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/ad-test-designer)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/ad-test-designer"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/ad-test-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/ad-test-designer"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/ad-test-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 | $0.00139 | $0.02570 |
| Opus 5 | $0.00069 | $0.01285 |
| Sonnet 5 | $0.00028 | $0.00514 |
| Haiku 4.5 | $0.00014 | $0.00257 |
Grade A, and why
ad-test-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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ad Test Designer
Designs paid-ad creative/landing A/B/n and incrementality tests and reads them out: hypothesis, variant matrix, sample-size/duration/power plan, effect size, uncertainty, practical-effect status, and guardrail state. This skill owns experiment design + statistical interpretation. It may apply an owner-approved, precommitted action rule, but it never treats a p-value or helper output as an automatic business decision. It does not produce variants (ad-creative-builder), read back one already-shipped change (paid-measurement-loop), or do cross-channel reporting (performance-analyzer).
Quick Start
Design an A/B test for two landing-page hero variants. Baseline CVR is 3%, I want to detect a 15% lift. Goal is DR.
I have 4 RSA creative variants to test on a prospecting set. Build the variant matrix, sample size, and run duration.
Here's my finished test results CSV (variant, sessions, conversions). Is the winner significant — promote or kill?
Skill Contract
- Expected output: a test design (hypothesis, variant matrix, immutable test/variant/measurement binding, primary/secondary/guardrail metrics, sample-size + duration + power plan) and/or a read-out bound to that exact design (effect estimate, interval, statistical flag, practical-effect flag, guardrails, and either an owner-governed recommendation or
decision: UNDECIDED). - Reads: what the user wants to test, the ROAS profile (
direct-response|prospecting|incremental-profit), baseline CVR/CTR and traffic volume, stable control/candidate refs, the exact creative or landing artifact hash, and the measurement-contract ref/hash; for a read-out, the user's own exported results CSV (variant, sessions/impressions, conversions/clicks) plus the original binding. - Writes: a user-facing test-design or read-out doc plus a
### Handoff Summary. - Promotes: the chosen hypothesis, design parameters, calculated read-out, and any explicitly owner-approved action (ask before writing memory).
- Done when: a falsifiable hypothesis is stated; the matrix isolates one variable per variant; the control, candidate, variant hash, signal spec, and measurement contract are bound; baseline, MDE, alpha, power, multiplicity/sequential policy, duration, and guardrails are declared; and a read-out reports effect/interval/statistical/practical flags with
Calculatedprovenance against the same binding. A mismatch returnsNEEDS_INPUT/UNDECIDED; without a precommitted action rule and owner, returndecision: UNDECIDED. - Primary next skill: ad-creative-builder (to produce the winning direction) or paid-measurement-loop.
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.
- 10d ago Changed · +2 lines 7160f23c77de
- 13d ago First seen · 91 lines · 139 tokens per session scan A 60a2d5e9586e
ad-test-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 139 tokens to every session and 2,570 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.