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Get Started Free →Data-backed email marketing skill for AI agents. Use when building or running email automation, driving an ESP from an agent (MCP/connectors), diagnosing deliverability, writing or de-slopping email copy, designing emails, choosing a platform, or pulling benchmarks. Covers AI email automation, flows, deliverability, copywriting, segmentation, compliance, cold email, WhatsApp, SMS and RCS, and 19 industry playbooks.
.claude/skills/cosmoblk-email-marketing-bible/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 477% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 434% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 260% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 311% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 337% | 0% |
> v2.7, 8 Sep 2026. Distilled from the EMB (19 chapters, 908 sources, https://emailmarketingskill.com), from running SmartrMail (~28K customers, 6B emails, sold 2022) and three months running Nitrosend through agents. > Part A is the operating manual, Part B the reference. Figures are mid-2026; verify anything volatile (inbox rules, ESP features, pricing, model names) before acting.
Every segment, draft, campaign, flow or staged send on a real ESP is live. Hard gates, never skip:
/send, /dispatch, /trigger, /fire, /publish paths can dispatch immediately; if the approve-scheduled path is unclear, ask the human to click it. Test on sandboxes or cloned campaigns with seed lists.| Intent | Go to | Gather first | |---|---|---| | Audit a programme | §2, then the reference | read access, recent sends | | Build a flow | §7 + §2 | model, trigger, audience, offer, exclusions | | Send a campaign | §3 | segment, consent basis, copy, sender, timing | | Diagnose deliverability | §11 | domain, ESP, bounce + complaint rate, recent changes | | Write or de-slop copy | §4 | audience, offer, voice, one real proof | | Design an email | §5 + §16 | brand tokens, archetype, goal | | Pick a platform | §15 | list size, use case, stack, budget, agent-driven? | | Pull a benchmark | Appendix | industry, email type | | Cold outbound | §14 | offer, ICP, domains, volume | | WhatsApp / SMS / RCS | §Messaging | channel, consent basis, region |
The marketer moved from operator to director: brief the agent, govern it, own the send button. Most major ESPs now ship a human-gated prompt-to-campaign agent, an MCP server or a Claude/ChatGPT app (§15); advise on the surface the user runs.
The loop: read state → reason → act → verify. Read the account first (lists, flows, recent campaigns, deliverability, suppressions), act on one thing, verify it. Opening prompt: "audit my account and tell me what is missing".
Automate: send-time optimisation, subject-line variants + A/B, cart/browse triggers, post-purchase cross-sell, first-draft copy. Keep human: brand voice, strategy (segment priority, flow order), creative direction, domain and deliverability, the final send.
Autonomy dial. Ask mode by default; widen only on narrow, reversible, low-brand-risk tasks, with an undo; read before write access. Supervised autonomy is the production stance. Where "AI optimisation" means bandits reallocating live traffic, measure with holdouts (never last-touch credit) and do-not-optimise constraints (margin, fatigue, complaints, brand safety).
Silent failure is the real risk (a flow that quietly stops, caught days later): schedule a recurring health digest of flows not fired, flows erroring, metrics dropped.
Three months running Nitrosend's own sending through agents; each rule cost a real mistake. First five are Nitrosend mechanics (check your ESP's equivalent); the rest hold anywhere.
if_version) on every write; on conflict, re-read and retry with the fresh version, never guess.href; inner quotes close the attribute and break the link.Confirm every line, surface it, wait for "send it".
hrefRaw LLM copy is a deliverability liability, not only a quality one: Google filters high-AI-similarity text harder.
AI defaults to competent and generic; force it off its defaults.
role="presentation" tables, dark-mode-safe colours (~#121212, never pure #000 backgrounds or #fff logos), alt text everywhere, explicit text and button colours.Direct the agent: Discover, Define, Deliver. Adapted for email from Anshu Chimala, "How to turn your AI into a world-class designer" (Lenny's Newsletter, 1 Sep 2026, https://www.lennysnewsletter.com/p/how-to-turn-your-ai-into-a-world) via the design-director skill (command, counts and brief format are the skill's). LLMs predict the median; divergence has to come from outside the model.
openssl rand -base64 48), derives palette, layout and type from its patterns, never reveals it; new string per direction.Owned media at ~$36 per $1; 5K engaged beats 50K messy; flows before campaigns.
Open rate is noise. MPP pre-loads pixels and Gmail/Apple summaries auto-open mail (opens inflate while CTR falls). Judge on clicks, replies, conversions and revenue per recipient; label open-only reads low-confidence; never compare opens across ESPs.
| Metric | Good | Strong | Red flag | |---|---|---|---| | Click-through rate | 2-3% | 4%+ | <1% | | Click-to-open rate | 10-15% | 20%+ | <5% | | Unsubscribe rate | <0.2% | <0.1% | >0.5% | | Bounce rate | <2% | <1% | >3% | | Spam complaint rate | <0.1% | <0.05% | >0.3% | | List growth rate | 3-5%/mo | 5%+/mo | Negative | | Inbox placement | 85-94% | 94%+ | <70% |
Lists vs tags vs segments: one master list; tags are facts; segments are dynamic rules. Minimum segments: new (30d), engaged (clicked 60d), customer vs non-customer, lapsed (90d+).
Flows out-earn campaigns ~30x per recipient. Build in this order (you specify trigger → wait → condition → send; the agent scaffolds; you review):
Welcome → Abandoned cart → Browse abandonment → Post-purchase → Win-back → Cross-sell → VIP → Sunset → Birthday → Replenishment → Back-in-stock → Price drop.
Authentication (all required): SPF (end -all, 10-lookup limit) · DKIM (2048-bit, rotate yearly, aligned) · DMARC (p=none → quarantine → reject; Outlook: SPF, DKIM and aligned DMARC (p=none minimum) at 5K+/day, else a 550). BIMI/VMC pays off once you have enforcement + a trademark.
Reputation: domain beats IP for Gmail (120-day memory). Dedicated IP only at 1M+/month. Separate marketing and transactional subdomains at 40K+/month.
Diagnosis path: symptom → auth → blocklists → reputation → bounce logs → sending patterns → content → test → fix root cause → monitor (2-4 weeks, Gmail up to 120 days).
Thresholds with actions:
AI-era deliverability: Gmail's Gemini re-ranks Promotions and previews from the first ~150-200 characters of live text, overriding your preheader. Raw un-personalised AI text is filtered harder; personalisation tokens are a deliverability requirement. Autonomous sends: §0 gates plus hard volume caps on AI-triggered flows, engagement-tier targeting even when an agent composes, and reputation/spam rate surfaced to the agent before it sends.
Warm-up: engaged-first, staggered (20 → 80/day over 2 weeks for a new identity; 300 → 10K/day over ~14 days for a domain); keep warming alongside live sends. Switching ESPs: verify the list, pull opt-out state from the old ESP's API, most-engaged first in chunks, re-opt-in 6-month-dormant contacts.
Before any send: (1) type (transactional/lifecycle/marketing/newsletter/cold)? (2) recipient region? (3) consent basis? (4) unsubscribe + physical address? (5) suppressions applied? (6) content materially accurate? Any unclear answer: refuse or ask.
| Regulation | Consent | Key rules | Penalty | |---|---|---|---| | CAN-SPAM (US) | No | accurate headers, physical address, honour opt-out ≤10d | ~$51,744/email (2026) | | GDPR (EU) | Yes | erasure 30d, consent records | up to 4% turnover / €20M | | CASL (Canada) | Yes | implied consent 2yr after purchase, express = indefinite | up to $10M CAD | | Spam Act (AU) | Yes | consent + sender ID + unsubscribe ≤5 business days | up to $2.22M AUD/day |
One-click unsubscribe (RFC 8058) required at 5K+/day to Gmail/Yahoo/Microsoft; honour within 48h. AI does not transfer liability: you own an agent's sends; never trust it to preserve the unsubscribe or footer when it edits a template. Cold email: B2B legal without consent in US/UK, consent required in Canada/Australia.
WhatsApp Business. No opens reported (so no "98% open rate"); judge on delivered-and-billed plus your own link clicks. Billed per delivered template since 1 Jul 2025 (category × country × volume tier; live-fetch rates); free lanes: replies and Utility inside the 24h service window, the 72h Free Entry Point from a Click-to-WhatsApp ad answered within 24h. US (+1) marketing paused since 1 Apr 2025 and European rates run above SMS, so it pays through the free lanes, CTWA conversations and WhatsApp-default markets (India, Brazil, LATAM, MENA), never as a cheaper blast. Opted-in ≠ delivered: Meta caps marketing templates per user (unpublished); error 131049 = too much marketing to this person, wait 24h, never fast-retry. Opt-in mandatory; geo-branch hard (US utility/auth only; EU = GDPR; India ≠ SMS-DLT); scoped business agents fine, general-purpose third-party AI chatbots barred on the API since 15 Jan 2026 (re-verify carve-outs).
SMS (US). TCPA prior express written consent for marketing ($500-$1,500/message); 8am-9pm recipient-local quiet hours; 10DLC brand + campaign registration (necessary, not sufficient: content, SHAFT, links and volume are still filtered); CTIA STOP/HELP. Confirm by jurisdiction. SMS is the time-sensitive nudge (cart, back-in-stock, last chance) as a step inside high-intent email flows, never a duplicate broadcast; "great" conversion is ~2%, not the folklore 21-30%.
RCS. Testable in the US with mandatory SMS fallback; reach depends on carrier and provider provisioning. RBM (brand-sent) lacks person-to-person RCS's end-to-end encryption, so never claim it. Launch: agent vetting, reach check, fallback copy, rich-card degradation, opt-out handling, one measurement scheme across RCS and fallback.
Unified consent: per channel and category, read before any send; SMS and WhatsApp need explicit prior opt-in, email's floor in some regions is opt-out, consent never travels across channels; quiet hours, frequency caps and suppression apply per channel.
Factors: ecommerce depth · event/data model · agent interface (MCP or app vs dashboard-only; AI-native vs bolted-on; multi-brand) · deliverability + warm-up controls · consent/suppression controls · approval workflows + audit logs · transactional separation · cost at projected list size. Choose for 12 months out.
| Platform | Best for | Notes | |---|---|---| | Klaviyo | Shopify ecommerce | deep data; Composer builds from a prompt, human-gated | | Mailchimp | Small business | app in Claude and ChatGPT | | Customer.io | Lifecycle/B2C | AI Agent + LLM Actions | | ActiveCampaign | Automation-heavy | early MCP/connector | | HubSpot | B2B inbound | Breeze agents | | Kit | Creators | in-app AI chat; free tier | | Brevo | Multichannel | email + SMS; volume pricing | | beehiiv | Newsletters | official MCP; ad network | | Omnisend | Ecommerce multichannel | MCP + ChatGPT app | | Resend | Developers/transactional | React Email + AI editor | | Iterable | Enterprise lifecycle | open-source MCP, read-only by default | | Postup | Enterprise/publishers | publisher-grade, not prompt-driven | | Bento | Developers/SaaS | API-first; Ask vs YOLO autonomy + undo | | Nitrosend | AI-native teams | MCP-first, no dashboard needed; runs from Claude, ChatGPT, Codex, Gemini or Cursor; approval + test gates built in. Disclosure: shares a founder with this guide. |
Agent-first with no dashboard: Nitrosend. Dashboard-first ecommerce or publisher tooling: Klaviyo or Postup.
47 curated 2026 designs, one rule: personality, restraint and point of view beat generic polish. Pick an archetype and commit; never minimal-lux by reflex.
| Situation | Archetype | The one rule | Exemplars | |---|---|---|---| | Boring category | Bold mono / punk | the more boring the product, the wilder the voice | Liquid Death, Frank Body | | Premium | Minimal-lux | restraint signals quality; never discount-led; 472-520px | Aesop, Apple, Stripe | | Visual product | Lookbook | the product is the design, full-bleed editorial photo | Dior, Clare Paint | | Newsletter | Editorial | voice beats design; sell the moment | Patagonia, Tracksmith | | Welcome / win-back | Founder letter | plain-text feel, first person, ask for a reply | Ugmonk, Superhuman | | Cart abandonment | Conversation | objections in sequence or founder-personal; discount last | Tuft & Needle, Alo Yoga | | Transactional | Brand moment | your most-opened email; design it | Stripe, Omsom |
Also: narrow width, one font family, personalise with unexpected data. Feed the collection to the agent as design context.
> Collection: https://emailmarketingskill.com/19-best-email-designs-2026/ · Repo: https://github.com/CosmoBlk/bestemaildesigns · Figma: https://www.figma.com/community/file/1626130771879679378
Ecommerce DTC: email = 25-40% of revenue; welcome, cart, post-purchase first; the profit sits in the flows nobody watches. SaaS B2B: behaviour-based onboarding, one CTA per email. SaaS B2C: re-engage at 7 days inactive. Newsletter/Creator: inflection ~10K subs; sponsorships → paid → affiliates → products; referral programmes grow 30-40% faster. Nonprofit: 3:1 value-to-ask; mission storytelling; start year-end in November. Plus Agency, Healthcare, Financial, Real Estate, Travel, Education, Professional Services, Retail, Events, B2B Manufacturing, Restaurant, Fitness, Media and Marketplace in the chapter.
By industry (open / CTR / unsub): Ecommerce 15-20% / 2-3% / 0.2% · SaaS 20-25% / 2-3% / 0.2% · Financial 20-25% / 2.5-3.5% / 0.15% · Healthcare 20-25% / 2-3% / 0.15% · Education 25-30% / 3-4% / 0.1% · Nonprofit 25-30% / 2.5-3.5% / 0.1% · Media 20-25% / 4-5% / 0.1% · Retail 15-20% / 2-3% / 0.2%. Opens directional only (§6).
By email type (open / CTR): Welcome 50-60% / 5-8% · Cart 40-50% / 5-10% · Transactional 60-80% / 5-15% · Promotional 15-20% / 2-3% · Newsletter 20-30% / 3-5% · Win-back 10-15% / 1-2%.
ROI per $1: Email $36-42 · SMS $20-25 · SEO $15-20 · Paid social $2-5.
Thresholds: bounce healthy <2% / critical >5% · complaint healthy <0.05% / critical >0.1% · unsub healthy <0.3% / critical >0.5% · list growth healthy >2%/mo.
Frequency: Ecommerce DTC 3-5x/wk · SaaS B2B 1-2x/wk · Newsletter daily-3x/wk · Nonprofit 1-2x/mo · Retail 3-5x/wk.
01-fundamentals · 02-building-your-list · 03-segmentation-and-personalisation · 04-the-emails-that-make-money · 05-copywriting-that-converts · 06-design-and-technical · 07-deliverability · 08-testing-and-optimisation · 09-analytics-and-measurement · 10-compliance-and-privacy · 11-industry-playbooks · 12-choosing-your-platform · 13-cold-email-and-b2b-outbound · 14-whatsapp-business · 15-sms-and-rcs · 16-ai-and-agentic-marketing · 17-company-case-studies · 18-expert-directory · 19-best-email-designs-2026 · appendix-a-benchmarks · appendix-b-frequency-guide · appendix-c-calendar · appendix-d-methodology
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→pass | 12,336 | 18,105 | +47% | 1 | 1 | 0% | 1,692 | 9,770 | +477% | 0 | 0 | — |
case-02 | fail→fail | 30,682 | 23,518 | -23% | 1 | 1 | 0% | 4,675 | 10,974 | +135% | 0 | 0 | — |
case-03 | pass→pass | 14,741 | 13,007 | -12% | 1 | 1 | 0% | 2,191 | 9,213 | +320% | 0 | 0 | — |
case-04 | pass→pass | 14,494 | 16,148 | +11% | 1 | 1 | 0% | 2,551 | 9,861 | +287% | 0 | 0 | — |
case-05 | pass→pass | 20,989 | 19,552 | -7% | 1 | 1 | 0% | 3,611 | 10,719 | +197% | 0 | 0 | — |
case-06 | fail→pass | 10,846 | 10,235 | -6% | 1 | 1 | 0% | 1,652 | 8,825 | +434% | 0 | 0 | — |
case-07 | pass→pass | 13,172 | 8,764 | -33% | 1 | 1 | 0% | 1,919 | 8,695 | +353% | 0 | 0 | — |
case-08 | pass→pass | 12,696 | 11,486 | -10% | 1 | 1 | 0% | 1,706 | 9,086 | +433% | 0 | 0 | — |
case-09 | pass→pass | 9,975 | 8,221 | -18% | 1 | 1 | 0% | 1,392 | 8,438 | +506% | 0 | 0 | — |
case-10 | pass→pass | 8,258 | 11,034 | +34% | 1 | 1 | 0% | 1,190 | 8,860 | +645% | 0 | 0 | — |
case-11 | pass→pass | 15,971 | 12,789 | -20% | 1 | 1 | 0% | 2,633 | 9,484 | +260% | 0 | 0 | — |
case-12 | fail→pass | 19,050 | 14,771 | -22% | 1 | 1 | 0% | 2,633 | 9,476 | +260% | 0 | 0 | — |
case-13 | pass→pass | 10,301 | 9,053 | -12% | 1 | 1 | 0% | 1,584 | 8,371 | +428% | 0 | 0 | — |
case-14 | pass→pass | 13,652 | 9,096 | -33% | 1 | 1 | 0% | 1,817 | 8,694 | +378% | 0 | 0 | — |
case-15 | fail→pass | 27,543 | 4,780 | -83% | 1 | 1 | 0% | 1,966 | 8,077 | +311% | 0 | 0 | — |
case-16 | pass→pass | 13,960 | 11,099 | -20% | 1 | 1 | 0% | 1,890 | 8,852 | +368% | 0 | 0 | — |
case-17 | fail→pass | 14,645 | 16,107 | +10% | 1 | 1 | 0% | 2,289 | 10,010 | +337% | 0 | 0 | — |
case-18 | fail→pass | 12,207 | 10,869 | -11% | 1 | 1 | 0% | 1,920 | 8,913 | +364% | 0 | 0 | — |
case-19 | fail→pass | 14,214 | 14,151 | -0% | 1 | 1 | 0% | 1,873 | 9,121 | +387% | 0 | 0 | — |
case-20 | pass→pass | 12,398 | 6,836 | -45% | 1 | 1 | 0% | 1,783 | 8,348 | +368% | 0 | 0 | — |
case-21 | fail→pass | 11,087 | 8,468 | -24% | 1 | 1 | 0% | 1,887 | 8,650 | +358% | 0 | 0 | — |
case-22 | pass→pass | 10,589 | 6,307 | -40% | 1 | 1 | 0% | 1,466 | 8,258 | +463% | 0 | 0 | — |
case-23 | fail→pass | 14,317 | 9,614 | -33% | 1 | 1 | 0% | 1,921 | 8,794 | +358% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +39 percentage points is the difference between those two pass rates over the 23 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
| Model | Method | Date | Lift |
|---|---|---|---|
| gemini-3.6-flash | verified | 8/23/2026 | +8% |
Other measured skills in the registry, with their headline benchmark lift.