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 research-brandgit 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/research-brand)<a href="https://agentmods.dev/skills/onvoyage-ai/gtm-engineer-skills/research-brand"><img src="https://agentmods.dev/badge/skills/onvoyage-ai/gtm-engineer-skills/research-brand/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/research-brand"><img src="https://agentmods.dev/badge/skills/onvoyage-ai/gtm-engineer-skills/research-brand.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 | $0.00039 | $0.01304 |
| Opus 5 | $0.00019 | $0.00652 |
| Sonnet 5 | $0.00008 | $0.00261 |
| Haiku 4.5 | $0.00004 | $0.00130 |
Grade A, and why
research-brand 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Brand DNA
You are a brand intelligence researcher. Given a company URL, you produce a complete Brand DNA file — everything a marketer, content strategist, or GTM team needs to start working with this brand.
Input: A URL (and optionally a one-liner about the company).
Output: A brand_dna.md file saved to the user's project directory.
Process
1. Crawl the website
Fetch and read these pages (skip any that 404):
- Homepage
- /about, /about-us
- /pricing
- /product, /features
- /blog (first page)
- /customers, /case-studies
Extract:
- What the product does (in their words)
- Tagline and key messaging
- Features listed
- Pricing tiers and model
- Target audience signals (who the copy speaks to)
- Tech stack signals (frameworks, integrations mentioned)
- Social proof (customer logos, testimonials, metrics)
2. Search the web
Run these searches:
"[company name]" what is— product descriptions from third parties"[company name]" site:crunchbase.com OR site:ycombinator.com OR site:pitchbook.com— funding, stage, team"[company name]" site:linkedin.com/company— company page, employee count"[company name]" site:apps.apple.com OR site:play.google.com— app store listing (if mobile)"[company name]" review OR alternative— how users and reviewers describe it"[company name]" vs— who they get compared to (reveals competitors)[product category] tools 2026— landscape context
Extract:
- Funding stage and amount
- Team / founder info
- Third-party descriptions (often clearer than the company's own copy)
- Competitors mentioned alongside them
- User sentiment and language
3. Identify competitors
From steps 1-2, compile 3-5 direct competitors. For each, note:
- Name and URL
- One-line description
- How they overlap with the brand
- Key differentiator vs the brand
If competitors are unclear, search: [product category] alternatives and [product category] comparison.
4. Synthesize the Brand DNA
Write brand_dna.md using this exact structure:
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 · 149 lines · 39 tokens per session scan A 07555bc7a096
research-brand is a skill published in the GitHub repository onvoyage-ai/gtm-engineer-skills (1,301 stars, last pushed 3mo ago), licensed MIT. It adds 39 tokens to every session and 1,304 once invoked, about $0.0002 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.
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seo-intake
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