Claude Skills vs Custom GPTs: What Actually Differs

If you’ve built or bookmarked a few Custom GPTs and you’re now looking at Claude, the first question is usually the same one we get in our inbox every week: “Is there a Claude version of Custom GPTs?”

Short answer: yes, and it’s called a skill. Longer answer: skills and Custom GPTs solve overlapping problems with very different shapes, and the shape matters more than the feature lists suggest. We run SkillProof, where we install and test Claude skills on a clean machine before recommending them, so we’ve spent months watching where each format shines and where each one quietly falls over. This comparison is written for people doing actual work, whether or not you’ve ever opened a terminal.

TL;DR

A Custom GPT is a hosted mini-assistant. You give it instructions, optionally upload knowledge files, optionally wire up API actions, and it lives inside ChatGPT at its own URL. You visit it when you need it, and you share it through the GPT Store.

A Claude skill is a folder of instructions on your own machine (or in your Claude project). It sits dormant until your request matches its description, then it activates inside your normal conversation. Several can be active at once, and they stack.

	Custom GPT	Claude skill
What it is	A hosted persona with instructions, knowledge files, and actions	A local file of instructions, mostly a SKILL.md
Where it lives	On OpenAI’s servers, inside ChatGPT	On your disk or in your Claude setup
How you use it	You open it, like visiting a specialist	It triggers inside your normal Claude when relevant
How many at once	One per conversation	As many as match the task
Sharing	One link, via the GPT Store	Copy a folder, clone a repo, or install from a catalog
Can you read what it does?	No, instructions are hidden	Yes, it’s plain markdown
Works on your local files	No	Yes, in Claude Code
Setup for non-technical users	Very easy	Easy in the Claude app, a bit more work in Claude Code

If you want the fundamentals of the skill format first, our guide to what Claude skills are covers it in ten minutes.

The mental-model difference that explains everything

Here is the one idea that makes every row in that table make sense.

A Custom GPT is a separate assistant you visit. A skill is a behavior your existing assistant gains.

When you open a Custom GPT, you leave your regular ChatGPT and walk into a specialist’s office. The specialist is great at one thing. It doesn’t know what you were doing five minutes ago in another chat, and while you’re in its office, you lose access to every other specialist. When the task shifts, you walk out and find a different door.

When you install a Claude skill, nothing about where you work changes. You keep talking to the same Claude, in the same conversation, on the same project. What changes is what Claude knows how to do. Ask it to draft a cold email and the cold-email skill wakes up. Ask it to tidy the draft’s tone in the next message and a copy-editing skill wakes up alongside it. You never left the room. Nobody handed you off.

Almost every practical difference falls out of this. Custom GPTs are easy to share because a hosted assistant is just a URL. Skills compose because behaviors, unlike offices, can overlap. Custom GPTs are opaque because you’re a guest in someone else’s building. Skills are auditable because the behavior is a text file in your own folder.

Hold this frame and the choice stops being a spec comparison. It becomes a simpler question: do you want another assistant, or do you want your assistant to get better?

What Custom GPTs genuinely do better

We test Claude skills for a living, so believe us when we say the GPT ecosystem earns real points here. Four things Custom GPTs simply do better:

Zero-install sharing. A Custom GPT is a link. Send it to a colleague and they’re using it eight seconds later, no files, no setup, no explanation. Skills require the recipient to put a folder in the right place. That gap has narrowed (installing in the Claude app is now mostly drag and drop, and our installation guide walks through every method), but a link still beats a folder for frictionless handoff.

Knowledge files as a first-class feature. Upload twenty PDFs to a Custom GPT and it retrieves from them automatically. Skills can ship reference files, and Claude Projects can hold documents, but the GPT builder’s “drop your docs here” flow is more obvious and requires no thought about structure.

Hosted actions. A Custom GPT can call external APIs that its creator configured, with authentication handled server-side. You, the user, do nothing. The Claude equivalent (MCP servers) is more powerful but runs on your machine and asks more of you.

A creation flow anyone can finish. The GPT builder is a chat: describe what you want, answer some questions, done. Writing a skill means writing markdown with a frontmatter block. It’s honestly not hard, and we’d argue the result is better, but “fill in this file” loses to “just talk” for a first-time creator on a Tuesday afternoon.

If your whole use case is “give my team a shared assistant with our FAQ loaded into it, today, with zero IT involvement,” a Custom GPT is a fine answer and we won’t pretend otherwise.

What Claude skills do better

Now the other side of the ledger, which is where we spend our days.

Composition. This is the big one. A Custom GPT conversation has exactly one persona. Your writing GPT can’t consult your fact-checking GPT. With skills, ten can be installed and any relevant subset activates together. In one real session we watched a founder’s Claude use a research skill to profile a prospect, a cold-email skill to draft the outreach, and a humanizer skill to strip the AI tells from the final text. Three specialists, one conversation, no copy-pasting between tabs. There is no Custom GPT equivalent of this. It’s the difference between hiring one contractor at a time and having a staffed workshop.

It works where your files are. In Claude Code, skills operate on your actual computer: your drafts folder, your spreadsheet exports, your codebase. A Custom GPT can only see what you paste or upload into the chat window. If your work lives in files, this one difference dwarfs everything else.

No context-switching. Because skills trigger inside your normal conversation, you never abandon context to go find the right assistant. The thread that started as “summarize this call” can flow into “now draft the follow-up” without a change of venue. GPT users know the tab shuffle we’re describing.

Auditability. A skill is a markdown file. Open it and you can read, word for word, what it will tell Claude to do. A Custom GPT’s instructions are hidden by default, and the store has hosted plenty of GPTs whose prompts quietly injected marketing agendas or upsells. When we review a skill at SkillProof, “we read the whole file” isn’t a bonus step. It’s possible because the format is transparent, and that transparency should matter to you even if you never read a single file yourself, because it means people like us can.

Portability. Your skills are yours. They’re files. If Anthropic changed its pricing tomorrow, your skills would still exist on your disk, readable by any future tool that speaks markdown. A Custom GPT exists at OpenAI’s pleasure, on OpenAI’s servers, under OpenAI’s terms.

The GPT Store lesson

The GPT Store launched in January 2024 with real excitement, and within months it had a problem the whole industry now recognizes: flooding. Millions of GPTs, most of them thin wrappers around a two-line prompt, near-duplicates of each other, abandoned after launch day, with no meaningful review between a creator clicking publish and you clicking use. Finding a good one became a lottery where the ticket price was your afternoon.

We’re not telling this story to dunk on OpenAI. We’re telling it because the Claude skill ecosystem is running the same experiment right now, with the same result. Skills are even easier to publish than GPTs (any GitHub repo counts), and in our testing roughly half of the community skills we pull from GitHub fail on first try: they don’t trigger, they reference files that don’t exist, or they were written for a Claude version three updates ago. We documented the failure patterns in why half of Claude skills don’t work.

Same story, different ecosystem. Open publishing plus zero review equals a quality lottery, every time. It’s exactly the problem we built SkillProof to counter: every skill in our catalog gets installed on a clean machine and tested against real work before it earns a pass, a “works after setup,” or stays in the queue. The lesson from the GPT Store isn’t that open ecosystems fail. It’s that they need a testing layer someone actually maintains.

When to use which: five scenarios

1. You want your team to query the company handbook. A shared assistant with uploaded documents, used by non-technical people, shared as a link. Verdict: Custom GPT. This is the format’s home turf.

2. You write a newsletter and want consistent voice, structure, and editing across every issue. You need behaviors that stack (drafting plus editing plus de-AI-ing the prose) inside one working session, ideally operating on your actual draft files. Verdict: Claude skills. Start with the copywriting skill and browse our writing category for the rest.

3. You’re a founder doing your own sales outreach. Research a prospect, draft the email, refine the follow-up, all in one thread, pulling from notes on your machine. Verdict: Claude skills. The tested cold-email skill is where we’d begin.

4. You built a quiz bot for your audience and want to share it in one click. Public-facing, personality-driven, zero setup for strangers. Verdict: Custom GPT. Skills aren’t built for anonymous public distribution, and pretending otherwise helps no one.

5. You currently juggle four Custom GPTs for one workflow. If you’re opening Research GPT, then Writer GPT, then Editor GPT, then pasting outputs between them, the workflow itself is telling you it wants to be one conversation with four skills. Verdict: Claude skills, and the section below shows the migration.

Porting a Custom GPT into a SKILL.md

Good news: the hard part of your Custom GPT, the instructions you refined over months, ports almost directly. Here’s a real example.

Say your GPT is “LinkedIn Post Polisher” and its instruction box reads:

You are a LinkedIn editor. When given a draft post, tighten it to under 1,300 characters, open with a hook line under 12 words, break walls of text into short paragraphs, cut corporate jargon, and end with one question to drive comments. Never use hashtags. Keep the author’s voice.

The skill version is that same knowledge inside a small markdown file. Create a folder called linkedin-polisher containing one file, SKILL.md:

---
name: linkedin-polisher
description: Edits and tightens LinkedIn post drafts. Use when the
  user shares a LinkedIn draft or asks to polish, shorten, or
  improve a post for LinkedIn.
---

You are editing a LinkedIn post. Apply these rules:

1. Keep the final post under 1,300 characters.
2. Rewrite the first line as a hook of 12 words or fewer.
3. Break any paragraph longer than 3 lines into shorter ones.
4. Cut corporate jargon ("leverage", "synergy", "thrilled to announce").
5. End with one specific question that invites comments.
6. Never add hashtags.
7. Preserve the author's voice; edit, don't rewrite.

Show the edited post first, then a short list of what you changed.

Two things changed in translation. First, the persona framing (“You are a LinkedIn editor”) became a description field that tells Claude when to activate, because unlike a GPT, a skill isn’t always on; it triggers when your request matches. Write that description carefully, since a vague one is the most common defect we find in testing. Second, the instructions became a numbered checklist, which in our testing follows far more reliably than paragraph-style prose.

Drop the folder into your skills directory (the install guide covers the exact paths for the Claude app and Claude Code) and the next time you say “polish this LinkedIn draft,” your normal Claude becomes your polisher. No separate assistant to visit, and it now stacks with everything else you’ve installed.

Knowledge files port too: put them in the folder next to SKILL.md and reference them from the instructions. Hosted actions are the one piece without a direct equivalent; those map to MCP servers, which are a bigger topic than this article.

FAQ

Can I use Claude skills without knowing how to code? Yes. A skill is a folder you place in the right spot, and in the Claude desktop app that’s a settings screen, not a terminal. Claude Code asks slightly more of you, but “slightly” means copying a folder, and our free skills roundup links tested options to start with.

Do skills cost extra? The skill files themselves are free or cheap (ours are a few dollars per pack, and many good ones cost nothing). You pay for Claude itself through your normal plan. There’s no marketplace cut or per-use fee baked into the format.

Can a skill do what GPT actions do, like calling an API? Not by itself. A skill changes behavior; reaching external services is the job of MCP servers, which pair with skills. For most writing, editing, and analysis workflows you won’t miss actions at all.

Is there an official skill store like the GPT Store? Anthropic ships some official skills, but there’s no central store. Skills spread through GitHub and catalogs like ours, which is precisely why we test everything before listing it. Unreviewed distribution is how the GPT Store became a lottery.

Can I run my Custom GPT and Claude skills side by side? Of course. Nothing about installing skills touches your ChatGPT account. Plenty of our readers keep a shared team GPT for the handbook use case and move their personal working sessions to Claude with skills. Use the frame from this article: assistants you visit stay GPTs, behaviors you want your daily driver to gain become skills.