aaron-marketing-skills is a collection of 120 AI-agent skills covering marketing work such as brand narrative, search optimization, social media, email, advertising, influencer campaigns, and launches. Marketers and agent users can install it as a plugin, use its portable skills, or run its described bot team. The catalogue entries are components of this marketing workflow.
Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add aaron-he-zhu/aaron-marketing-skills/plugin install aaron-marketingWrote 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/aaron-he-zhu/aaron-marketing-skills/send-experiment-designer)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/send-experiment-designer"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/send-experiment-designer/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/aaron-he-zhu/aaron-marketing-skills/send-experiment-designer"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/send-experiment-designer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk 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 | $0.00146 | $0.03986 |
| Opus 5 | $0.00073 | $0.01993 |
| Sonnet 5 | $0.00029 | $0.00797 |
| Haiku 4.5 | $0.00015 | $0.00399 |
Grade A, and why
send-experiment-designer 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 10d 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Send Experiment Designer
Designs email experiments across four modes and reads them out: a falsifiable hypothesis, a variant matrix that isolates one variable per cell, a sample-size / minimum-detectable-effect / run-duration / power plan, and a documented effect/uncertainty read. It may apply an owner-approved precommitted action rule, but statistical output alone never chooses a business action.
Mode set (pick one):
| Mode | Isolated variable | Primary metric |
|---|---|---|
a-b |
one change — subject or preheader or CTA or creative | open (subject) / click / CTOR (CTA/creative) |
multivariate |
2+ factors crossed (e.g. subject × CTA), one variable per cell | the goal metric, powered per cell |
send-time |
deploy hour/day; subject, segment, creative held constant | same-window engagement (open/click) |
hold-out |
send vs no-send (randomized control receives nothing / current default) | conversion or revenue-per-recipient (incremental lift) |
Default the mode from the request when it is unambiguous (e.g. "test two subject lines" → a-b, "best hour to send" → send-time, "measure incremental revenue" → hold-out); state the picked mode back and proceed.
Scope guard: this skill owns email experiment design + the significance read only. It scores the SEND E (Engagement) lever as a test signal — it does not compute the profile-weighted EQS or run the S1/S2/N1/D1 vetoes (email-quality-auditor does), and it does not write the subject/preheader/body/CTA under test (email-creative-builder does). Design here, produce there, gate there.
Quick Start
Design an A/B subject-line test. Baseline open rate is 38%, I want to detect a 3-point lift. Goal is retention, list is 12,000.
Send-time test: what's the best hour to deploy my weekly newsletter? Baseline open 40%, list 20,000.
I have a 2×2 subject × CTA multivariate idea and a hold-out. Build the variant matrix, sample size per cell, and run duration. Baseline click 2.1%.
Here's my finished test export (variant, delivered, opens, clicks, conversions). Is the winner significant — promote or kill?
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.
- 10d ago Changed · +1 lines 7d0f3e2825a4
- 13d ago First seen · 138 lines · 146 tokens per session scan A 680ae64c0dcd
send-experiment-designer is a skill published in the GitHub repository aaron-he-zhu/aaron-marketing-skills (2,767 stars, last pushed today), licensed Apache-2.0. It adds 146 tokens to every session and 3,986 once invoked, about $0.0007 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
geo-visibility-check
One-shot GEO audit: does your brand appear in Claude, ChatGPT, and Gemini answers for the buyer questions that matter? Runs a prompt panel through the engines with citation tracing and reports per-prompt verdicts, who wins instead, and which sources the answers come from.
geo-optimizer-skill
Run geo audit first. It scores the site 0–100 across 8 categories and generates a prioritized action list.
geo-loop
Run one bounded eGEOagents loop iteration over a workspace domain - read the charter and fresh collector data, do ONE unit of work, write substrate artifacts, append one Timeline entry and one LOG line. Use for loop mode, /geo:loop, scheduled GEO runs, or continuous monitoring.
content-scoring
Score content against the 10 GEO criteria with evidence and prioritized fixes. Use when users ask to score, rate, evaluate, or estimate ranking strength.
competitive-analysis
Analyze AI-search competitors for a query and recommend ranking strategy. Use when users ask competitor analysis, who ranks, or competitive landscape.
schema-generator
Generate JSON-LD schema markup for pages and content types with an implementation checklist. Use when users ask for schema, structured data, rich snippets, or markup.