Custom GPTs vs Gems vs Claude Skills

Pick Gemini Gems if your work lives in Google Workspace and you want a usable assistant built in five minutes. Pick Claude Skills if you need long, detailed instructions with no character cap and want the same assistant to run everywhere, including outside the Claude app. Pick a Custom GPT if public sharing through the GPT Store or connecting to an external API matters more than anything else. All three solve the same surface problem: a reusable AI assistant with your instructions baked in. Underneath, they make very different bets, and two of those bets catch almost everyone off guard.

The two that surprise people: Gems push you toward a short instruction box (Google's own guidance is to keep instructions concise and cover persona, task, context, and format, then push detail into reference files), while a Claude Skill is just a SKILL.md file with no hard length limit that Anthropic published as an open, cross-platform standard in December 2025.

The 30-second decision

• You live in Gmail, Drive, Docs, and Calendar, and you want it working today: build a Gem.
• Your instructions are long and procedural (a code-review checklist, a brand style guide, a multi-step report format) and you want them to run in more than one place: write a Claude Skill.
• You want strangers to find and use your assistant, or you need it to call an external API or webhook: build a Custom GPT.
• You want persistent context for one ongoing body of work, like a single client or codebase, rather than a repeatable procedure: use a Claude Project, not a Skill.
• You are price sensitive and only need a personal helper: a Gem is free for Gemini users, while building a Custom GPT requires a paid ChatGPT plan.

What each one actually is

Gemini Gems: the Workspace-native quick build

A Gem is a saved Gemini configuration: a name, an instruction block, and optional reference files. Its real advantage is location. Gems now appear in the side panel of Gmail, Drive, Docs, Sheets, and Slides, so the assistant sits next to the documents it works on. Grant access and a Gem can read your email, summarize Drive files, and reference Docs you are editing. That proximity is the whole pitch. You are not copying text into a separate chat window and pasting the answer back; the helper is already where the work happens.

On capacity, Gemini Advanced offers a 1 million token context window and supports large uploads (Google cites up to roughly 1,500 pages of files), and a custom Gem can carry up to 10 reference files. So Gems are strong at ingesting big documents. A long policy manual, a quarter of meeting notes, a dense research PDF all fit. The catch sits on the instruction side. Google's own tips are to keep Gem instructions concise, covering persona, task, context, and format, which is fine for a tight persona but awkward once you have a long, branching procedure. Detail belongs in the reference files, not the instruction box.

Custom GPTs: the public marketplace and the API hook

A Custom GPT bundles instructions, uploaded knowledge files, and optional Actions, the feature that lets the GPT call an external API, webhook, or service. Two things set it apart. First, sharing: a GPT can be private, link-only, or published to the public GPT Store, where another user can find and run it without ever seeing how you built it. OpenAI reported more than 3 million GPTs created within two months of launch, though only a small fraction are public and active in the store. Second, building a Custom GPT requires a paid ChatGPT plan, while using one is broadly available.

Actions are what separate a Custom GPT from a glorified prompt. Hand it an OpenAPI schema and credentials, and a GPT can hit your booking system, query a database behind a webhook, or pull live pricing, then fold the result into its reply. That makes a Custom GPT the right shape when the assistant needs to do something in the outside world, not just reason over text you handed it. If you never need a live call and never plan to publish, the marketplace and Actions are weight you are not using, and a simpler tool will serve you better.

Claude Skills: the portable instruction file

A Claude Skill is a folder containing a SKILL.md file: YAML frontmatter with a name and description, then markdown instructions Claude follows when the skill applies. There is no character cap, and when the file grows you split it into reference files (reference.md, forms.md) that the skill loads on demand. Anthropic published Agent Skills as an open standard in December 2025, and the same skill runs across Claude.ai, Claude Code, the Claude Agent SDK, and the Claude Developer Platform. Because a skill is just files, you version-control it in git and a whole team shares one identical copy.

The load-on-demand mechanism is the quiet strength here. The model reads the short frontmatter description to decide whether the skill is relevant, then pulls the full body and any reference files only when it fires. So a Skill can be enormous on disk without bloating every conversation, because the detail loads only when the task calls for it. That is why a long, branching procedure that suffocates inside a Gem's instruction box sits comfortably in a SKILL.md.

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Gemini Gems vs Custom GPTs vs Claude Skills: capability table

The two gotchas most comparisons skip

Most side-by-side guides stop at the feature checklist and never warn you about the two things that actually bite once you start building. Here they are.

1. The Gems instruction box rewards brevity

Google's guidance is explicit that Gem instructions should stay concise and cover four things: persona, task, context, and format. The workaround is to push the heavy detail into the up-to-10 reference files a Gem can hold, and keep the instruction box to those four blocks. Think of the box as the casting brief and the reference files as the script. If your real need is a long, branching procedure with many conditional steps, a Gem is the wrong shape, and you will fight it on every edit.

2. SKILL.md is a portability standard, not a Claude-only feature

Because a skill is plain files, it loads in Claude.ai today, in Claude Code, and everywhere Claude runs, and it travels through git like source code. That changes who controls the asset. A Gem or a GPT is a configuration locked inside one company's product; if you leave, it stays. A Skill is yours, reviewable in a pull request and diffable line by line. If you expect to switch tools, or you want your team's writing standards approved the same way you approve code, that portability is the deciding factor.

Copy-paste build recipes

Build a Gem in 5 minutes

• Open gemini.google.com and click Gem manager in the lower left, then New Gem.
• Name it for its job, for example 'Weekly Status Drafter'.
• Write four short blocks: Persona (who it acts as), Task (the one job), Context (audience, tone, constraints), Format (bullets, table, or email). Google's tips note you do not have to use all four.
• Attach up to 10 reference files for anything too long for the instruction box, such as a style guide PDF.
• Open it from the Workspace side panel in Gmail or Docs so it can read the document you are working on, after you grant access.

Build a Custom GPT for sharing or API calls

• In ChatGPT (paid plan), open Explore GPTs, then Create.
• Use Configure to set name, description, and instructions directly rather than relying only on the chat builder.
• Upload knowledge files for reference material the GPT should cite.
• Add an Action only if you need a live API call; supply the OpenAPI schema and auth.
• Set sharing: keep it private while testing, switch to link-only for a team, or publish to the GPT Store for public discovery.

Write a Claude Skill

• Create a folder named for the skill, for example 'pr-review'.
• Add SKILL.md starting with YAML frontmatter: a name and a description that tells Claude when to use it.
• Below the frontmatter, write the full procedure in markdown. No character cap, so be as detailed as the task needs.
• When SKILL.md gets long, split details into reference.md or checklist.md and point to them by name from SKILL.md.
• Commit the folder to git so the same skill runs everywhere Claude runs and your team shares one copy.

Where your own files and memory fit

All three tools hold instructions well. Personal context is the weaker spot. A Gem reads your Google account, a Custom GPT reads what you upload, and a Skill carries the files you bundle, but none of them is a durable memory layer over your whole life of documents, photos, and notes spread across apps and devices. They know the procedure you wrote. None of them remembers the body of personal material you never re-paste.

MemX is a consumer AI memory app that fills that gap: an external memory layer over your own documents, photos, notes, and chats on Android, iOS, and WhatsApp, so an assistant can recall what you actually have rather than what you re-upload each session. It is private by architecture, with per-user keys, encryption at rest, and an on-device first pass, which is a different promise from a Gem reading your live inbox. Build a Gem, GPT, or Skill for how the assistant behaves; use a memory layer for what it remembers.

Bottom line

There is no single winner, only a best match for your constraint. Heavy Google Workspace user who wants speed and free access: Gems. Long, portable, version-controlled instructions that outlive any one app: Claude Skills. Public distribution or an external API integration: Custom GPTs. Decide on the one constraint that matters most to you, then build for it. All three are fast to build, so the only real cost is choosing wrong.