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title: “Bring One GPT You Don’t Want to Lose: How to Choose What’s Worth Preserving”
slug: choose-custom-gpt-to-turn-into-skill
meta_title: “How to Choose a Custom GPT to Turn Into a Skill”
meta_description: “Not sure which Custom GPT to turn into a Skill? Learn what makes a GPT worth preserving, how to choose one, and what to prepare before you begin.”
yoast_focus_keyword: “turn a Custom GPT into a Skill”
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You probably have at least one Custom GPT that has quietly become part of the way you work. Perhaps it helps you plan an offer, sort through a difficult decision or turn a scattered idea into something you can actually use. You know what to ask it, and it seems to know which questions to ask you.
If access to it changed, you could find another AI tool. What would take longer is rebuilding all the little things that make this particular GPT helpful.
Before you attempt to preserve a whole collection, choose one. Give yourself a short, practical exercise to identify what you would really miss. You can use the result whether you’re joining the GPT to Skills workshop or exploring the process on your own.
In a Nutshell
Bring a GPT you use regularly, especially one that helps with a job that would be awkward to rebuild. Run one familiar task, note its useful questions, priorities and decisions, and keep an example of the result. If someone else created it, check for an official portable version first and work only from permitted normal use. Your aim is not an identical copy. It’s a useful, independent starting point that you can personalise and test.
Choose the GPT by what you’d miss, not how impressive it looks
The most elaborate GPT isn’t automatically the right starting point. A simple assistant that saves you from staring at a blank page every Monday may be more valuable than a complicated tool you’ve only used twice.
Look for three things: repeated use, a job that would be inconvenient to rebuild, and behaviour that feels noticeably different from asking a general AI the same question. You don’t need all three in equal measure. They’re clues to where you’ve already found value.
For example, imagine two GPTs. One creates a long business strategy document you rarely open. The other helps you turn a messy project into a small Weekly Action Plan, asks what you’ve already tried and notices when you’re adding too much. The second may be a more useful first candidate because you can clearly describe what you rely on and test whether your new version provides it.
Do this ten-minute exercise before you build anything
Open your chosen GPT and give it a task you’ve used before. Pick something ordinary rather than inventing a special test to make it look good. As you use it, write down the following observations in your own words:
The job: What are you actually trying to get done?
The questions: What does it ask before producing an answer?
The sequence: What steps does it guide you through, and in what order?
The judgement: What does it prioritise, notice or steer you away from?
The result: What makes the output useful enough to act on?
Keep one example of your input and the resulting output, provided you’re permitted to retain it. You now have a small reference for testing your own implementation later.
Notice the difference between writing “It helps me plan projects” and “It asks what finished looks like, checks what is already in motion, separates essential work from interesting extras and helps me choose one next action.” The second description gives you something you can build and test.
What should you preserve, and what should you leave behind?
Think back to the four layers we’ve explored in this series: function, workflow, judgement and distinctive method. The basic function is only the beginning. The helpfulness often comes from the order of the questions and the standards applied along the way.
If you built the GPT yourself, gather the instructions and supporting resources you own. Check which pieces are current and which you might want to improve rather than carrying them forward automatically.
If someone else created it, begin by checking whether the creator offers an official Skill, template or other portable option. Don’t attempt to retrieve hidden prompts, private files or protected methods. You can observe how a GPT behaves through ordinary, permitted use and design your own independent approach to a similar job. If an important resource isn’t available to you, replace it with your own legitimate equivalent rather than trying to copy it.
That boundary matters, but it can also be useful creatively. Your new Skill doesn’t have to preserve every quirk of the original. It can reflect your preferred language, business context and way of working.
A simple workshop-day preparation checklist
You don’t need to organise your entire GPT library or arrive with a finished specification. One good example will give us something concrete to work with.
Choose one GPT you genuinely don’t want to lose.
Confirm that you can still open and use it. If it’s your own, have access to its configuration and the materials you own.
Bring one familiar task and, if possible, a saved example of its response.
Write down three to five behaviours that make it useful to you.
Note whether it’s your own GPT or someone else’s, and check the creator’s official options if relevant.
Think of one change that would make your independent version fit you better.
That’s enough. If you’re short on time, bring the GPT and one example of what it does. The notes can be rough. They are working material, not homework to be marked.
What should you expect to have at the end?
The useful outcome is not a promise of an exact replica that works identically everywhere. It’s a clearer understanding of what you value in the original and a practical route to an independent Skill version.
We’ll work from the job and behaviour you can legitimately use, identify the questions and standards worth carrying forward, and begin a version you can try on a real task. From there, you can compare results, adjust what feels off and decide whether it needs a quick conversion, further refinement or a deeper implementation.
Portability also needs testing. Different AI platforms can handle instructions and reference material differently, so a Skill should be tried in the environments where you intend to use it. The aim is more control over your own useful process, not another tool you have to trust blindly.
One small action you can take today
Finish this sentence: “If I lost this GPT tomorrow, the part I’d miss most is…”
Be specific. Perhaps it’s the way it challenges an idea before writing, helps you avoid overplanning or remembers to ask about your available time. That answer is a good place to start your own Skill, even if you don’t attend the workshop.
If you’d like to work through it with me, bring that GPT to the live GPT to Skills workshop. We’ll focus on preserving the useful behaviour through an independent version you can personalise, rather than copying someone else’s protected work. [Insert approved workshop registration link.]
One GPT. One useful job. A starting point you can keep improving.
Key Takeaways
Choose a GPT by its real value in your work, not its complexity.
Observe the questions, sequence, priorities and judgement that make it helpful.
Keep one permitted example to use as a comparison when testing your own Skill.
Check the creator’s official options first when the GPT isn’t yours.
Aim for an independent, useful first version rather than a perfect clone.
I Have Questions
Do I need to know how to create a Skill before attending?
No. Bring a GPT and a familiar task. The preparation is about recognising useful behaviour, not completing the technical work in advance.
What if I can’t access the GPT’s instructions?
If you didn’t create it, that may be expected. Check for an official option from the creator, then use only what you can observe through permitted normal use to design your own approach. You don’t need hidden instructions to describe the job you want your Skill to do.
Should I bring my most complicated GPT?
Not necessarily. Choose one you use and can test. A smaller, well-understood job often makes a better first project.
Will my new Skill produce exactly the same answers?
Not necessarily, and identical output isn’t the goal. Test whether it preserves the support that matters, then refine it for your needs and the platforms you plan to use.
Can I prepare even if I can’t attend the workshop?
Yes. The ten-minute observation exercise and checklist above give you a useful starting point for developing your own independent process.
