Custom GPTs vs Gems vs Claude Skills: Which to Build

Short answer: all three solve "stop retyping the same instructions," but they win in different places. Pick a Custom GPT if public sharing matters, a Claude Skill if you want the same assistant to run outside Claude too (Codex CLI, Cursor), and a Gem if you want a persona set up in Google Workspace in five minutes. Building the same assistant in all three means maintaining it in three separate places.

This isn't a "which is better" post. The real question is where to invest in a given assistant, and the answer depends on what you're building.

What's the core difference between a Custom GPT, a Gem, and a Skill?

Short answer: a Custom GPT and a Gem are persona-shaped — an assistant configured once with instructions and optional knowledge files — while a Skill is behavior-shaped: a packaged instruction module that teaches the model how to do X, complete with templates and decision rules.

In practice, that means a Custom GPT or Gem is a single identity: "you're an SEO consultant, talk like this." A Claude Skill is a task-specific procedure: "when someone asks for an SEO audit, follow these steps, use this template, apply this checklist." Skills have no character limit, which allows genuinely detailed, multi-page instruction sets; Custom GPTs cap out around 8,000 characters of instructions plus Actions.

Which one is locked in, and which is portable?

Short answer: Claude Skills are portable via an open standard; Custom GPTs and Gems are locked to their own platform. The SKILL.md open standard, defined in December 2025, is now read by dozens of agent tools including Codex CLI, Gemini CLI, Cursor, and JetBrains Junie — meaning you can write a Skill once and run it outside Claude too.

A Custom GPT only runs in ChatGPT, and a Gem only runs in the Gemini app and Google Workspace context. If you're building a production agent for Telegram, Discord, or WhatsApp, Skills are the only genuinely portable option among the three.

Where does each one win?

How do sharing and governance actually work?

Short answer: for public distribution, the Custom GPT's GPT Store is still the most mature channel — it hosted more than 3 million custom GPTs as of early 2026. Gems have no real Projects equivalent; the closest thing Gemini offers is Gems built around a persona rather than a shared workspace. Skills have no storefront, but they can be distributed inside a team through a repo or file share, versioned, and put through code review — a more controlled path for team governance than what a Custom GPT offers.

How does maintenance cost change as models change?

Here's my honest take: the least-discussed cost here is maintenance. A Custom GPT's instructions can sometimes shift behavior unexpectedly when the underlying model updates, because the instructions and the model's behavior are tightly coupled. A Skill reduces that risk by separating the task-specific procedure from the model's general behavior — even if the model changes, the "follow these steps" instruction stays the same. Gems experience this least, since they're persona-focused, but they also carry the shallowest customization depth to begin with.

Setting up all three systems for the same assistant in parallel ("I have users on every platform") is tempting, but it means keeping three separate instruction sets in sync. Forget to update one when you change an instruction, and users end up with an assistant that behaves differently depending on platform.

What should you build, and when?

• Building a public product or community tool where distribution and discoverability matter: a Custom GPT, thanks to the GPT Store's current scale, is the most mature option.
• Want the same procedure to run in Claude Code, Cursor, or another agent tool too: a Skill, because the SKILL.md format is portable.
• Want a fast, low-maintenance persona for a team working inside Google Workspace: a Gem — setup speed wins here.
• Teaching the model a long, multi-step procedure (an audit, a report template, a code-review checklist): a Skill, thanks to the lack of a character limit.

One lock-in warning: before investing heavily in a Custom GPT or Gem, think about whether you'll have to rewrite the assistant from scratch if you switch platforms — say, from ChatGPT to Claude. Writing instructions platform-agnostically from the start, grounded in solid prompt engineering patterns, makes that switch far cheaper. ChatGPT's newer context features like Computer History fall into the same lock-in logic — the deeper the context gets, the harder the switch becomes.

How costly is it, really, to set up all three at once?

Short answer: not double the maintenance load, but closer to triple — because each platform's instruction format, character limit, and test cycle differ. Picture a team that's set up its customer-support assistant as a Custom GPT (for public self-service), a Gem (for the in-Workspace support team), and a Skill (for an automation running through Claude Code). When product policy changes ("stop routing on this topic"), the update has to happen in three separate places, in three separate formats.

The practical fix is to keep a single "source instructions" file and derive each platform-specific version from it. The source file stays as plain text or Markdown, before platform-specific constraints (character counts, supported sections) get applied. That's more sustainable than keeping all three in sync fully by hand, but it's still not automatic — none of the three platforms offers a shared "instruction source" API, so syncing remains a manual step.

Frequently Asked Questions

What's the biggest difference between a Custom GPT, a Gem, and a Claude Skill?

A Custom GPT and a Gem are single assistant identities configured as a persona; a Claude Skill is a portable, no-character-limit instruction module that teaches the model how to perform a specific task. Skills can also run outside Claude via the SKILL.md open standard.

Which one is most portable?

Claude Skills are the most portable, since the SKILL.md open standard (December 2025) is readable by more than 30 agent tools, including Codex CLI, Gemini CLI, Cursor, and JetBrains Junie. Custom GPTs run only in ChatGPT, and Gems run only in the Gemini app.

Should I build the same assistant as both a Custom GPT and a Skill?

Usually not. Running both in parallel means keeping the same instructions in sync in two places, and forgetting an update leaves you with an assistant that behaves differently by platform. It's more sustainable to pick one based on your primary use case and move to a portable base (a text file, a repo) for the second only when you actually need it.

Can Custom GPTs be shared publicly?

Yes, through the GPT Store — it hosted more than 3 million custom GPTs as of early 2026, making it the most mature public-sharing channel among the three systems. Gem sharing is more limited, and Skills have no storefront but can be distributed inside a team via a repo or file share.