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Lifecycle Messaging

CommunityPopular
cbrock84
lifecycle-messaging

Designs automated email and SMS programs — welcome and onboarding sequences, nurture, re-engagement, transactional messaging, and the timing and segmentation behind them. Use this to build or fix an email sequence, plan lifecycle automation, improve open or click performance, set up SMS flows, or decide what messages a customer should receive and when.

Overview

Publishercbrock84
Repositoryheadcount
Skill namelifecycle-messaging
Stars
1.6K
Forks
237
Bundled files
1
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cbrock84 on GitHub. Read the source before you install it.

Installation

Install the Lifecycle Messaging AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cbrock84/headcount.git /tmp/headcount
mkdir -p .claude/skills
cp -r /tmp/headcount/plugins/demand-generation/skills/lifecycle-messaging .claude/skills/lifecycle-messaging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lifecycle Messaging in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Lifecycle Messaging on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Lifecycle Messaging is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Lifecycle messaging

Map the lifecycle before writing any message

For each stage, name what the person is trying to do and what would move them forward. Messages written without that answer become announcements, and announcements get unsubscribed from.

The stages that matter: just signed up, activated but not habitual, habitual, at risk, lapsed. Most programs over-invest in the first and neglect at-risk entirely, which is where the money is.

The welcome sequence

The highest-engagement messages you will ever send. Do not waste them on company history.

  1. Immediate — deliver whatever was promised, plus the single next action.
  2. Within days — the fastest route to first value, one step.
  3. After that — the use case most people miss, or the objection most people have.

Set expectations early: what you send, how often. It reduces unsubscribes more than any subject-line technique.

Timing and cadence

Trigger on behavior, not the calendar, wherever possible. A message sent because someone did something is many times more relevant than one sent because it is Tuesday.

Cadence sustainable at your worst week. Every message should be one the recipient would miss.

SMS is a different medium

Higher consent bar, higher intrusion, higher cost. Reserve it for time-sensitive and transactional messages — delivery, appointment, security, an expiring window. Marketing SMS at any volume trains people to opt out.

Explicit opt-in, honored opt-out, sending hours respected in the recipient's timezone. These are legal requirements in most jurisdictions, not preferences.

SMS compliance is not optional

Consult qualified counsel before launching an SMS program. The exposure here is statutory damages per message, which is how these become class actions.

In the US, marketing SMS requires express written consent obtained before sending — implied consent, an existing customer relationship, or a phone number collected for another purpose does not qualify. The consent record must show what the person agreed to receive and when, and it must be retained.

The operational requirements that follow:

  • Disclose program purpose, frequency, and that message rates may apply, at the point of consent.
  • Honor opt-out immediately, on every standard keyword, with a single confirmation message and nothing after it.
  • Respect quiet hours in the recipient's timezone, not yours.
  • Keep consent and opt-out records for as long as the retention rules require — these records are the entire defense if challenged.
  • Never buy or rent SMS lists. Purchased consent is not consent.

Other jurisdictions impose their own rules, and several are stricter. Determine which apply by where recipients are, not where you are.

Subject lines and preview text

They are one unit and get read together. A subject line that works with the preview repeating it wastes the second-most-read text in the message — use the preview to extend the subject, not echo it, and never leave it to default to the first line of the body.

Diagnosing

  • Low open — subject line, sender reputation, or list quality. Check deliverability before rewriting subject lines; a reputation problem looks exactly like a copy problem.
  • Open but no click — the message did not deliver on the subject, or has no single clear action.
  • Click but no conversion — the destination, not the email.
  • Rising unsubscribes — frequency or relevance. Usually frequency.

Sources

references/sources.md in this skill lists the outside authorities that settle the questions here — what each one is authoritative for, and what you may do with it. Check them before answering on anything they cover, and cite what you used. Most are free to read and not free to reproduce; the use note on each is binding.

Tooling

Lifecycle automation: Customer.io, Braze, Iterable, Klaviyo for commerce, HubSpot or Marketo for B2B, and similar.

Transactional sending is a separate job with separate reputation: Postmark, SendGrid, Resend, and similar. Keep it off the marketing domain.

In-product messaging: Intercom, Appcues, Pendo, and similar. An email that should have been an in-app prompt reaches half the audience at best.

Never

  • Send to a list that did not opt in.
  • Bury the unsubscribe.
  • Run a re-engagement program without a plan to actually remove the people who do not re-engage. An unengaged list damages delivery for everyone else.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Lifecycle Messaging AI skill do?

Designs automated email and SMS programs — welcome and onboarding sequences, nurture, re-engagement, transactional messaging, and the timing and segmentation behind them. Use this to build or fix an email sequence, plan lifecycle automation, improve open or click performance, set up SMS flows, or decide what messages a customer should receive and when.

Why use Lifecycle Messaging on TypingMind?

Because you install it once and use it with any model. Lifecycle Messaging is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Lifecycle Messaging in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cbrock84/headcount/tree/main/plugins/demand-generation/skills/lifecycle-messaging. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Lifecycle Messaging?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Lifecycle Messaging?

As many as you like. As long as a model supports skills, you can use Lifecycle Messaging with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Lifecycle Messaging AI skill free?

Yes. It is published on GitHub by cbrock84 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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