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name: email-marketing-bible description: > 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, directing AI email design, choosing a platform, or pulling benchmarks. Covers flows, segmentation, compliance, cold email, WhatsApp, SMS and RCS, and 19 industry playbooks. category: email license: MIT

Email Marketing Bible, Skill Reference

By George Hartley, co-founder of Nitrosend.

v2.7, 8 Sep 2026. Distilled from the EMB (19 chapters, 908 sources, https://nitrosend.com/email-marketing-bible), from running SmartrMail (~12K 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.

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PART A: OPERATING MANUAL

0. AGENT OPERATING RULES

Every segment, draft, campaign, flow or staged send on a real ESP is live. Hard gates, never skip:

• No send or schedule to more than one recipient without explicit human approval in this conversation ("send it" or equivalent). Single-recipient test sends still need a yes.
• Preview before asking; show the packet before any send: preview URL, audience size, exclusions/suppressions applied, subject, preview text, send time, from-name + reply-to, unsubscribe present, compliance risk.
• Block the send if authentication is missing, unsubscribe or physical address is absent, complaint rate is at or above 0.1%, consent basis is unclear, or the audience includes suppressed, bounced or complained contacts.
• Never probe unknown mutating endpoints on a live audience. /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.
• Separate the modes. Transactional, marketing, lifecycle and cold outbound have different rules, domains and consent bases. Never mix them.
• Log every autonomous action (segment changed, flow edited, campaign created, send staged) so the human can audit it.

1. TASK ROUTER

| 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 |

2. AI EMAIL AUTOMATION (the operating model)

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.

2b. FIELD NOTES: RUNNING AN ESP FROM AN AGENT (JUN-SEP 2026)

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.

• Optimistic-concurrency version (if_version) on every write; on conflict, re-read and retry with the fresh version, never guess.
• Re-assert brand or account before every write batch after idle; MCP context resets silently to the default brand while reporting a deliberate selection.
• Silent-parameter APIs default to send-to-all: pre-flight assert audience id and count, never probe a mutating endpoint on a live audience (one unknown body key mailed 1,003 contacts).
• Liquid merge defaults go unquoted inside href; inner quotes close the attribute and break the link.
• Animated WebP rather than GIF for heroes; then fetch the served URL and confirm it still animates (CDN variants can flatten to frame one).
• Set text and button text colours explicitly on every design; theme defaults drift (grey headlines, dark text on a coloured button).
• Decode tracking-wrapped CTA URLs before approving; the wrapper hides the target.
• Never backfill or re-dispatch failed sends without a human order; late sends look worse than none.
• Drafts by default; the literal "send it" in chat is the only thing that fires a blast.
• Every email gets a hero, a live-text headline and one button; secondary content gets inline links.
• Quote tiles come from HTML in headless Chrome, never an image model (garbled type, invented names).
• Migration opt-out state comes from the old ESP's API, never a list CSV; exports drop unsubscribes.

3. PRE-SEND CHECKLIST

Confirm every line, surface it, wait for "send it".

• [ ] Audience: size and segment logic verified against actual counts (AI segments run over-broad)
• [ ] Suppressions: unsubscribed, bounced, complained, globally suppressed, frequency-capped, open support issue
• [ ] Authentication: SPF, DKIM, DMARC aligned, p=quarantine or stronger (Outlook requires all three at 5K+/day)
• [ ] One-click unsubscribe (RFC 8058) + physical address present
• [ ] Copy: §4 pass, one CTA, subject ≤45 chars, preview text adds information
• [ ] Design: single column ≤600px, dark-mode safe, alt text, live-text headline, explicit text and button colours, images <200KB each and <800KB total, cross-client preview, spam score, hero animates at the served URL
• [ ] Links: wrapped CTAs decoded, no placeholder URLs, merge defaults render inside href
• [ ] Sender: correct from-name + monitored reply-to; brand and account re-asserted; send time set; consent basis valid for this audience and content
• [ ] Non-email: US SMS 10DLC brand + campaign registered; WhatsApp opt-in for the category + approved template; quiet hours per recipient local time (SMS 8am-9pm)
• [ ] Kill switch: batched or throttled send with a working pause and rollback plan
• [ ] Test send reviewed in a real inbox with real merge data
• [ ] Personalisation confidence, inventory and pricing freshness checked; kill plan named
• [ ] Human approval captured

4. ANTI-SLOP COPY PROTOCOL

Raw LLM copy is a deliverability liability, not only a quality one: Google filters high-AI-similarity text harder.

• The deepest tell is the absence of stakes. Put one genuine, defensible opinion in every email. Ask the draft where it is too safe.
• Burstiness. Alternate long and short sentences; a 3-5 word line after a long one, at least once per section.
• Blacklist (lint before send): delve, leverage, foster, ignite, empower, unleash, streamline, navigate, seamless, robust, cutting-edge, transformative, multifaceted, pivotal, dynamic, comprehensive, tapestry, landscape, beacon, realm, journey, furthermore, moreover, "in today's fast-paced", "I hope this email finds you well".
• Syntax fingerprints (survive find-and-replace): "it's not X, it's Y", rule-of-three padding, copula avoidance ("serves as" for "is"), em dashes.
• Specificity is the cheapest humaniser. Real numbers, names and dates. Pull one real metric from the brand's own data into every email.
• Workflow: human strategy → AI draft → human edit. High-personality formats (founder letter, welcome): rough human notes first, AI tightens. Never AI-first.

5. AI EMAIL DESIGN PROTOCOL

AI defaults to competent and generic; force it off its defaults.

• Two readers: the human and the summariser. Gmail's Gemini and Apple Intelligence summarise from the opening live text (rollout tiered). Front-load the offer in real text, semantic headings, never image-only; live text also wins accessibility and dark mode.
• Context beats prompt. Feed brand kit, design tokens, a tested module library and a rules file before iterating on wording.
• Safe substrate. Emit MJML, React Email or Maizzle (compile to inbox-safe HTML), never raw HTML from a prompt.
• Anti-slop design rules: own one colour (30-60% of the surface); restraint over decoration; real photography, never AI stock; bold live-text headlines; one message, real negative space. Ban the purple-to-blue gradient and the beige wash.
• Compliant by default: single column ≤600px, 44px tap targets, 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.

• Seed strings. The agent generates a random string in a shell (openssl rand -base64 48), derives palette, layout and type from its patterns, never reveals it; new string per direction.
• Broad before deep. Ask for 12-20 directions as one-liners, "go broad, not deep". The human picks from text before any image or code exists. Reject anything guessable from the category alone.
• Ambitious briefs. One sentence naming a real reference (Graza's chartreuse drench, Aesop's restraint) plus two anti-references.
• The critic loop. Screenshot the rendered test send and hand it to a separate, stronger model in a fresh context (no code, history or earlier critiques). It names the aesthetic, imagines how a top studio would execute it, lists the biggest gaps and scores /10. Fix, re-screenshot, re-critique with the same prompt (target score kept out of it) until the critic scores 9/10, capped at four rounds. The critic is ~10% of output tokens and most of the taste.
• Chain models. Code model for structure, image model for stills, video model for a looping hero or state transition. As of Sep 2026 (verify): Claude Fable 5.1 as critic; Claude Opus 5 or Sonnet 5 (Claude Code) or GPT-6 Astra (Codex CLI) as implementer; gpt-image-2 for stills; Gemini Omni 1.1 for video.
• Deliver by subtraction. Cut glows, gradients, decorative containers and labels that repeat the visual, then a light anti-slop pass on copy (§4) and visuals (reflex fonts, centred hero + three cards, purple on dark).
• Keep failed prompts; retest on the next model generation.
• Who to follow (Chapter 18, 49 practitioners, five added in v2.7): Anshu Chimala @anshuc, Karri Saarinen @karrisaarinen, Ryo Lu @ryolu_, Jenny Wen @jenny_wen, Lee Munroe @leemunroe.

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PART B: REFERENCE

6.