• Reads: list size and active-subscriber count, open / click / CTOR (from a

  ~~email platform

  own-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 from

  memory/claims/claims-ledger.md

  and

  memory/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.md

  and propose price/mix decisions as

  pending-decision

  items in

  memory/open-loops.md

  .
• Done when:
  1. 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.
  2. 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.
  3. The growth ↔ revenue projection names at least one loop (referral / recommendation / boost) and its assumed input.
  4. 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.

Emit the standard shape from skill-contract.md §Handoff Summary Format: Status, Objective, Key Findings / Output, Evidence (each labeled Measured / User-provided / Estimated), Assumptions, Open Loops, Recommended Next Skill.

• ~~email platform

  (ESP, own-data manual export) — the campaign report's open / click / CTOR and active-subscriber count. These size the sellable audience and the sponsorship CPM base. Mark them Measured.

• ~~web analytics

  (GA4, own data) — landing/checkout conversion for paid-sub sign-up flows and referral-page performance, when the program links out. Mark Measured.

• ~~ecommerce

  (own data) — order-ID truth set for any product/affiliate revenue attributed to the list, not the ESP's self-reported attributed revenue.

[needs source]

1. Confirm inputs and goal — list size, active-subscriber count, open / click / CTOR, cadence, existing revenue, and the monetization goal (paid-subs / sponsorship / both). If none of list size, open rate, or a price/target is inferable, take the NEEDS_INPUT path below rather than guessing the whole model.
2. Size the sellable audience — active subscribers × open rate = the per-send impression base that a sponsorship CPM prices against; click base sizes click-priced or affiliate inventory. Label these Measured when they come from the ESP export, Estimated when derived from a benchmark.
3. Build the paid-subscription model (if in goal) — set free/paid tier structure and price points, apply a conversion-rate assumption per tier (state it explicitly, mark Estimated), and compute MRR/ARR from

   active × free-to-paid % × price

   . Never present the revenue as Measured — it rests on the assumed conversion rate.
4. Build the ad/sponsorship rate card (if in goal) — choose the rate basis per placement: CPM (price per 1,000 opens/impressions), CPC/flat by click, or flat per send. Set inventory (primary/secondary/classified slots per issue), a fill-rate assumption, and a floor price. Output a rate-card table.
5. Design the growth loops — referral / recommendation / boost mechanics: referral reward tiers, a recommendation-network swap, or paid boosts. State the assumed input per loop (e.g. share rate, referral conversion, or K-factor) and mark it Estimated. Growth loops feed the projection in step 6.
6. Project list-growth ↔ revenue — combine the growth-loop inputs with the per-line revenue to project revenue at growth milestones (e.g. current list, +25%, +50%). Show the assumption behind each milestone. Hand the return math (payback, revenue-per-send, list value) to roi-calculator — cite it as the SSOT; do not recompute ROI here.
7. Run the honest-offer / disclosure checks — every sponsorship must be labeled as an ad (FTC / native-ad disclosure); every price, discount, guarantee, or performance claim in a paid-tier or sponsor unit must trace to the current claims projection. Use only accepted wording and record its revision/offset. Flag — do not assert — any unsubstantiated or undisclosed claim as a D1 risk for the auditor; submit unresolved claims as authorized

   operation: propose

   requests through

   registry-events.py

   to

   memory/events/claims.ndjson

   for offer-claims-registry to resolve. Confirm the sellable audience excludes anyone without commercial-mail consent (per consent-registry); a consent gap is an S2 concern to flag, not to silently include.

[needs source]

• Stop and ask (NEEDS_INPUT) — when none of list size, open rate, or a price/revenue target is provided or inferable: you cannot size any revenue line. Ask for (1) active-subscriber count, (2) open/click rate or an ESP export, and (3) the monetization goal.
• Continue silently — missing optional data does not stop the run: no GA4 export → mark landing conversion Estimated and proceed; sponsorship not in scope → skip the rate card; no consent ledger present → flag the S2 gap as an open loop and model on the stated audience.

• SEND Benchmark — the framework; this skill produces the owned-audience D (Direct-response / Conversion) planning inputs the auditor scores, and it flags the D1 claim-integrity red line.
• skill-contract.md — shared contract, handoff schema, Output Voice, and Save Results template.
• state-model.md — memory tiers and save-path conventions.
• CONNECTORS.md — free/keyless data recipe per connector category.
• SECURITY.md — untrusted-input handling for exports and pasted sponsor/competitor copy.
• Sibling skills:
  • email-sequence-designer — the N lifecycle flows that carry these offers.
  • email-creative-builder — writes the pre-click E/D sponsor/paid-tier unit.
  • email-quality-auditor — the gate that computes EQS and runs D1.
  • roi-calculator — revenue-per-send / list-value math (SSOT).
  • landing-optimizer — the paid-sub / sponsor post-click page.
  • offer-claims-registry — registers offer wording and resolves D1 claim flags.
  • consent-registry — the commercial-mail consent SSOT that bounds the sellable audience.

• Primary: roi-calculator — turn the revenue model into revenue-per-send, list value, and payback math (it owns the return arithmetic; this skill only sets the inputs).
• Alternate: email-quality-auditor — score the program's EQS and run the D1 claim-integrity veto once the offer and disclosures are drafted. Route here first if any unit carries a D1 flag.
• If claims are unregistered or carry

  [needs source]

  : offer-claims-registry — register the offer wording with evidence provenance, then swap the resolved wording back before the auditor gate.
• If the sellable audience has a consent gap (S2): consent-registry — reconcile who may be mailed a commercial offer, then re-size the model.

max-depth: 3