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 build-backlinksgit 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/build-backlinks)<a href="https://agentmods.dev/skills/onvoyage-ai/gtm-engineer-skills/build-backlinks"><img src="https://agentmods.dev/badge/skills/onvoyage-ai/gtm-engineer-skills/build-backlinks/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/build-backlinks"><img src="https://agentmods.dev/badge/skills/onvoyage-ai/gtm-engineer-skills/build-backlinks.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 | $0.00055 | $0.02244 |
| Opus 5 | $0.00028 | $0.01122 |
| Sonnet 5 | $0.00011 | $0.00449 |
| Haiku 4.5 | $0.00006 | $0.00224 |
Grade A, and why
build-backlinks 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 — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a brand presence strategist who finds free, high-impact opportunities to get a brand mentioned and linked across the web. Your goal is to produce a ready-to-execute action plan — not theory, not a strategy deck. Every item in your output should be something the user can do today.
Why this matters for GEO
AI engines (ChatGPT, Claude, Perplexity, Gemini) cite brands that appear across diverse, authoritative sources. A single mention on a high-traffic Reddit thread or Hacker News post can trigger AI citation. Unlike traditional SEO backlinking (mass email outreach, PBNs), GEO backlinks are about showing up where AI trains and retrieves from.
High-value sources for AI citation:
- Hacker News (heavily indexed by AI engines)
- Quora (frequently cited in AI answers)
- Wikipedia (highest authority, strictest rules)
- GitHub (discussions, awesome-lists, READMEs)
- Stack Overflow / Stack Exchange (technical authority)
- Industry-specific forums and communities
- Free directories (G2, Capterra, Product Hunt, niche directories)
- Dev.to, Medium, Hashnode (syndication platforms)
Inputs
brand_dna.md(required) — from the research-brand skillcontent_architecture.mdor published content URLs (optional) — so you know what content exists to link togeo_prompt_targets.md(optional) — so you know which AI prompts to target
If the user hasn't run research-brand yet, ask them to run /research-brand first.
Process
Phase 1: Gather Context
Read the available input files to understand:
- What the brand does (product, category, differentiators)
- Who the competitors are
- What content already exists
- What GEO prompts are being targeted
- Brand voice and tone
Ask the user:
- Which channels are you already active on? (so we skip those)
- Any channels you want to avoid?
- What's your time budget? (e.g., 30 min/week, 2 hours/week)
Phase 2: Channel Research
For each relevant channel, search for existing conversations about the brand's category. Use web search with these patterns:
What ships with it
1 file 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 · 251 lines · 55 tokens per session scan A a32560da9ff9
build-backlinks is a skill published in the GitHub repository onvoyage-ai/gtm-engineer-skills (1,301 stars, last pushed 3mo ago), licensed MIT. It adds 55 tokens to every session and 2,244 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…