-bots-list.md)
- [ ] `/llms.txt` — present at site root, structured, updated
- [ ] **WebSite** schema — in global `<head>` on all pages
- [ ] **WebApplication** schema — on every tool or calculator
- [ ] **FAQPage** schema — on every page with Q&A content
- [ ] At least **3 external citations** (links to authoritative sources)
- [ ] At least **5 concrete numerical data points**
- [ ] Meta description — accurate, 120–160 chars
- [ ] Canonical URL — on every page
- [ ] Open Graph tags — og:title, og:description, og:image

---

## Running Tests

```bash
# All tests
pytest tests/ -v

# With coverage report
pytest tests/ -v --cov=geo_optimizer --cov-report=term-missing

# Single test file
pytest tests/test_core.py -v

# Single test
pytest tests/test_core.py::TestAudit::test_name -v
```

800+ tests covering core audit, CLI, security, citability, MCP, and edge cases. All use `unittest.mock` — no real network calls.

See [Codecov](https://codecov.io/gh/auriti-labs/geo-optimizer-skill) for live coverage analysis.

---

## Security

Security issues should be reported via [SECURITY.md](SECURITY.md). The package includes anti-SSRF protection with DNS pinning, thread-safe caching, path traversal validation, and XSS-safe output rendering. All URL inputs are validated against private IP ranges (RFC 1918, loopback, link-local, cloud metadata) before any network request is made.

---

## 🔬 Research Foundation

GEO Optimizer's scoring and recommendations are grounded in peer-reviewed academic research — not marketing claims.

### Core Research

| Paper | Venue | Key Finding | How We Use It |
|-------|-------|-------------|---------------|
| [GEO: Generative Engine Optimization](https://arxiv.org/abs/2311.09735) | **KDD 2024** (Princeton, Georgia Tech, AI2, IIT Delhi) | 9 optimization methods tested on 10,000 queries. Cite Sources: +115%, Statistics: +40%, Fluency: +30% | Our citability score implements all 9 methods with measured weights |
| [AutoGEO](https://arxiv.org/abs/2510.11438) | **ICLR 2026** (Carnegie Mellon) | Automatic rule extraction from generative engines. +50.99% over best Princeton baseline. Introduces GEU (utility) metric | Informs our scoring weight updates and content analysis |
| [C-SEO Bench](https://arxiv.org/abs/2506.11097) | **2025** (Puerto et al.) | Most conversational SEO methods are ineffective; gains decrease as adoption increases | Validates our focus on technical infrastructure over content manipulation |

### What This Means

We intentionally focus on **infrastructure optimization** (robots.txt, llms.txt, schema, meta tags, content structure) rather than content rewriting. The research shows that technical discoverability is a prerequisite — if AI crawlers can't find and parse your content, no amount of prose optimization matters.

The adversarial research (ETH Zurich ICLR 2025, Harvard, UC Berkeley EMNLP 2024) demonstrates that manipulative text injection degrades the ecosystem. We build tools for white-hat GEO.

---

## 📊 How It Compares

GEO Optimizer sits in a specific niche: **open-source CLI tool for technical GEO audit and fix generation**.

| | GEO Optimizer | SaaS Tools (Profound, Texta, Otterly) | geo-lint | geo-team-red/geo-optimizer |
|---|---|---|---|---|
| **What it does** | Audit infrastructure + generate fixes | Monitor brand mentions in AI answers | Lint content against GEO rules | Framework for content rewriting via LLM |
| **Approach** | Technical audit (robots.txt, llms.txt, schema, meta) | Brand tracking dashboard | Content quality rules (97 rules) | Pluggable LLM-powered optimization |
| **Requires API key** | ❌ No | ✅ Yes (paid subscription) | ❌ No | ✅ Yes (LLM API) |
| **Install** | `pip install geo-optimizer-skill` | Sign up + pay | `npm install @ijonis/geo-lint` | `go get` |
| **Time to first result** | 30 seconds | 5-10 minutes | 30 seconds | 5+ minutes |
| **Generates fix files** | ✅ Yes (`geo fix --apply`) | ❌ No (reporting only) | ❌ No (suggestions only) | ⚠️ Via LLM rewrite |
| **MCP Server** | ✅ Native | ❌ No | ❌ No | ❌ No |
| **CI/CD integration** | ✅ SARIF + JUnit | ❌ No | ⚠️ JSON output | ❌ No |
| **Research-backed scoring** | ✅ Princeton KDD 2024 | Proprietary | Partial | Partial |
| **Price** | Free (MIT) | $49-$999/month | Free (MIT) | Free (MIT) |

**Our position:** We don't compete with SaaS monitoring tools — they track *if* you're cited, we optimize *why* you'd be cited. We complement them.

---

## 📚 Resources

| | |
|---|---|
| 📖 Full Documentation | [docs/index.md](docs/index.md) |
| 📄 Princeton Paper | https://arxiv.org/abs/2311.09735 |
| 🧪 GEO-bench dataset | https://generative-engines.com/GEO/ |
| 📋 llms.txt spec | https://llmstxt.org |
| 🏗️ Schema.org | https://schema.org |
| ✅ Schema Validator | https://validator.schema.org |

---

## 👤 Author

<table>
<tr>
<td>

**Juan Camilo Auriti**
Web Developer · GEO Researcher
📧 juancamilo.auriti@gmail.com
🐙 [github.com/auriti-labs](https://github.com/auriti-labs)

</td>
</tr>
</table>

---

## 🤝 Contributing

We welcome contributions of all sizes. Here's how to get started:

- **🐛 Found a bug?** [Open a bug report](https://github.com/Auriti-Labs/geo-optimizer-skill/issues/new?template=bug_report.yml)
- **✨ Have an idea?** [Request a feature](https://github.com/Auriti-Labs/geo-optimizer-skill/issues/new?template=feature_request.yml)
- **🔍 New audit check?** [Propose a check](https://github.com/Auriti-Labs/geo-optimizer-skill/issues/new?template=new_audit_check.yml)
- **🛠️ Want to code?** Check [good first issues](https://github.com/Auriti-Labs/geo-optimizer-skill/labels/good%20first%20issue) or read [CONTRIBUTING.md](CONTRIBUTING.md)

```bash
# Set up dev environment in 60 seconds
git clone https://github.com/YOUR_USERNAME/geo-optimizer-skill.git
cd geo-optimizer-skill
pip install -e ".[dev]"
pytest tests/ -v  # 800+ tests, all mocked — no network needed
```

---

## 📄 License

[MIT](LICENSE) — free to use, modify, and distribute.

---

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