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/newsletter-monetization-plannerWrote 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/newsletter-monetization-planner)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/newsletter-monetization-planner"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/newsletter-monetization-planner/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/newsletter-monetization-planner"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/newsletter-monetization-planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 83 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.03577 |
| Opus 5 | $0.00069 | $0.01788 |
| Sonnet 5 | $0.00028 | $0.00715 |
| Haiku 4.5 | $0.00014 | $0.00358 |
Grade A, and why
newsletter-monetization-planner 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Newsletter Monetization Planner
Plans the money and growth-loop economics for an owned-audience program — a newsletter or creator list — across three revenue lines: paid-subscription tiers, ad/sponsorship inventory with a rate card, and referral/recommendation loops. This is the build skill for the SEND D (Direct-response / Conversion) lever on owned audiences: it produces the revenue model, the list-growth ↔ revenue projection, and the honest-offer / disclosure checks. It does not compute the profile-weighted EQS or run the D1 veto (that is email-quality-auditor), and it delegates the return math to roi-calculator and the post-click page to landing-optimizer.
Scope guard: this skill plans monetization and growth economics only — it scores/handles the SEND-D owned-audience lever and hands off. It does not compute the final EQS, run any of S1/S2/N1/D1, or do the return math itself. Only email-quality-auditor computes EQS and enforces the vetoes; roi-calculator owns revenue-per-send / list-value math as the SSOT.
Quick Start
Shortest invocation:
Model monetization for my 20,000-subscriber newsletter — paid tiers and sponsorships
Common scenario:
Build a sponsorship rate card and a paid-sub revenue model for a 45K list at 42% open / 3.1% click — compare a paid-sub-only vs a hybrid (subs + sponsorship) plan
Output: a labeled revenue model (paid-tier table + ad/sponsorship CPM-or-flat rate card + referral-loop line), a list-growth ↔ revenue projection, and a disclosure / honest-offer checklist — with every projected number tagged Measured / User-provided / Estimated.
Skill Contract
- Reads: list size and active-subscriber count, open / click / CTOR (from a
~~email platformown-data export), current send cadence, existing revenue lines, the monetization goal (paid-subs / sponsorship / both), any target revenue or price points, and a growth rate or acquisition source. Offer terms and approved wording frommemory/claims/claims-ledger.mdandmemory/claims/offers.md— the offer-claims-registry ledger — when present. Consent/suppression state (who may be mailed a commercial offer) from consent-registry (memory/consent/) when present. - Writes: a user-facing revenue model and growth ↔ revenue projection plus the disclosure/honest-offer checklist, and a reusable handoff summary. Save path:
memory/email/newsletter-monetization-planner/YYYY-MM-DD-<topic>.md. - Promotes: the chosen monetization mix, locked price points, the sponsorship rate basis (CPM vs flat), and any unsubstantiated-claim or missing-disclosure risk — ask before writing, then promote durable facts to
memory/hot-cache.mdand propose price/mix decisions aspending-decisionitems inmemory/open-loops.md. - Done when:
- The revenue model covers each active line (paid tiers and/or sponsorship inventory and/or referral loop) with a stated conversion or fill-rate assumption per line.
- Every projected number is labeled Measured / User-provided / Estimated, and no revenue figure is presented as measured when it rests on an assumed conversion rate.
- The growth ↔ revenue projection names at least one loop (referral / recommendation / boost) and its assumed input.
- The disclosure/honest-offer checklist is completed: every sponsorship is labeled as an ad, and any claim needing substantiation is flagged for D1, not asserted.
- Primary next skill: roi-calculator — turn the revenue model into revenue-per-send / list-value / payback math, or email-quality-auditor to score the program and run D1.
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 e38e5914781b
- 13d ago First seen · 115 lines · 139 tokens per session scan A 93eaf217b05a
newsletter-monetization-planner 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 3,577 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.