Ai Search Optimization

Organization

social-media-skills

ai-search-optimization

Use to get a brand and its content CITED and RECOMMENDED by AI answer engines — the GEO (Generative Engine Optimization) / AI-search-visibility skill. Run when the user says "GEO," "get cited by ChatGPT/Perplexity/Google AI," "ChatGPT SEO," "LLM SEO / LLMO," "AI Overviews," "answer engine optimization (AEO)," "will AI recommend my brand," or wants to show up in AI-generated answers, not just the feed or Google links. Reads brand-profile and audience first. AI engines retrieve + fan-out and cite community/social sources heavily (Reddit, YouTube, Wikipedia); platforms disagree; earned media beats product pages; extractable, fresh content drives citation. Covers the four GEO levers (retrievable, earned mentions, extractable content, entity clarity), authentic social plays, and the manual prompt-audit method. Refuses astroturfing; never fabricates "share of voice." Sibling of social-seo (platform + Google search). Judges via the audit + GEO tools + AI referral traffic.

Overview

Publisher	social-media-skills
Repository	`skills`
Skill name	`ai-search-optimization`
Stars	215
Forks	44
Bundled files	5
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
5 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by social-media-skills on GitHub. Read the source before you install it.

Installation

Install the Ai Search Optimization AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/social-media-skills/skills/tree/main/skills/ai-search-optimization
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/social-media-skills/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/ai-search-optimization .claude/skills/ai-search-optimization

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ai Search Optimization in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ai Search Optimization on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ai Search Optimization is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AI Search Optimization (GEO)

Get your brand into the answer when people ask ChatGPT, Perplexity, Google AI, Gemini, or Copilot a question in your space — instead of watching a competitor get named. This is GEO (Generative Engine Optimization; also AEO/LLMO): optimizing to be cited and recommended by AI engines. It supplements search/SEO; it doesn't replace it.

Four truths shape everything:

AI answers are built by retrieval + fan-out. Engines retrieve live from search indexes (ChatGPT via OpenAI's own crawler/index, OAI-SearchBot — historically Bing-seeded; Google feeds AI Overviews/AI Mode) and split your topic into sub-queries — so ranking in search feeds AI citation, and you optimize for a constellation of questions.
AI cites community/social sources most. Reddit, YouTube, Wikipedia, LinkedIn, listicles and review sites dominate citations — the cited pages are usually not your pages. Earned mentions beat product pages.
Platforms disagree. ChatGPT skews Wikipedia, Perplexity skews Reddit, AI Overviews lean on E-E-A-T + the community web. Optimizing for one ≠ all.
Extractable, fresh, well-sourced content gets quoted. Quotations, statistics, citations, Q&A structure, and schema lift citation; stale content gets displaced.

(Full mechanics: references/how-ai-engines-cite.md.)

Step 0 — Read the foundation + the goal

Load brand-profile.md and audience.md (entity clarity + the real questions matter). Identify the queries the user wants to be recommended for and the engines their audience uses.

Step 1 — Run the prompt-audit (always start here)

Ask the user's 10–30 buyer-intent queries (plus fan-out sub-questions) across ChatGPT / Perplexity / Gemini in fresh sessions; document whether the brand appears, how it's described, and which sources are cited. The cited sources are the strategy; the gaps are the content list. This is the honest ground-truth method — see references/audit-and-measurement.md.

Step 2 — Be retrievable (the foundation)

If you can't be found in search, you can't be cited: rank in Google/Bing and in platform search → social-seo (the sibling). Same keyword/question research powers both. And verify AI retrieval crawlers can reach the site — robots.txt and CDN/bot-protection defaults (e.g. Cloudflare) often block OAI-SearchBot / ChatGPT-User / PerplexityBot / Claude's bots unintentionally.

Step 3 — Earn brand mentions across cited sources (the social core)

Where AI looks most — done authentically: valuable Reddit participation in buyer-intent threads; YouTube with brand + keywords in titles/transcripts (a top AI-Overview signal); LinkedIn expertise; Quora; earned "best [X]" listicle and review-site (G2/Trustpilot) inclusion; relationship-driven PR. The goal is a web of mutual verification. See references/the-geo-levers.md.

Step 4 — Make content extractable

So a model can lift a clean claim: lead with a TL;DR answer, question-shaped headings, lists/ tables, quotations + verifiable stats + citations (the research-backed levers), FAQ/Article schema, named author + dates, and keep it fresh (citations decay). (This lever spans your website/blog too — broader than social; pair with social-seo.)

Step 5 — Build entity clarity

Give the model a clean entity to recommend: a consistent one-line description across site/profiles/ listings → brand-profile; Wikipedia/Wikidata if genuinely notable; claimed listings + consistent NAP; a corroborated "the X for Y" position.

Step 6 — Measure (honestly) + the boundary

Re-run the audit monthly (expect a multi-week lag; judge over quarters), optionally add a GEO tracking tool, and watch AI referral traffic (chatgpt/perplexity referrers). Never fabricate a "share of voice" or citation %. No WoopSocial analytics. Sibling boundary: social-seo = found in platform + Google search; this = cited by AI answer engines.

Orchestration map

ai-search-optimization sets the AI-visibility layer; it routes to / pairs with: social-seo (retrieval/search foundation — sibling) · brand-profile (entity) · content-pillars (question clusters) · reels-script / the growth skills (the YouTube/Reddit/LinkedIn content that earns mentions) · viral-reverse-engineering (what gets cited/shared) · scheduling-and-queue (publish).

Quality bar — self-check

Did I start with the prompt-audit and let the cited sources drive strategy?
Did I apply the four levers (retrievable → earned mentions → extractable → entity), foregrounding the community/social plays?
Did I respect that AI cites earned/community sources over product pages, and that platforms differ?
Did I keep it authentic (refuse astroturfing/fake reviews) and never fabricate share-of-voice numbers?
Did I hand the search/retrieval foundation to social-seo, note GEO spans the web too, and use audit/tools/referral measurement (no WoopSocial analytics)?

Edge cases & pushback

"Flood Reddit / buy reviews" → refuse astroturfing; it's detectable, removed, and trust-destroying → authentic participation + earned reviews.
"Tell me my AI share of voice %" → can't see inside models; run the audit / a tool; don't invent.
"Just optimize my product page" → that's ~3% of it; most citations are earned/community sources.
"Optimize for AI search" (one thing) → engines differ (ChatGPT≠Perplexity≠AI Overviews); pick the field.
"Is this my TikTok/Google SEO?" → related but distinct → social-seo owns platform/Google search.
"Does WoopSocial track this?" → no; measure via audit + GEO tools + referral analytics.
AI-generated content dump → AI down-weights low-quality AI content; needs human judgment + sources.

Related skills

social-seo — the sibling: platform + Google search (the retrieval foundation AI pulls from).
brand-profile — the entity/positioning AI must understand; content-pillars — question clusters.
reddit-marketing — the how of credible Reddit participation (the top AI-citation source).
reels-script, instagram-growth/tiktok-growth/linkedin-growth — the YouTube/Reddit/LinkedIn content that earns the mentions AI cites.
viral-reverse-engineering — what gets shared/cited; scheduling-and-queue — publish.

References

references/how-ai-engines-cite.md — RAG + query fan-out, which sources get cited, per-engine differences, freshness/decay.
references/the-geo-levers.md — the four levers (retrievable · earned mentions/social plays · extractable · entity), with the research-backed lifts.
references/audit-and-measurement.md — the manual prompt-audit method, GEO tools, referral traffic, honesty rules.
references/examples.md — a worked audit + Reddit/YouTube/extractability/entity plays + honest scope.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ai Search Optimization AI skill do?

Why use Ai Search Optimization on TypingMind?

Because you install it once and use it with any model. Ai Search Optimization is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ai Search Optimization in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/social-media-skills/skills/tree/main/skills/ai-search-optimization. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ai Search Optimization?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ai Search Optimization?

As many as you like. As long as a model supports skills, you can use Ai Search Optimization with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ai Search Optimization AI skill free?

Yes. It is published on GitHub by social-media-skills under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

AI Search Optimization (GEO)

Step 0 — Read the foundation + the goal

Step 1 — Run the prompt-audit (always start here)

Step 2 — Be retrievable (the foundation)

Step 3 — Earn brand mentions across cited sources (the social core)

Step 4 — Make content extractable

Step 5 — Build entity clarity

Step 6 — Measure (honestly) + the boundary

Orchestration map

Quality bar — self-check

Edge cases & pushback

Related skills

References

Bundled files

Frequently asked questions

Related AI Skills

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