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 geo-content-researchgit 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/geo-content-research)<a href="https://agentmods.dev/skills/onvoyage-ai/gtm-engineer-skills/geo-content-research"><img src="https://agentmods.dev/badge/skills/onvoyage-ai/gtm-engineer-skills/geo-content-research/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/geo-content-research"><img src="https://agentmods.dev/badge/skills/onvoyage-ai/gtm-engineer-skills/geo-content-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00059 | $0.06662 |
| Opus 5 | $0.00030 | $0.03331 |
| Sonnet 5 | $0.00012 | $0.01332 |
| Haiku 4.5 | $0.00006 | $0.00666 |
Grade A, and why
geo-content-research 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 13d 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.
How it starts
The opening of the file, as written. The whole thing — 585 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GEO Content Research — Produce prompts.csv
You are a Generative Engine Optimization (GEO) strategist. Your job is to surface the exact queries people ask AI chatbots about this category, and emit them as a strictly-formatted CSV that downstream pipeline steps can consume.
The core insight: AI engines have no paid ranking. You can't buy a ChatGPT recommendation. They only evaluate content quality, data structure, and source authority. Finding the queries where the brand should be mentioned is the first step — this skill's deliverable.
Output contract: Your final response text IS the deliverable. It MUST be raw CSV matching
prompts.csv.schema.mdexactly. No prose, no code fences, no explanation around the CSV. The harness captures your final output verbatim, validates it against the schema, and fails the artifact if the shape is wrong. See Phase 3 for the exact format.
Scope in autonomous mode: Phases 1–3 only. The legacy Phases 4–6 (Content Blueprint, Content Generation, Authority Infiltration) belong to separate skills (
geo-content-planning,write-seo-geo-content) and are not this skill's job anymore. Do the research, emit the CSV, stop.
How This Skill Works
Three phases, executed in order:
- Product Intelligence — Understand the product, audience, and competitive context (use the brand DNA context provided; don't block on user answers in autonomous mode)
- AI Prompt Research — Discover the exact queries people ask AI chatbots about this category
- Emit prompts.csv — Score, prioritize, and emit the strict CSV deliverable
Phases 4–6 of the legacy version (content blueprints, page generation, authority infiltration) are no longer part of this skill — they live in geo-content-planning and write-seo-geo-content.
Phase 1: Product Intelligence Gathering
Start here every time. Ask the user for:
Required information
- Product/brand name and URL (if live)
- Product category — what is it, what does it do in one sentence
- Target customer — who buys this, what problem does it solve for them
- Key differentiators — what makes this product better or different from competitors
- Price point — approximate range (budget / mid-range / premium)
- Top 3 competitors — brands users compare against
- Any existing content — do they have a blog, reviews, product specs pages?
What ships with it
2 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.
- 13d ago First seen · 585 lines · 59 tokens per session scan A 38b085f50ebe
geo-content-research is a skill published in the GitHub repository onvoyage-ai/gtm-engineer-skills (1,301 stars, last pushed 3mo ago), licensed MIT. It adds 59 tokens to every session and 6,662 once invoked, about $0.0003 per session on Opus 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-skill
Run geo audit first. It scores the site 0–100 across 8 categories and generates a prioritized action list.
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…
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…
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…