Allie K. Miller recently shared something that caught my attention: "I just saved hours per week with ONE hour and ONE button press inside Claude." When I read that, I immediately thought, yes - this is the kind of AI productivity win that actually sticks, because it turns a one-off good result into a repeatable system.

In her post, Allie described a simple loop: go back to a thread where you already did a task well (like writing newsletter headlines or editing a client email), ask Claude to abstract that process into a reusable "skill," and then hit the magical "Copy to Skills" button. After that, you can type a short request like "write me a newsletter subject line" and Claude applies your rules, preferences, style, structure, and output format automatically.

"Now, for the rest of eternity, I just have to type into a conversation "write me a newsletter subject line" it will apply all of my rules and preferences and style and structure and output them in the exact way and order that I want."

I want to expand on what Allie is pointing at because it is bigger than a neat Claude feature. It is a mindset shift: treat your best AI interactions as assets you can productize.

The real idea: stop prompting from scratch

Most people use AI like a vending machine: type a request, get an output, move on. That works for random, low-stakes tasks. But for recurring work (headlines, outreach emails, meeting notes, client updates), starting from scratch is the hidden tax.

Allie’s workflow attacks that tax in two ways:

It captures what already worked (your best example)
It converts your preferences into a reusable instruction set (a skill)

The result is consistency. You are not just getting "a" subject line. You are getting your subject line style, with your constraints, in your order, every time.

Allie K. Miller’s exact process, expanded

Here is the flow Allie shared, with a bit more context on why each step matters.

1) Start from a thread where you already did the task

Allie said she went to a recent thread where she completed a task she has to do often, like "write newsletter headlines" or "edit a client email." This is crucial.

Why? Because the best way to teach an AI your standards is to show it a successful example that already reflects your taste, your audience, and your constraints.

Tip: pick an example that you would happily reuse as a template, not one you only tolerated.

2) Ask Claude to abstract it into a reusable skill

Allie dictated: "I'm going to turn this into a skill. Ask me a few clarifying questions so we can abstract (this one client email/this one newsletter subject line) into many." That sentence is doing a lot of work.

You are explicitly telling the model:

The goal is generalization (not just this one output)
It should interview you (so the rules are accurate)
The output will be operationalized (so structure matters)

This is the fastest way to move from vibe-based prompting to a defined workflow.

3) Answer the clarifying questions (briefly but precisely)

Allie dictated replies to all questions. This is where you encode the rules that normally live in your head.

Examples of clarifying questions you want Claude to ask (or you can volunteer proactively):

Who is the audience? (customers, executives, founders, internal team)
What is the tone? (direct, warm, witty, formal)
What should always be included or avoided? (buzzwords, exclamation points, certain phrases)
What is the structure? (numbered list, short paragraphs, AIDA, PAS)
What is the success metric? (opens, replies, clarity, conversions)

The win here is not that Claude writes for you. The win is that Claude writes like you, reliably.

4) Tell it: "turn this into a Claude Skill"

Allie’s next instruction was simply: "turn this into a Claude Skill." Think of this as switching the output format from "answer" to "reusable tool."

A good skill definition usually includes:

What the skill does
When to use it
Required inputs (topic, audience, offer, constraints)
Steps the model should follow internally
Output format (for example: 10 subject lines, grouped by style)

5) Wait, then press "Copy to Skills"

This is the part Allie celebrated (with the pillow and USB blessings). The "Copy to Skills" button removes friction. You do not have to manually paste instructions into a separate configuration page or rebuild your prompt library by hand.

The productivity lesson: when setup friction drops, you create more reusable systems. And that compounding effect is where "hours per week" really comes from.

A concrete example: the newsletter subject line skill

Let’s make the abstract real. Suppose your newsletter subject lines follow these rules:

35-55 characters
No clickbait, no vague promises
Prefer specific nouns and numbers
Use a consistent pattern: "[Outcome] in [Timeframe]" or "[Mistake] + [Fix]"
Provide 12 options, then pick the top 3 and explain why

If you turn that into a skill, then "write me a newsletter subject line about onboarding" becomes a 10-second request, not a 10-minute back-and-forth.

The key is that you are not saving time on writing. You are saving time on decision-making, formatting, and re-explaining your standards.

Where this approach shines (and where it can fail)

Allie’s approach is best for repeatable tasks with stable quality criteria. Here are great candidates:

Email editing for a specific client or stakeholder
Weekly status updates in a consistent format
Newsletter headlines and preview text
Meeting note cleanup and action item extraction
Social post repurposing with a defined brand voice
Proposal sections (scope, timelines, assumptions)

Where it can fail:

When the task is truly novel each time (strategy from zero)
When your standards change weekly and you do not update the skill
When the inputs are unclear (garbage in, garbage out)

The fix is simple: treat skills as living documents. If you notice drift, update the rules, add a better example, or tighten the required inputs.

A simple checklist to build better skills

If you want to replicate what Allie described, here is a checklist I use:

Skill inputs

What does the user need to provide every time?
What optional context improves the result?

Skill rules

What must always be true? (tone, length, structure)
What must never happen? (forbidden phrases, risky claims)

Skill output

Exactly how should the answer be formatted?
Should it return options, a single best answer, or both?

Skill quality control

Should it self-check against constraints before outputting?
Should it ask a question if a key input is missing?

This turns the skill into a small system, not just a bigger prompt.

Why this is a content strategy lesson too

Allie’s post went viral because it delivered something rare: a specific, replicable workflow with a clear payoff. It is not "AI will change everything." It is "here is the button, here are the steps, here is what you get forever afterward."

If you create content (or internal docs) about AI at work, this is the bar to aim for:

Show the exact sequence
Anchor it in a real task people do weekly
Emphasize compounding returns (set up once, benefit repeatedly)

That is how a small product feature becomes a big productivity story.

This blog post expands on a viral LinkedIn post by Allie K. Miller, #1 Most Followed Voice in AI Business (2M) | Former Amazon, IBM | Fortune 500 AI and Startup Advisor, Public Speaker | @alliekmiller on Instagram, X, TikTok | AI-First Course with 300K+ students - Link in Bio. View the original LinkedIn post →