Next up after the setup the main determinant of success is list quality. Here is what I learnt:

1. List sources are plentiful spend time to figure out the right ones for you. Apollo.io is great for B2B and even for Shopify Stores, Bloomberry, Storecenses, Builtwith are others.

2. Always make sure to only export verified emails

3. Always de-select exporting catch all emails like support@shopifystore.com

4. Avoid list sources such as Storecensus.com because they provide you with lead sources that have catch all emails address.

5. Catch up email addresses will generate a lot of OOO responses and makes you feel like something is happening.

6. Very few people set OOO responses: emails these days because people are always on so the number of OOO responses will be limited. Don't be alarmed.

7. Open and click rates are messy signals. The algorithms that Google and other ESP use to open emails for fraud and other reasons are not transparent so several open timestamps will be within seconds of email send time. This makes detecting which email was opened by a human vs. an agent / bot is not easy to decipher. Best is to not even track open and click rate, most tools provide ways to disable that and focus on deliverability and outcomes.

8. Filters are key -

• The first one to consider is geo-location for most tools Shopify stores, US is the #1 geo-location to target. Every other place in the world is sub-optimal.
• Second, Revenue of your target audience. For us at Humanic its $2M to $10M because lower ARR folks don't have a very robust digital presence.
• For folks that have higher ARR you can connect with them on Linkedin with tools such as Dripify and others. $60 a month is sufficient.
• For audience that is below $2M they are likely to be on Instagram (which is preferred over TikTok)
• Technology spend is the third consideration.
• Title would be the fourth - smaller the company in ARR higher the title.
• Never worked with Intent signals I think they are an overkill you need a wide net when sending out small batch cold emails.

9. There are newer AI segmentation players in the market such as Orita - haven't used them yet.

10. Haven't used Clay for enrichment for cold emails I think its complicated to use and expensive for cold emails.

What do you think?

Humanic is a AI Native Email Marketing so obviously our preferred outreach channel is email. Here is a list of top 10 things I've learned.

1. Small Batch cold email is better than large volume cold emails. I define small batch on average 300-500 emails per cohort. Some of the cohorts above are larger but I let it go because the AI recommended it and the open rates were pretty awesome

2. Plain text with a single CTA is better

3. Make unsubscribe easy: Given that your target customer has never heard of you adding a line such as this -- You’re receiving this as part of a targeted Shopify store outreach campaign. If it isn’t relevant, unsubscribe here." in the footer works so you are upfront about your outreach.

4. Make it easy to unsubscribe.

5. Start with credibility instead of an offer - someone who has never heard of you needs to know if your offer is worth reading about.

6. Segmentation is key: Most people download a list of email addresses and then struggle to generate the right cohorts based on user attributes. Use AI to generate cohorts and spend time getting that right. (Humanic does that for you)

7. Do not send more than two emails in a campaign to a cohort. If someone is interested he/she will respond in the first email. Sending more emails only hurts your deliverability and domain reputation.

8. Email lists can be re-used every 90 days. Most people will forget if they received an email from you few months ago.

9. You need to maximize for open rates. with simple straight forward subject lines not crazy offers.

10. Protect your Sub-domain: Finally, you need to send all emails using a sub-domain best is something like try.yourdomain.com or get.yourdomain.com these work best. Monitor your domain reputation and pause campaigns that are getting more bounce or content rejection rates than what google and yahoo allow.

Thoughts?

I've been doing webinars on Prompting, Claude Code and Skills, this one by far is the best. Take a look if you are starting out with AI x Marketing, Claude Code, Skills and Writing Emails.

https://www.youtube.com/watch?v=Q8StY-XrG_E

On Thursday, April 23, 2026 at 12:00 PM PST, I'm hosting a free 60-minute live session on Maven — and I want to make sure you actually show up, because what I'm sharing is going to change how you build email campaigns.
The session is called Write Email Campaigns That Convert Using Claude and Humanic — two years of hard-won lessons from building an AI-native email platform, distilled into one tight hour.
Here's the thing: 921 marketers have already enrolled in my email prompting program since January. The folks who show up to sessions like this are the ones pulling ahead — shipping better campaigns, faster. The ones who don't? Still writing every email by hand, wondering why their numbers aren't moving.

Same model, different context overhead

Both the VS Code extension and a direct API call hit the same Claude API with the same per-token pricing. The model, capability, and cost-per-token are identical.

Where they diverge is what gets loaded into context automatically:

	VS Code / Claude Code	Direct API
Project files	Auto-loaded based on relevance	You control what goes in
CLAUDE.md	Auto-injected	Manual
Git status, open files	Auto-included	Manual
Conversation history	Managed automatically	You manage
System prompt (agentic harness)	Always present	Only if you add one
MCP tool definitions	Added when tools are used	N/A unless you build it

Net effect

The VS Code extension uses more tokens upfront because it automatically pulls in project context, memory files, git state, and tool schemas — things you'd have to explicitly include if calling the API yourself.

On the flip side, that automatic context often means fewer back-and-forth turns to get the right answer, so total session token usage can be comparable.

Prompt caching

Claude Code does leverage prompt caching on the system prompt and stable context — so repeated turns in the same session get cache read rates (much cheaper than full input tokens). If you're calling the API directly, you need to implement prompt caching yourself using the cache_control parameter.

Bottom line

• Same price per token — no surcharge for the IDE extension
• More tokens auto-loaded in VS Code vs. a bare API call
• Caching is handled for you in Claude Code; manual in the API
• Direct API gives you full token control — useful if you're optimizing aggressively

I just ran a live session with a small group of marketers using Claude, and I realized a bunch of their questions are probably what many others are wondering too.

Here are the exact questions people asked me:

1. “Do you need to upgrade to Claude Cowork to use Skills & instructions?”
2. “Where do you get the skills from? So I could get a skill that creates/uses that connector to create a new newsletter campaign or campaign for my Shopify store or similar.”
3. “What is ‘vibe coding’ and is it something done in Claude Code?”
4. “Will we get a recording of this session please?” (i.e., people already expect async learning here.)
5. “Do you connect GitHub with API or plugin?”

For context, my current understanding (please correct/add detail):

• Plan type: You need to be on a paid Claude plan (Pro/Max, not Free) to get full access to Skills and Cowork; free users don’t get Skills, and Cowork is tied to the paid tiers
• Where Skills “come from” -- Anthropic provides some built‑in skills, and you can also create your own by defining a folder with a SKILL.md plus scripts/resources, or use partner/organization skills depending on your setup.
• Using Skills for marketing/Shopify -- In theory you can build a custom skill that talks to your email tool or Shopify (via their APIs or a connector) and have Claude generate and launch campaigns, but you’d still need to wire up the actual API credentials/flows
• “Vibe coding” in Claude Code -- Vibe coding is when you describe what you want in natural language and let the AI generate most of the code, then you iterate with prompts instead of hand‑writing everything; Claude Code is one of the tools you can do this in
• GitHub: API vs plugin It looks like right now you generally connect via API (PAT, webhooks, etc.) or external tools, rather than a magical first‑party “GitHub plugin” button inside Claude itself

If you’re deep into Claude Cowork/Skills, I’d love concrete answers or corrections on:

• The cleanest explanation of “when exactly do I need Cowork vs just regular Claude + Skills?”
• Best practices for building skills that talk to external SaaS (e.g., GitHub, Shopify, email platforms).
• Any good public examples/templates of “campaign‑launcher” type skills.

I’m especially interested in answers that a non‑developer marketer could follow (click‑by‑click or file‑by‑file), since that’s who’s asking me these questions in the first place.

I'm hosting a session on sharing how you can build AI emails with Claude Code and Humanic tomorrow morning here is the link to register 

https://maven.com/p/92ae30/write-email-campaigns-using-claude-skills-with-humanic

The official MCP server for Humanic — an AI-native email marketing platform built for product-led growth teams.

Connect Humanic directly to your AI tools and agents to create campaigns, manage audiences, and trigger email workflows programmatically from any MCP-compatible environment.

What You Can Do

Once connected, AI agents in your environment can interact with Humanic programmatically. Example capabilities include:

• Campaigns — Create, activate, pause, and monitor email campaigns
• Audiences — Query and segment users based on product behavior and cohort data
• Email generation — Prompt-based email creation with AI-driven personalization
• Analytics — Retrieve campaign performance data (open rates, click rates, deliverability)
• Contacts — Create and manage contact records and lifecycle stages
• Domains — Inspect sending domain configuration and deliverability status

Here is the link for the humanic-mcp server

https://github.com/Humanic-ai/humanic-mcp-server