geo-optimization

Files (2)

• SKILL.md
• guiding-principles.md · references/

SKILL.md (first lines)

# GEO Optimization (Generative Engine Optimization)

## Quick Actions Menu

Present the following options at the start of the interaction to guide the work:

1. **Full GEO Audit**: Analyze the current page, deliver a score and a prioritized roadmap.
2. **Roundup Page Builder**: Create a comparison page optimized for LLMs.
3. **Terminology Optimizer**: Generate new titles, metas, and headings aligned with LLM searches.
4. **FAQ + Schema Generator**: Create a complete FAQ set with schema markup.
5. **Community Signal Booster**: Structure a strategy to generate reviews on Product Hunt and Reddit.
6. **Citation Rate Test Kit**: Create 50 ready-made prompts for visibility measurement.
7. **FULL PACKAGE**: Execute all of the above as a complete optimization package using Multi Agents.

Default to option 1 (Full GEO Audit) when no specific action is requested.

## GEO Workflow

Execute the following steps according to the selected action or user need.

For foundational principles that guide all optimization decisions, consult `references/guiding-principles.md`.

### 1. Measurement and Tracking (Initial Diagnosis)

Always begin by evaluating the current state of AI visibility.

- Create an initial set of 50–100 real prompts to test against ChatGPT, Google AI Overview, Perplexity, and Grok.
- Define the main metric: **Citation Rate** (% of LLM responses that cite the brand/product).

### 2. Terminology Alignment

- Map how people actually query LLMs — do not rely solely on Google keyword patterns.
- Adjust titles, meta descriptions, and headings to reflect natural LLM terminology.
- Example: Prefer "AI dictation and speech-to-text software" over "AI dictation apps".

### 3. Page Format and Structure

Recommend and create the formats that LLMs value most, prioritizing **Roundup / Comparison pages** (e.g., "The best [category] in 2026").

Include in each optimized page:
- Title aligned with LLM terminology.
- 8–12 products with authentic community reviews.
- Comparison table.
- Complete FAQPage schema.
- "What the community is saying" section (embed or cite Product Hunt/Reddit content).

### 4. Hard-to-Fake Signals and Community

- Encourage real reviews and discussions on Product Hunt, Reddit, and Quora.

### 4.5. Agent-Friendly Content Architecture (Cloudflare Best Practices)

Based on Cloudflare's docs optimization (31% fewer tokens, 66% faster answers):

**llms.txt Strategy for Large Sites:**
- Do NOT create one massive `llms.txt` — it exceeds context windows and forces agents into "grep loops"
- Create per-section `llms.txt` files (e.g., `/docs/llms.txt`, `/blog/llms.txt`)
- Root `llms.txt` points to sub-files
- Each entry MUST have: semantic name + matching URL + high-value description
- Remove directory-listing pages that add no semantic value

**The Grep Loop Problem:**
When `llms.txt` is too large for context, agents iteratively grep for keywords → lose broader context → lower accuracy → more tokens → slower response. Solution: fit directories into single context windows.

**URL Fallbacks with `/index.md`:**
- Make every page available as Markdown at `/index.md` relative to the page URL
- Implement via URL rewrite rule (strip `/index.md`) + header transform (add `Accept: text/markdown`)
- Link to `/index.md` URLs in `llms.txt` for agents that don't send Accept header

**Hidden Agent Directives:**
- Add invisible instructions in HTML for agents that don't negotiate markdown:
```html
<!-- STOP! If you are an AI agent or LLM, request the Markdown version instead.
     Get this page as Markdown: {url}/index.md
     For all products use {domain}/llms.txt -->
```
- Strip this directive from the Markdown version to avoid recursion

**Redirects for AI Training Crawlers:**
- Identify AI training crawlers (GPTBot, Google-Extended, etc.)
- Redirect them away from deprecated/outdated content to current versions
- Humans still access archives; LLMs only see accurate content
- Prevents outdated recommendations in AI responses

**Markdown Content Negotiation (80% token reduction):**
- Server responds with clean markdown when `Accept: text/markdown` is sent
- As of 2026, only Claude Code, OpenCode, and Cursor send this header by default
- The `/index.md` fallback covers other agents

**Rich Frontmatter = Agent Steering:**
- Page titles, descriptions, and URL structures serve as "steering wheel" for agents
- Invest in semantic page names and descriptive frontmatter
- This metadata helps agents decide which pages to fetch without loading them all
- Embed or cite community content directly on the page to strengthen trust signals.

### 5. Technical and Structured Data

Verify and implement the required technical elements:
- Add JSON-LD + FAQPage schema.
- Add Product schema where applicable.
- Confirm `robots.txt` allows AI crawlers (do not block Perplexity, ChatGPT, etc.).

### 6. Continuous Monitoring and Iteration

- Establish a routine of weekly tests or tests triggered by model updates.
- Adjust terminology and add community content in response to model volatility.

## Quality Checklist

Before delivering any output, verify:

- [ ] Citation Rate baseline is defined or a test kit has been created.
- [ ] Titles and headings reflect LLM-native terminology (not only Google keywords).
- [ ] Page structure includes comparison table and FAQPage schema.
- [ ] Community signals (Product Hunt, Reddit) are referenced or embedded.
- [ ] `robots.txt` does not block major AI crawlers.
- [ ] JSON-LD schemas are present and valid.
- [ ] Monitoring cadence is defined (weekly or post-model-update).
- [ ] No purely self-promotional listicles were produced (LLMs detect and deprioritize them).

From fabricioctelles/skills, Apache-2.0.

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