Story

I co-founded SmartrMail (email marketing SaaS, 12K ecommerce customers, acquired 2022). When I left, I no longer had access to the sending data I'd spent years learning from, billions of sent emails, deliverability patterns, things that actually move email engagement

Cos' I don't have this data anymore, I built the best thing I could to help with the email marketing I currently do:

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my research process

Multiple sprints across all major email marketing topics. The crawler pulled 908 sources: Litmus, Klaviyo, HubSpot, Campaign Monitor, and Salesforce annual reports; practitioner blogs; academic research; platform documentation; Reddit threads; Shopify forums; and community discussions on X. From those, I extracted 4,798 discrete insights. Every claim that made it into the guide has a source. Anything that was unsourced opinion got cut.

That produced EMB v4: Over 80k words across 16 chapters. After two editorial passes, cutting duplicates, consolidating overlapping sections, removing anything that didn't earn its place it landed at 55,000 words. I reached out to all the email experts cited for their feedback too, over half contributed and made changes. I was stoked with that.

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the skill vs bible issue

The full 55K-word guide is too big for a context window. You can't just point Claude at the whole thing and expect coherent answers.

So the SKILL.md is a separate, condensed extraction: the key frameworks, benchmarks, practitioner names, and tactical thresholds that fit in context. When you ask Claude a question with the skill installed, it's drawing on a structured summary rather than trying to retrieve from a raw document dump.

but this creates a problem not solved yet: the skill and the Bible can drift. As the Bible gets updated (experts send corrections, better data emerges), the skill needs to be manually kept in sync. The obvious fix is an MCP server that connects them so changes to the Bible automatically propagate to the skill. I'll probably build that with the next big update to the bible.

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the data gap

I saw on smartmail that occasionally "conversion wisdom" or email best practise would actually not line up with our real, aggregate sending data. Eg "best time of the week to send a newsletter is 9am tue/thurs". Nope. Was absolutely a case by case solution and the rule of thumb here would often hurt email engagement. So, the weakness of this project is it's built on published research, not proprietary sending data. Published benchmarks are backward-looking, aggregated across wildly different use cases, and often produced by ESPs with an incentive to make email look good.

What I'd really want is anonymised send-level data from real campaigns. Subject line → open rate, across list sizes, industries, send times. Body structure → click rate. Flow configuration → revenue per recipient.

If anyone is working on this or has access to send-level data and wants to contribute, I'm very interested.

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the ESP integration problem

The other thing I'm frustrated by is AI connections into ESPs are still terrible. Most platforms have API coverage for basic CRUD operations but nothing close to what you'd need to actually run a campaign from Claude. You can pull subscriber counts. You can't meaningfully analyse flow performance, trigger segment rebuilds, or get real deliverability diagnostics programmatically.

A few ESPs are starting to add MCP servers (it's mentioned in Ch 14 of the guide), but it's early and patchy. Until that's solved, the skill is advisory, it can tell you what to do, it can't do it for you. That gap is worth building toward.

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what's live now

Install:

    git clone https://github.com/CosmoBlk/email-marketing-bible.git ~/.claude/skills/email-marketing-bible

MIT licensed. No paywall. No email gate. No affiliate links.

Happy to answer questions!