Custom GPT vs Skills: Choose a Reusable Work Setup

A Custom GPT is a configured ChatGPT assistant: a name, a system prompt, optional files and a set of tools, packaged so you can reuse the setup without rebuilding it each time. Skills are reusable task instructions that you define once and load into any AI app that supports them. Both solve the same underlying problem, getting the AI ready for a specific kind of work, but they solve it in different places and with different portability.

The choice between them matters more now than it did a year ago. OpenAI has announced that Custom GPTs are being retired; check the OpenAI help center↗ (opens in a new tab) for the current timeline and what replaces them. Tools like fenbs keep skills and project rules in a shared library that works across ChatGPT, Claude, Cursor and other supported apps, so the instructions you write today remain useful whatever tool you use next.

Quick comparison

What a Custom GPT is and how it works

A Custom GPT is a wrapper around ChatGPT that you configure through the GPT Builder: you write a system prompt describing how the assistant should behave, optionally upload files it can reference, enable or restrict tools such as web search or code execution, and give the result a name and a description.

The result is a ChatGPT entry point you can return to without rebuilding the setup. You can share it with others who have ChatGPT access. The assistant behaves consistently because the system prompt loads automatically. For workflows that live entirely inside ChatGPT, a Custom GPT is a convenient unit: you configure it once and everyone who uses it starts from the same setup.

The limitation is lock-in. A Custom GPT is tied to OpenAI's platform and to the account that created it. It does not transfer to Claude, Cursor, Gemini or any other app. When you switch models, the configuration does not travel with you. And with Custom GPTs being retired, any workflow that depends on them needs a new home.

What skills are and how they differ

A skill, in the general sense used across AI products, is a named, reusable instruction for a specific kind of task. You write the instruction in plain language: the input, the steps and the output format. The instruction can then be stored anywhere a connected AI app can read it, and loaded at the start of a session.

Skills in Claude, for example, are saved instruction sets you create and reuse from the Claude interface. Skills in fenbs are instructions stored in a shared library that any connected app can list and read over MCP. The underlying idea is the same: name a task, define how to do it well, save the definition. The claude skills examples guide shows what these look like in practice.

The key advantage over a Custom GPT is portability. A skill written in plain language works in any app that accepts a text instruction. You may need to adapt the delivery mechanism when moving between apps, but the core instruction and the lessons from testing it stay useful.

The portability gap and why it matters

Say a content team has a Custom GPT that drafts blog post briefs in a specific format. The team switches from ChatGPT to Claude for a particular project. The Custom GPT does not follow them. They rebuild the system prompt in a Claude Project, or paste the instructions manually each time, or start from a weaker prompt from memory.

If the same setup had been a skill, the instruction would travel. A copy in a shared library is readable by any connected app. The team adjusts the delivery mechanism for Claude, but the core instruction, the output format definition and the lessons from past test runs all survive the move.

This portability gap becomes significant the moment you use more than one AI app regularly, or when an app you depend on changes. The claude projects vs custom gpts guide explores a similar comparison within those two products specifically.

fenbs keeps approved skill instructions in a shared library alongside project rules and decisions. Any connected AI app can list your skills and read the full instruction before starting work. When you move from ChatGPT to Claude or any other supported app, the skills travel with you.

When a Custom GPT is the better choice

Despite the retirement timeline, a Custom GPT is still useful for work that happens entirely within ChatGPT and where a full assistant configuration is the right unit of reuse. If your team shares a configured assistant, everyone starts from the same system prompt without any setup on their end. That convenience is real.

Custom GPTs also support knowledge file uploads. If the assistant needs to reference a specific document or data set on every run, uploading it as a knowledge file is simpler than including it in every session. For teams already fully committed to ChatGPT and not planning to move, the configuration overhead is low.

The honest caveat is the retirement. Building new workflows around Custom GPTs now means rebuilding them when the retirement completes. For new setups, a skill-based approach is more durable.

When skills are the better choice

Skills are the better choice any time portability matters. If you use more than one AI app, if your team members use different tools, or if you expect your toolkit to change in the next year, skills outlast any single app's configuration format.

Skills are also better when the work context matters as much as the task instruction. A skill can be loaded alongside project rules, current task status and lessons from previous sessions. A Custom GPT can reference uploaded files, but it does not have a live connection to a changing project record.

For teams using Claude, the how to create a claude skill guide covers the specific steps. The chatgpt projects vs custom gpts guide covers another within-ChatGPT option if you want to stay in that ecosystem.

How to do this with fenbs

fenbs keeps your skills in a shared library that works whether your team uses ChatGPT, Claude, Cursor or another supported app. If you are moving off Custom GPTs, this is where to put the instructions.

1. Create a free account. Set up a project for the work these skills belong to.
2. Copy the system prompt from each Custom GPT you rely on. Trim it to the essential instruction, input and output specification, and save it as a skill in the project.
3. Add the project's standing rules as Decisions and rules in fenbs. These appear first in the AI context before a skill runs, giving every connected app the same constraints the Custom GPT had.
4. Connect your AI apps over MCP at Integrations. Each app can list your skills and load any one by name before starting work.
5. Test each migrated skill on three real inputs. Record what needed adjusting as lessons in the project so the next session starts smarter.