Geo Fundamentals
wasp-lang/wasp
Generative Engine Optimization for AI search engines (ChatGPT, Claude, Perplexity).
When the user wants to optimize for AI search visibility (ChatGPT, Claude, Perplexity, AI Overviews).
Install Generative Engine Optimization
$ npx skills add kostja94/marketing-skills --skill generative-engine-optimization -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install kostja94/marketing-skills generative-engine-optimization --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/kostja94/marketing-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/strategies/commercial/geo .claude/skills/generative-engine-optimization && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "generative-engine-optimization" agent skill from https://github.com/kostja94/marketing-skills/tree/main/skills/strategies/commercial/geo into .claude/skills/generative-engine-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "generative-engine-optimization", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/kostja94/marketing-skills/tree/main/skills/strategies/commercial/geoType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add kostja94/marketing-skills --skill generative-engine-optimization -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install kostja94/marketing-skills generative-engine-optimization --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kostja94/marketing-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/strategies/commercial/geo .agents/skills/generative-engine-optimization && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "generative-engine-optimization" agent skill from https://github.com/kostja94/marketing-skills/tree/main/skills/strategies/commercial/geo into .agents/skills/generative-engine-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "generative-engine-optimization", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add kostja94/marketing-skills --skill generative-engine-optimization -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install kostja94/marketing-skills generative-engine-optimization --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kostja94/marketing-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/strategies/commercial/geo .cursor/skills/generative-engine-optimization && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "generative-engine-optimization" agent skill from https://github.com/kostja94/marketing-skills/tree/main/skills/strategies/commercial/geo into .cursor/skills/generative-engine-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "generative-engine-optimization", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/kostja94/marketing-skills.git --path skills/strategies/commercial/geo--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add kostja94/marketing-skills --skill generative-engine-optimization -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install kostja94/marketing-skills generative-engine-optimization --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kostja94/marketing-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/strategies/commercial/geo .gemini/skills/generative-engine-optimization && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "generative-engine-optimization" agent skill from https://github.com/kostja94/marketing-skills/tree/main/skills/strategies/commercial/geo into .gemini/skills/generative-engine-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "generative-engine-optimization", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install kostja94/marketing-skills generative-engine-optimizationInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add kostja94/marketing-skills --skill generative-engine-optimization -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/kostja94/marketing-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/strategies/commercial/geo .github/skills/generative-engine-optimization && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "generative-engine-optimization" agent skill from https://github.com/kostja94/marketing-skills/tree/main/skills/strategies/commercial/geo into .github/skills/generative-engine-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "generative-engine-optimization", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add kostja94/marketing-skills --skill generative-engine-optimization -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install kostja94/marketing-skills generative-engine-optimization --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/kostja94/marketing-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/strategies/commercial/geo .opencode/skills/generative-engine-optimization && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "generative-engine-optimization" agent skill from https://github.com/kostja94/marketing-skills/tree/main/skills/strategies/commercial/geo into .opencode/skills/generative-engine-optimization/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "generative-engine-optimization", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
generative-engine-optimization- GitHub stars
- 1k
- Token cost
- ~2.9k tokens
- SKILL.md length
- 1,259 words
- Files
- 1
- Skills in repo
- 102
- Repo updated
- First seen
- Licence
- MIT
At a glance
When the user wants to optimize for AI search visibility (ChatGPT, Claude, Perplexity, AI Overviews).
- Wants to optimize for AI search visibility (ChatGPT
- SKILL.md covers Scope, GEO vs. SEO, AI Search Platforms (SERP… and How GEO Works (RAG & Search…, plus 7 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- The user mentions GEO
What it does
Generative Engine Optimization is an agent skill from kostja94/marketing-skills. When the user wants to optimize for AI search visibility (ChatGPT, Claude, Perplexity, AI Overviews). Also use when the user mentions "GEO," "AEO," "generative engine optimization," "AI search visibility," "LLM optimization," "GitHub GEO," "Grokipedia," "optimize for ChatGPT," "AI Overviews," "Bing Copilot," "Yandex AI," "Perplexity optimization," "GEO strategy," or "AI search optimization." For third-party publishing strategy (which platforms to use), use parasite-seo. For GitHub repos, README, and Awesome…
Its SKILL.md is about 2.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Marketing & SEO, covering AI search optimization. It works with OpenAI, Perplexity and GitHub. The repository describes itself as: Agent Skills for Marketing — SEO, Social, Influencer & More. 160+ open-source skills for SEO, content, 40+ page types, paid ads, channels, and strategies. Add project context… The licence is MIT.
When your agent uses it
- Wants to optimize for AI search visibility (ChatGPT
- The user mentions GEO
- Generative engine optimization
- AI search visibility
Example prompts
- “generative engine optimization,”
- “AI search visibility,”
- “LLM optimization,”
- “/generative-engine-optimization”
What it can do on your machine
Read from SKILL.md and the folder at commit 8dd89c5. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
geneo.appgeoaiomarketing.comvercel.com
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Generative Engine Optimization loads about 2.9k tokens when it runs. Until then it costs about 178 tokens; SKILL.md has 1,259 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from kostja94/marketing-skills at commit 8dd89c5, republished under its MIT licence (© kostja94). 1,259 words, ~2,908 tokens.
Download SKILL.mdSave it as
.claude/skills/generative-engine-optimization/SKILL.md (or your agent's skills folder).- name
- generative-engine-optimization
- description
- When the user wants to optimize for AI search visibility (ChatGPT, Claude, Perplexity, AI Overviews). Also use when the user mentions "GEO," "AEO," "generative engine optimization," "AI search visibility," "LLM optimization," "GitHub GEO," "Grokipedia," "optimize for ChatGPT," "AI Overviews," "Bing Copilot," "Yandex AI," "Perplexity optimization," "GEO strategy," or "AI search optimization." For third-party publishing strategy (which platforms to use), use parasite-seo. For GitHub repos, README, and Awesome lists, use github. For Medium.com only, use medium-posts. For Grokipedia edits, use grokipedia-recommendations. For traditional Google SERP strategy, use seo-strategy.
- metadata.version
- 1.4.0
Strategies: GEO (Generative Engine Optimization)
Guides GEO/AEO strategy for AI search visibility. GEO optimizes content for ChatGPT, Claude, Perplexity, and AI search summaries (Google AI Overviews, Bing Copilot, Yandex Search with AI)—getting cited in AI-generated answers rather than ranking in traditional SERPs. See serp-features for AI search as SERP features; featured-snippet for snippet optimization that overlaps with AI Overviews.
When invoking: On first use, if helpful, open with 1-2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Scope
- GEO = Generative Engine Optimization
- AEO = Answer Engine Optimization
- LLMO = Large Language Model Optimization
- AIO = Artificial Intelligence Optimization
All refer to the same goal: visibility in AI assistant responses.
GEO vs. SEO
| Dimension | SEO | GEO |
|---|---|---|
| Goal | Rankings in search results | Citations in AI answers |
| User path | Click → visit → convert | Answer in-place; may not visit |
| Content | Full page optimization | Clear, citable paragraphs |
| Metrics | Clicks, traffic | Citations, brand mentions |
| Platforms | Google, Bing, Yandex (organic) | AI Overviews, Copilot, Yandex AI, ChatGPT, Perplexity |
Both matter: Create content that ranks and gets cited. AI search summaries (AI Overviews, Copilot, Yandex AI) are SERP features—see serp-features. When SERP features cause zero-click (user gets answer without clicking), citation becomes the primary value; optimize for being cited, not just ranked.
AI Search Platforms (SERP Features + Standalone)
| Platform | Type | Source Selection | Optimization Focus |
|---|---|---|---|
| Google AI Overviews | SERP feature | Top 10–12 organic; Gemini; favors older domains (49% over 15 yrs) | Traditional SEO; structured data; citable blocks |
| Bing Copilot Search | SERP feature | Bing index; GPT-4; 9.81% domain overlap with Google; favors younger domains (18.85%); LinkedIn signals for B2B | Bing optimization; LinkedIn presence; structured content |
| Yandex Search with AI / Neuro | SERP feature | Real-time Yandex search; YandexGPT; Russia-focused | Yandex indexing; Russian content; cited sources |
| Perplexity | Standalone | 200B+ URL index; independent crawl; favors recency, semantic alignment | Content freshness; semantic markup; mid-tier site opportunity |
| ChatGPT (web search) | Standalone | GPTbot; high-authority, frequently updated, LLM-friendly; favors older domains (45.8%) | Backlinks; structured data; authority signals |
Citation behavior: AI Overview citations 20–35% higher CTR than equivalent organic. Copilot: shortest responses, fewest links (~3.13/response). Perplexity: prominent URL citations, high trackability. Geneo, GEO AIO
Platform traffic context: Among standalone AI tools, ChatGPT captures ~60%+ of independent Gen AI traffic, Gemini ~20%+, while Claude, Perplexity, and Grok each account for ~2-4%+. These tools reach users directly. SERP features (AI Overviews, Copilot) reach users through existing search flows rather than as independent destinations. Prioritize optimization effort proportionally.
How GEO Works (RAG & Search Supply)
GEO operates through RAG (Retrieval-Augmented Generation)—AI tools retrieve content first, then generate answers. The retrieval supply type varies by platform and determines which content surfaces for citation.
Retrieval Supply Types
| Type | Description | Platforms |
|---|---|---|
| Self-built index | Platform maintains its own crawl and search index | Perplexity (200B+ URL index, PerplexityBot); ChatGPT (OAI-SearchBot index) |
| Bound search engine | Platform uses a fixed first-party search API | Copilot (Bing); Google AI Overviews / AI Mode (Google Search + query fan-out) |
| Third-party API | Platform contracts a third-party search API | Claude for Government (Brave Search API); smaller AI tools using Tavily, Exa, You.com |
| Hybrid | Combination of self-built + external API | ChatGPT (OAI-SearchBot + possible search partners); Claude Web Search (supplier not publicly disclosed) |
Platform Retrieval & Implications
| Platform | Primary Supply | Strategic Implication |
|---|---|---|
| Google AI Overviews / AI Mode | Google Search (query fan-out) | Strong traditional SEO + structured data is the most reliable path |
| Bing Copilot | Bing index | Requires Bing indexing; LinkedIn signals for B2B visibility |
| ChatGPT (web search) | OAI-SearchBot + partners | High-authority, frequently updated content favored; backlinks matter |
| Perplexity | Proprietary crawl | Content freshness; semantic alignment; mid-tier sites have opportunity |
| Claude (web search) | Not publicly disclosed | Focus on general crawlability and clear structured content |
Third-party search APIs (Tavily, Exa, You.com, Brave Search API) feed smaller AI tools and custom agents. Content that is crawlable and indexed via standard web search reaches these APIs through their supply indexes. Core model training (long, costly, not widely actionable): focus on RAG optimization.
AI Crawlers & Discovery
AI crawlers fall into three categories with different implications for content strategy:
| Type | Purpose | Examples | Implication |
|---|---|---|---|
| Training crawlers | Gather data for model training | GPTBot, ClaudeBot, Google-Extended, Meta crawlers | Blocking via robots.txt prevents training data use; does not affect real-time search/retrieval by the same provider |
| Index/RAG crawlers | Build search index for retrieval | OAI-SearchBot, PerplexityBot, Claude-SearchBot, Bytespider (Cohere), AppleBot | Must allow for RAG-based AI citation; critical for GEO |
| Real-time crawlers | Fetch content on-demand at query time | ChatGPT-User (opt-in via web search) | Content must be accessible without a login; paywalls may block citation |
Content discovery: "Push" submissions for general web content are primarily supported through Bing IndexNow. Google's Indexing API is limited to JobPosting and BroadcastEvent pages only (not general content). OpenAI, Anthropic, Perplexity, xAI, Meta, and DeepSeek do not offer public submission portals—their crawlers discover content through standard crawl and sitemaps only.
AI crawlers generally do not execute JavaScript—critical content must be in initial HTML. See rendering-strategies for SSR, SSG, CSR; site-crawlability for AI crawler optimization; robots-txt for allow/block decisions. Vercel/MERJ study (2024)
Show full SKILL.md (433 more words)Show less
Content Best Practices
| Practice | Purpose |
|---|---|
| Direct-answer format | Answer specific questions in clear paragraphs |
| Entity signals | Clear brand, product, author identity; see entity-seo |
| Citable paragraphs | Each block understandable on its own |
| Distribution | Website, YouTube (Google prioritizes YouTube in search; ~78% of social media citations in AI Overviews come from YouTube + Reddit), forums, Reddit—thoughtful comments can outrank blog posts |
Article-Level GEO
For blog posts and articles, structure content for AI citation. Studies find content with TL;DR, structured formats, and clear answers is cited substantially more by AI engines.
| Element | Guideline |
|---|---|
| TL;DR or Key Takeaways | Choose one: TL;DR = 50–100 word bold summary paragraph; Key Takeaways = 5–7 bullet points; placed after intro |
| QAE pattern | Question (H2) → Answer (2 sentences) → Evidence (data, examples, lists) |
| Answer-first | Direct answer in first 40–60 words after each H2 |
| Answer blocks | 100–200 words per section; direct answer + context + evidence + nuance |
| Structured formats | Lists, tables, numbered steps increase citation rate |
See article-content for content creation; article-page-generator for page structure.
Parasite SEO & High-Authority Platforms
Parasite SEO = Placing content on high-authority platforms to leverage their domain strength for rankings and AI citation. See parasite-seo for full strategy.
GitHub: Tier 2 technical authority; very high AI citation. See github for repos, README, Pages, gists, awesome lists.
YouTube: Google prioritizes YouTube in search; YouTube citations in AI Overviews surged 25.21%. Long-form instructional and visual-demo videos dominate. See youtube-seo for channel and video optimization; video-optimization for website-embedded video SEO.
Grokipedia: xAI's AI encyclopedia; ChatGPT, Perplexity, Copilot cite it. See grokipedia-recommendations for adding recommendations or links. Contribute genuinely useful content; avoid manipulative placement (Google Site Reputation Abuse policy).
Tools
- GEO tracking and optimization tools for measuring AI citation and visibility
Key Insight
ChatGPT traffic converts at significantly higher rates than Google search—studies report 2x to 9x uplift depending on industry. AI tool users often have clearer intent, but results vary by vertical.
Output Format
- Content structure for AI citation
- Entity optimization; see entity-seo
- Distribution strategy
- Measurement approach
Related Skills
- site-crawlability: AI crawler optimization; URL/redirect management
- rendering-strategies: SSR, SSG, CSR; content in initial HTML for AI crawlers
- robots-txt: AI crawler allow/block (GPTBot, ClaudeBot, PerplexityBot)
- parasite-seo: Parasite SEO strategy; high-authority platforms for GEO
- github: GitHub for GEO; repos, README; Tier 2 technical authority
- youtube-seo: YouTube optimization; GEO distribution; Google prioritizes YouTube
- serp-features: Strongly related—AI Overviews, Bing Copilot, Yandex AI; platform comparison
- featured-snippet: Snippet optimization; overlaps with AI Overviews
- entity-seo: Entity signals; Organization, Person schema; GEO citation
- article-content: Article body creation; TL;DR, Key Takeaways, QAE pattern
- article-page-generator: Article page structure; schema; layout
- faq-page-generator: FAQ structure for GEO; citable Q&A blocks; content in initial HTML
- howto-section-generator: HowTo step sections; citable ordered procedures; HowTo JSON-LD
© kostja94, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in skills/strategies/commercial/geo of kostja94/marketing-skills.
Open the folder on GitHubat commit 8dd89c5
Compare with similar skills
Generative Engine Optimization next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Generative Engine Optimization this skillkostja94/marketing-skills | 1k | — | ~2.9k | Automated safety check: Pass | MIT | |
| Geo Fundamentalswasp-lang/wasp | 19k | 8 repos | ~861 | Automated safety check: Pass | MIT | |
| SEO GeoReScienceLab/opc-skills | 1.8k | 4 repos | ~2.1k | Automated safety check: Pass | Apache-2.0 | |
| GEO-First SEO Audit Toolzubair-trabzada/geo-seo-claude | 11k | — | ~2.8k | Automated safety check: Notes | MIT | |
| Fire Your SEO Agencyleopard627/fire-your-seo-agency | 706 | — | ~1.1k | Automated safety check: Pass | MIT | |
| Marketing OsYuzzyuk/marketing-os | 535 | — | ~2.5k | Automated safety check: Pass | MIT |
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Questions about Generative Engine Optimization
What does Generative Engine Optimization do?
When the user wants to optimize for AI search visibility (ChatGPT, Claude, Perplexity, AI Overviews). Generative Engine Optimization is an agent skill from kostja94/marketing-skills. When the user wants to optimize for AI search visibility (ChatGPT, Claude, Perplexity, AI Overviews).
When should I use Generative Engine Optimization?
Generative Engine Optimization fits situations like: wants to optimize for AI search visibility (ChatGPT; the user mentions GEO; generative engine optimization; AI search visibility.
How do I install Generative Engine Optimization in Claude Code?
Run `npx skills add kostja94/marketing-skills --skill generative-engine-optimization -a claude-code`. Or copy the skill folder (skills/strategies/commercial/geo in kostja94/marketing-skills) into .claude/skills/generative-engine-optimization in your project. Claude Code loads it when a task matches its description.
How do I install Generative Engine Optimization in Codex?
Run `npx skills add kostja94/marketing-skills --skill generative-engine-optimization -a codex`. Or copy the skill folder (skills/strategies/commercial/geo in kostja94/marketing-skills) into .agents/skills/generative-engine-optimization in your project. Codex loads it when a task matches its description.
Can I use Generative Engine Optimization in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add kostja94/marketing-skills --skill generative-engine-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/generative-engine-optimization, .gemini/skills/generative-engine-optimization, .github/skills/generative-engine-optimization and .opencode/skills/generative-engine-optimization in your project.
What does Generative Engine Optimization need to run?
SKILL.md names no scripts, command-line tools or credentials: Generative Engine Optimization is instructions for the agent only.
Does Generative Engine Optimization access the network?
SKILL.md names 3 domains. As links in the text: geneo.app, geoaiomarketing.com and vercel.com. This is read from the text; nothing was executed.
Is Generative Engine Optimization safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Generative Engine Optimization use?
Generative Engine Optimization is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Generative Engine Optimization use?
About 2.9k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Generative Engine Optimization?
Skills that share tags, products or a category with Generative Engine Optimization: Geo Fundamentals (wasp-lang/wasp, 19k stars), SEO Geo (ReScienceLab/opc-skills, 1.8k stars), GEO-First SEO Audit Tool (zubair-trabzada/geo-seo-claude, 11k stars) and Fire Your SEO Agency (leopard627/fire-your-seo-agency, 706 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Generative Engine Optimization?
kostja94 (a GitHub user) maintains it in kostja94/marketing-skills, which has 1,016 GitHub stars. The repository holds 102 skills in this directory. The repository was last updated on October 6, 2026.
Source: kostja94/marketing-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.