GTM Engineer Skills is a collection of agent workflows that research brands and markets, plan searchable content, audit websites for visibility in AI-generated answers, and produce related marketing files or code changes. Marketing and growth operators use it to improve how websites are discovered, cited, and understood by search engines and AI assistants. The catalogue entries are the project's individual skills.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add onvoyage-ai/gtm-engineer-skills --skill improve-aeo-geogit clone --depth 1 https://github.com/onvoyage-ai/gtm-engineer-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/onvoyage-ai/gtm-engineer-skills/improve-aeo-geo)<a href="https://agentmods.dev/skills/onvoyage-ai/gtm-engineer-skills/improve-aeo-geo"><img src="https://agentmods.dev/badge/skills/onvoyage-ai/gtm-engineer-skills/improve-aeo-geo/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/onvoyage-ai/gtm-engineer-skills/improve-aeo-geo"><img src="https://agentmods.dev/badge/skills/onvoyage-ai/gtm-engineer-skills/improve-aeo-geo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Output Handling · line 325 Model output is used without validation or sanitization. Unvalidated output injected into downstream contexts (SQL, shell, HTML) enables injection attacks and arbitrary code execution.Fix: Validate and sanitize all model output before using it in downstream contexts. Use parameterized queries for SQL, shell quoting for commands, and HTML encoding for web output.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00061 | $0.05565 |
| Opus 5.5 | $0.00024 | $0.02226 |
| Sonnet 5.5 | $0.00012 | $0.01113 |
| Haiku 4.5 | $0.00006 | $0.00556 |
Grade A, and why
improve-aeo-geo scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- improve-aeo-geo — 97% identical, 50 lines differ
How it starts
The opening of the file, as written. The whole thing — 561 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Improve Website AEO/GEO Skill
You are an expert at AI Engine Optimization (AEO) and Generative Engine Optimization (GEO). When invoked, you analyze the user's website codebase and make concrete, actionable code changes so AI agents — ChatGPT, Claude, Perplexity, Google AI Overviews, and others — can better discover, parse, quote, and cite the site.
The web is shifting from human-first to AI-first discovery. AI agents don't browse like humans. They extract structured data, scan for direct answers, and decide in milliseconds whether content is worth citing. This skill makes websites visible to that new audience.
Workflow
When invoked on a codebase, follow this exact sequence:
Step 1: Baseline
- If the user has a live URL, get a baseline score first. Run the
audit-website-aeoskill for a local crawl + scored report, or use the hosted aeo-audit.sh. If anaeo_audit_report.mdalready exists, read it — its prioritized fixes and weakest pages tell you exactly what to fix. - If no URL is available, proceed with a code-level audit.
Step 2: Discover the stack
- Identify the framework (Next.js, Nuxt, Astro, SvelteKit, Remix, WordPress, Hugo, Jekyll, 11ty, plain HTML)
- Find where
<head>is managed (layout files, document components, plugins, theme files) - Find where content lives (pages, MDX/MD files, CMS templates, components, PHP templates)
- Check for existing SEO plugins/packages (next-seo, @astrojs/sitemap, Yoast, etc.)
- Check if the site uses SSR, SSG, or client-side rendering
Step 3: Audit existing state
Run through all checks below. For each failing check, note the file(s) to modify and the specific fix.
Step 4: Fix in priority order
Apply changes starting with Priority 1 (blockers), then work down. Make the smallest, most targeted changes needed.
Step 5: Verify
- Re-run the audit (
audit-website-aeoskill, or aeo-audit.sh) and compare against the Step 1 baseline to confirm the score improved - Target: 80+ overall score (B+ grade or higher)
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 561 lines · 61 tokens per session scan A a2d27da45090
improve-aeo-geo is a skill published in the GitHub repository onvoyage-ai/gtm-engineer-skills (1,339 stars, last pushed 16d ago), licensed MIT. It adds 61 tokens to every session and 5,565 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
bytesagain-geo-seo
GEO SEO — Generative Engine Optimization reference. Complete guide for optimizing websites for AI search engines. Covers llms.txt spec, GEO checklist, automated scanning, AI bot tracking with nginx, AEO (Answer Engine Optimization) for getting cited by ChatGPT/Perplexity/Gemini, citation monitoring, and content…
geo-optimizer
Audit and fix a website for AI search visibility (Generative Engine Optimization). Use when asked to check whether a site or page can be found and cited by ChatGPT Search, Perplexity, Claude or Google AI Overviews, to score it 0-100 with geo audit, or to fix robots.txt AI crawler rules, llms.txt, JSON-LD schema, meta…
backlink-outreach
Find, evaluate, pitch and track natural backlink and content partnerships. Prospects come from the GEO citation data rather than a generic blog search: the targets are the pages an AI already cites when answering your category questions. Research runs on the Monid tool layer (web search and scrape, authority metrics…
content-thicken
Evidence-driven blog driver. Takes ONE target from the fused search plus AI-answer content library, pulls the real questions it has to answer, drafts a thick long-form guide against a template contract, validates it deterministically, and stops at preview. Two modes, thicken an already-earning post IN PLACE (never…
geo-monitor
Run and read the GEO pipeline, which measures whether AI answer engines mention, recommend and cite your site. Puts a fixed registry of real user questions to an answer engine through the Monid tool layer, detects the three signals plus competitors, and turns "a rival is named and we are not" into a tracked work…
seo-intake
Run and read the SEO intake pipeline. Pulls the organic keyword set for your domain and for each competitor through the Monid tool layer, computes the gap locally, and routes every keyword to the one action that can help it (write-new / striking / build-depth / defend / noise). Use when asked to refresh the SEO queue…