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Email Lifecycle Lifecycle Audit

OrganizationPopular
gooseworks-ai
email-lifecycle-lifecycle-audit

Step of the email-lifecycle playbook: audits list, consent, live flows, recent sends and sending setup, marking every item verified, from-you or unknown. Run email-lifecycle instead; not for direct use.

Overview

Publishergooseworks-ai
Repositorygoose-skills
Skill nameemail-lifecycle-lifecycle-audit
Stars
1.3K
Forks
219
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 gooseworks-ai on GitHub. Read the source before you install it.

Installation

Install the Email Lifecycle Lifecycle Audit 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.
    https://github.com/gooseworks-ai/goose-skills/tree/main/skills/marketing/email-lifecycle-lifecycle-audit
  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/gooseworks-ai/goose-skills.git /tmp/goose-skills
mkdir -p .claude/skills
cp -r /tmp/goose-skills/skills/marketing/email-lifecycle-lifecycle-audit .claude/skills/email-lifecycle-lifecycle-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Email Lifecycle Lifecycle Audit 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 Email Lifecycle Lifecycle Audit 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 Email Lifecycle Lifecycle Audit 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-audit

Reads: the optional audit items from intake message 1 (flows list, 90-day campaign report, list size, the "original" text of one recent campaign, the spam-rate chart — it asks nothing separately); lifecycle/program.md and the latest lifecycle/deliverability/*.md if present; the latest reports/performance/*.md; whatever the marketer handed over (tool exports, screenshots, Postmaster chart); the customer's own email-tool connector if the host has one (read only). Loads ../email-lifecycle/references/deliverability.md and ../email-lifecycle/references/benchmarks.md.

Does:

  1. Plumbing. For each checklist item in deliverability.md, collect evidence. Authentication is green only from a marketing test email's headers (sent through the email tool) showing spf=pass, dkim=pass aligned with the From domain, and dmarc=pass — SPF on the tool's own return-path is fine (Mailchimp, and HubSpot without a dedicated IP, can't align SPF; that is not a fix item). Check the headers came through the email tool (its Return-Path or DKIM signature, a List-Unsubscribe header) — a personal Gmail or Workspace email's headers don't count and stay unknown; with a shell, dig TXT supports it (return-path domain, _dmarc, DKIM selector). A root-domain record alone is "present — not verified for marketing mail". Mark every item verified / from you / unknown. Never mark unknown items green.
  2. List. Size; engaged-30/90 counts and share (clicks and orders, not opens); growth and churn in the last 30 days; consent source per channel; any bought, scraped or marketplace-sourced segment (hard stop for those contacts); last send date (6+ months dormant → re-permission first).
  3. Flows live — in the email tool and in the store platform (Shopify's default abandoned-checkout email, Shopify Email automations, review-app request emails such as Judge.me or Okendo), so the build order can switch off duplicates. Name, trigger, status, message count, the last 90 days' recipients, conversion or revenue per recipient. Look for the classic breaks: buyers still receiving cart emails (no "placed order since" filter), overlapping flows with no priority, dead or expired codes, links to removed products, missing fallbacks.
  4. Read-back against last cycle. For each flow in lifecycle/program.md, compare its recorded hypothesis and target — and the last run record's numbers for it (../email-lifecycle/references/run-memory.md: same window length, same attribution setting, settled, never opens) — with this cycle's result (the weekly read's email rows or the marketer's export): beat it → keep, promote the winning test; missed it → its weakest message is first in line to rewrite; no data → "not measured". Write each flow's result and next hypothesis into the state block for step 2's ranking.
  5. Recent sends (90 days). Cadence by segment, revenue or conversion per recipient, unsubscribes, complaints, bounces; the worst three sends and why.
  6. Verdict. GREEN / AMBER / RED with the blockers first. Only missing consent or complaints ≥ 0.3% stop the writing. Red or unknown authentication blocks switching on and broadcasts, not drafting: flows are still written, marked "switch on after fix #N", and unknown items go on the marketer's fix list. Bulk-sender rules are requirements at ≈ 5,000+ a day to Gmail/Outlook (a list above ~5,000 with unknown Gmail share is treated as bulk); below that, amber best practice. If the list's main market is one where people buy over chat (India, Southeast Asia, Brazil, much of Latin America), the first line says WhatsApp is likely the main channel and is not covered yet.

Writes: the report in the format at the end of deliverability.md, shown to the marketer before any writing, and saved later as lifecycle/deliverability/<YYYY-MM-DD>.md. A short "state of the program" block (list, flows live with last cycle's result vs target, what is broken, what is missing) feeds the flow choice. In chat, the report names no tools or ids — "your sending isn't verified yet: here's the one setting to change".

Never: estimates a number it did not see; calls opens engagement; treats an AI guess of the tool's setting as verified.

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 Email Lifecycle Lifecycle Audit AI skill do?

Step of the email-lifecycle playbook: audits list, consent, live flows, recent sends and sending setup, marking every item verified, from-you or unknown. Run email-lifecycle instead; not for direct use.

Why use Email Lifecycle Lifecycle Audit on TypingMind?

Because you install it once and use it with any model. Email Lifecycle Lifecycle Audit 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 Email Lifecycle Lifecycle Audit in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gooseworks-ai/goose-skills/tree/main/skills/marketing/email-lifecycle-lifecycle-audit. 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 Email Lifecycle Lifecycle Audit?

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 Email Lifecycle Lifecycle Audit?

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

Is the Email Lifecycle Lifecycle Audit AI skill free?

Yes. It is published on GitHub by gooseworks-ai 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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