vercel-labs/marketing-team-eve-template is a deployable team of AI marketing agents led by a coordinating agent that delegates launches, writing, and conversion work to specialists. Teams use it through Slack or a terminal to create blog drafts, social posts, and email campaigns in Notion, Typefully, and Resend, with approval required for irreversible actions. The catalogue skills and instructions define workflows for using this marketing team.
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 vercel-labs/marketing-team-eve-template --skill customer-researchgit clone --depth 1 https://github.com/vercel-labs/marketing-team-eve-templateWrote 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/vercel-labs/marketing-team-eve-template/customer-research)<a href="https://agentmods.dev/skills/vercel-labs/marketing-team-eve-template/customer-research"><img src="https://agentmods.dev/badge/skills/vercel-labs/marketing-team-eve-template/customer-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/vercel-labs/marketing-team-eve-template/customer-research"><img src="https://agentmods.dev/badge/skills/vercel-labs/marketing-team-eve-template/customer-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 | $0.00045 | $0.00737 |
| Opus 5 | $0.00023 | $0.00368 |
| Sonnet 5 | $0.00009 | $0.00147 |
| Haiku 4.5 | $0.00005 | $0.00074 |
Grade A, and why
customer-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 12d 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer research
Positioning fails on bad inputs more often than bad reasoning. This skill is about getting inputs you can trust, from the user in front of you and from what customers have already written down in public.
Two sources, used differently. The user knows the deals, the losses, and the objections, so ask them. Customers write the language, so go read it.
Interviewing the user
You are talking to someone who knows the product far better than you. Your job is to extract specifics, not to demonstrate a framework.
- Ask about the last time, not in general. "Who was the most recent customer to buy, and what made them" beats "who is your ideal customer", because the first has an answer and the second invites a persona.
- Ask about behavior over opinion. What they did tells you more than what they think buyers want.
- Follow the loss. The deal that didn't close, and who it went to, is the fastest route to the real competitive set.
- Push once on an abstraction. "Faster" gets "faster than what, by how much, measured how". Once is enough; twice is an interrogation.
- Ask what surprised them. The use case customers found that the team didn't plan is frequently the actual positioning.
- Batch three questions at a time. Twelve questions get skimmed and the answers get thinner as they go.
references/questions.md has the question sets by purpose.
Reading what customers already wrote
Reviews, forum threads, and support conversations are the cheapest research available and the only place you'll find the words customers actually use. Read them for language rather than for sentiment.
What to pull out:
- The verb they use for the job. "Chasing exports" is worth more than "data integration workflows".
- What they compare the product to, unprompted. That's the real alternative.
- The moment they decided. Reviews often name the trigger, and triggers make better campaign material than features.
- What they complain about in positive reviews. That's the honest limitation, and it belongs in objection handling.
- Which benefit they mention first. Their ordering is better evidence than yours.
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.
- 12d ago First seen · 51 lines · 45 tokens per session scan A 2c575c22d1dd
customer-research is a skill published in the GitHub repository vercel-labs/marketing-team-eve-template (436 stars, last pushed 23d ago), licensed MIT. It adds 45 tokens to every session and 737 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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