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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-skillsnpx agentmods add skills/aaron-he-zhu/aaron-marketing-skills/email-sequence-designerWrote 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/email-sequence-designer)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/email-sequence-designer"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/email-sequence-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/email-sequence-designer"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/email-sequence-designer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00197 | $0.03855 |
| Opus 5 | $0.00098 | $0.01928 |
| Sonnet 5 | $0.00039 | $0.00771 |
| Haiku 4.5 | $0.00020 | $0.00385 |
Grade A, and why
email-sequence-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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Email Sequence Designer
Designs email lifecycle and automation flows plus the program's frequency governance, and scores the SEND N (Nurture / Lifecycle) dimension. It maps each flow's trigger, step timing, branch/exit conditions, and goal, layers a global cadence policy (send caps, quiet hours, fatigue guardrail) and a re-engagement/sunset path over the top, then hands the flow map to the skill that writes each step and to the auditor that scores the full program. It covers general lifecycle flows and owns the engagement-decay/sunset sub-item; the closed-loop win-back / re-consent (re-permission) program on a defined lapsed cohort is reactivation-specialist's, and the preference-center / frequency-options design is preference-frequency-manager's. It does not write the individual email or compute the final EQS.
Quick Start
Design a welcome flow for [product/audience] on [ESP]. Trigger is [signup event]; here is my current list/segment export: [paste/path].
Build an abandoned-cart sequence: [N] steps, [timing], with a discount branch and an exit-on-purchase condition.
My unengaged segment is [X]% of the list and complaints are rising. Design a win-back sequence and a sunset policy with send caps and quiet hours.
Skill Contract
Expected output: a set of lifecycle flow maps (trigger, per-step timing, branch/exit conditions, goal per flow), a frequency-governance block (global send cap, quiet hours, fatigue guardrail), a re-engagement/sunset path, a SEND N dimension score with sub-item notes and the typed profile named, and the standard handoff summary.
- Reads: the flow type or lifecycle goal, the trigger event, a versioned segment definition (from the user or from list-segment-builder when present), current consent/suppression snapshot refs, the versioned creative/HTML bindings for each step when available, an ESP flow/automation export (own data) and current cadence/complaint signals, and one SEND profile (
promotional|retention|cold-outbound|newsletter). - Writes: a user-facing flow map + cadence plan and a reusable handoff summary to
memory/email/email-sequence-designer/YYYY-MM-DD-<flow-or-goal>.md. - Promotes: chosen flow set, cadence/quiet-hours policy, sunset thresholds, the N-dimension score, and missing exports to
memory/hot-cache.mdandmemory/open-loops.md; propose durable cadence/flow decisions aspending-decisionitems — never writedecisions.mddirectly. - Done when: every flow has a trigger, per-step timing, a goal, and explicit branch/exit conditions; each step names the segment-definition version and exact creative/HTML version when available; a global send cap + quiet hours + a fatigue guardrail are specified; a re-engagement/sunset path exists; the SEND N score is emitted; and the output distinguishes local plan, ESP create result, send intent, and real send receipt.
- Primary next skill: email-creative-builder to write each step, or email-quality-auditor to score the program and enforce N1.
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.
- 10d ago Changed · +2 lines c6461154d48b
- 13d ago First seen · 100 lines · 197 tokens per session scan A cb6aeeaf6b91
email-sequence-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 197 tokens to every session and 3,855 once invoked, about $0.0010 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.