What does this skill do, and when should you use it?
Newsletter Monetization Planner is for newsletter and creator-list operators planning how an owned email audience generates revenue. It models paid-subscription tiers, sponsorship inventory and rate cards, and referral or recommendation growth loops. Using supplied list, engagement, cadence, pricing, and growth data, it projects how list growth maps to revenue while labeling every figure as Measured, User-provided, or Estimated. It also checks sponsorship disclosure, honest-offer wording, claim substantiation, and commercial-mail consent, but does not score the full SEND program or calculate ROI.
Reads user-provided ESP exports, active-subscriber counts, open/click/CTOR data, send cadence, existing revenue lines, monetization goals, prices, targets, growth rates, offer claims, and consent state when available. It builds paid-tier MRR/ARR scenarios, CPM/CPC/flat sponsorship rate cards with inventory and fill assumptions, referral/recommendation/boost loop models, and list-growth-to-revenue projections. It produces evidence labels, a disclosure and honest-offer checklist, and a reusable handoff summary; missing figures are marked [needs source] rather than invented.
- A newsletter operator wants to compare a paid-subscription-only plan with a subscription-plus-sponsorship model.
- A creator needs a sponsorship rate card based on list size, opens, and clicks.
- An email team wants to estimate how referrals, recommendation swaps, or paid boosts affect list growth and revenue.
- A commercial newsletter needs a pre-sale check for ad disclosure, substantiation, and consent coverage.
How do you install this skill?
- This is a static source review; the skill, formulas, write flow, and registry-events path were not executed.
- When list size, engagement data, and a price or revenue target are unavailable, the skill should stop at NEEDS_INPUT rather than invent benchmarks.
- If commercial-mail consent is absent, the documented fallback models the stated audience while flagging an S2 gap; consent must be reconciled before selling or sending.
- Sponsorship, pricing, discount, guarantee, and performance claims require traceable substantiation; unresolved claims should remain D1 risks.
- Shell / CLI
- Local filesystem
Install the repository in Claude Code with /plugin marketplace add aaron-he-zhu/aaron-marketing-skills, followed by /plugin install aaron-marketing@aaron. On other Agent Skills-compatible hosts, run npx skills add aaron-he-zhu/aaron-marketing-skills; the repository can also be installed with git clone https://github.com/aaron-he-zhu/aaron-marketing-skills.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/email/nurture/newsletter-monetization-planner" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Invoke it with a prompt such as Model monetization for my 20,000-subscriber newsletter — paid tiers and sponsorships, including list size, goal (paid-subs, sponsorship, or both), and open/click data when available. Another supported scenario is Build a sponsorship rate card and a paid-sub revenue model for a 45K list at 42% open / 3.1% click. After delivering the model, it asks whether to save the results.
What are this skill's strengths and limitations?
- Covers paid subscriptions, sponsorship inventory, and referral growth economics in one workflow.
- Keeps measured data separate from user inputs and estimates.
- Includes disclosure, claim-integrity, honest-offer, and consent checks.
- Works at Tier 1 without requiring paid APIs or keyed integrations.
- Does not calculate ROI, revenue-per-send, payback, or list value; roi-calculator owns that math.
- Does not compute the full SEND/EQS score or enforce the final D1 gate; email-quality-auditor does.
- Ships no built-in CPM, conversion-rate, or K-factor benchmarks.
- The supplied material provides no independent test results or platform-specific validation for this individual skill.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Newsletter Monetization Planner this page | 61 · Recommended | ★ 3k | 13d ago | Apache-2.0 |
| List Growth Designer | 58 · Recommended | ★ 3k | 13d ago | Apache-2.0 |
| Email Marketing Bible | 60 · Recommended | ★ 342 | 14d ago | MIT |
| Advocacy Program Designer | 59 · Recommended | ★ 3k | 13d ago | Apache-2.0 |
| Email Sequence Generation (Market Emails) | 49 · Use with care | ★ 2.8k | 7mo ago | MIT |
This skill handles newsletter monetization and growth economics. email-quality-auditor handles full SEND scoring and D1 gating, roi-calculator owns return and list-value calculations, and landing-optimizer handles the post-click page.
How did FollowSkills review this skill?
The skill treats exports and pasted briefs as untrusted, uses keyless Tier-1 inputs, requires consent and disclosure checks, references a claims ledger, and asks before durable writes. It still writes to memory and may submit registry proposals without fully specifying rollback, recovery, data-flow boundaries, or per-action confirmation in this skill, so 6 points were deducted.
The contract, input/output definitions, NEEDS_INPUT path, labeling rules, completion criteria, formulas, and failure-gap handling are detailed. However, there are no skill-specific tests or execution records; referenced skills and registry-events dependencies cannot be reproduced from the supplied files, and edge-case coverage is limited, so 11 points were deducted and the static cap applies.
Triggers, audience, inputs, outputs, exclusions, and handoff routes are clearly described, with bilingual wording. Evidence for adaptation across newsletter types, jurisdictional compliance, and Chinese operational environments is limited, and geo-relevance is marked low, so 4 points were deducted.
The skill has structured frontmatter, versioning, Apache-2.0 licensing, Quick Start examples, a contract, data-source guidance, quality bar, save template, and next-skill routing. Maintenance ownership, changelog linkage, stability commitments, and single-skill installation/troubleshooting guidance remain incomplete, with some governance only documented at repository level, so 5 points were deducted.
The intended outputs are concrete: paid tiers, sponsorship rate cards, growth loops, list-growth/revenue projections, and disclosure checks. Measurement labels make the result potentially usable, but no representative generated output or execution verification is supplied, and the result depends on user data and sibling skills; the static maximum of 7 therefore applies.
The skill requires provenance labels, first-party ESP data, claims-ledger references, and order-ID truth sets while separating measured, user-provided, and estimated values. It provides no skill-specific test suite, third-party execution evidence, or corroborating sources, so 5 points were awarded.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →