Claude Skills vs Custom GPTs: Different Answers to Reuse
Claude Skills are portable procedures; Custom GPTs are packaged chatbots inside ChatGPT. How they differ on portability, API access, sharing, and where each wins.
Verdict first: a Custom GPT is a destination — a packaged chatbot living inside ChatGPT; a Claude Skill is a capability — a portable procedure any conforming agent loads mid-task. Skills win on portability (an open standard 40+ tools read, ChatGPT-maker's Codex included), API access, and git-native sharing; GPTs win on consumer-facing packaged assistants with the Store's reach.
Key takeaways
- The shape difference explains everything: you visit a GPT and converse with it; a skill comes to your existing session when the task matches, then gets out of the way.
- Portability is the widest gap: skills are an open standard readable by 40+ tools (including OpenAI's Codex); a Custom GPT runs only inside ChatGPT and still can't be called from the API.
- Creation differs in kind: a skill is a Markdown file in git — diffable, reviewable, PR-able; a GPT is configured in a web builder with knowledge uploads and Actions.
- Availability skews opposite: claude.ai skills work on every plan including Free, while creating a GPT requires a paid ChatGPT plan.
- OpenAI's direction is telling twice over: Workspace Agents (April 2026) succeed GPTs for organizations, and Codex adopted the Agent Skills standard outright.
People compare these two because both answer "how do I stop re-explaining the same thing to my AI?" — but they answer it with opposite architectures. A Custom GPT is a destination: a packaged chatbot you (or the GPT Store's audience) go visit. A Claude Skill is a capability: a procedure your existing agent picks up mid-task, wherever you already work. Once you see that shape difference, every row of the comparison follows from it.
What each one is
A Custom GPT is a configured instance of ChatGPT: system instructions, up to 20 uploaded knowledge files (512 MB each), optional Actions (external APIs described by OpenAPI schemas), and capability toggles like code interpreter and canvas — assembled in a web builder, used inside ChatGPT, shareable privately, by link, to a workspace, or through the GPT Store. Creating one requires a paid plan; building happens on the web only.
A Claude Skill is a folder with a SKILL.md: frontmatter that says when it applies, a Markdown body that says what to do, optional bundled scripts. It isn't a place you go — it loads into whatever session you're already in when the task matches, on claude.ai (every plan, Free included), in Claude Code, via the API, and — because the format is an open standard — in 40+ other tools.
The comparison
| Claude Skills | Custom GPTs | |
|---|---|---|
| Shape | Procedure loaded into your session | Packaged chatbot you converse with |
| Runs in | claude.ai, Claude Code, API, Agent SDK, 40+ standard-compliant tools | ChatGPT only |
| API access | Yes — /v1/skills + code-execution container | No |
| Created with | A Markdown file (any editor, lives in git) | Web builder UI |
| Versioning & review | git diffs, PRs, plugin/API versions | Builder version history |
| Sharing | Repo checkout, ZIP, plugins, /v1/skills workspace | Link, workspace, GPT Store |
| External integrations | Bundled scripts + the agent's own tools | Actions (OpenAPI) |
| Cost to use | All Claude plans incl. Free (claude.ai) | Use varies by plan; creation requires paid |
| Composability | Many skills active per session, auto-routed | One GPT per conversation |
That last row is quietly the big one. Skills compose: your commit skill, migration skill, and style-guide skill all sit dormant in one session and fire per task. GPTs silo: each conversation happens inside exactly one GPT's configuration, and switching contexts means switching chatbots.
Where each wins
Choose skills for working procedures. Team conventions, runbooks, format enforcement, anything engineers touch — skills live in the repo, review like code, and follow the task across tools. The use-cases list is effectively a catalog of things GPTs are the wrong shape for.
Choose a Custom GPT for a packaged, audience-facing assistant. A branded tutor, a product helper, a niche persona with curated knowledge — something non-technical users visit. The Store's distribution into ChatGPT's user base has no skills equivalent, because skills deliberately aren't products.
Actions vs. scripts is the one dimension where GPTs hold real technical ground: a GPT with well-built Actions calls your live APIs conversationally, while an API-attached skill runs in a container with no network access. (In Claude Code, skills pair with MCP servers for live integrations — that combination outclasses Actions, but it's a Claude Code answer, not a claude.ai one.)
The direction of travel
Two 2026 facts say where this is heading. First, OpenAI introduced Workspace Agents (April 2026) as the org-side "evolution of GPTs" — Codex-powered, Slack-connected, schedulable, API-triggerable, with GPT-to-agent conversion promised — signaling that packaged chatbots are giving way to agents with capabilities even inside OpenAI's world. Second, and more striking: OpenAI's Codex adopted the Agent Skills standard itself. The procedure layer converged on Anthropic's format; the walled garden kept the storefront. If your reuse problem is "my agent should know how we do things," the ecosystem has effectively voted — write it as a SKILL.md, install it once, and it will outlive whichever chat product you're using this quarter.
Frequently asked questions
- Are Custom GPTs being discontinued?
- Not for individuals — GPTs and the GPT Store remain live as of mid-2026. For organizations, OpenAI introduced Workspace Agents in April 2026 as 'an evolution of GPTs' (business plans, research preview) with a promised GPT-to-agent conversion path and an unspecified eventual deprecation for org GPTs. No sunset has been published for individual GPTs.
- Can I use a Custom GPT through the OpenAI API?
- No — GPTs remain a ChatGPT-surface feature with no API endpoint, which matters if you want the same behavior in an app. Claude skills upload to /v1/skills and attach to API requests; OpenAI's new Workspace Agents can be API-triggered, but that's the successor product, not GPTs.
- Which is better for a team's internal workflows?
- Skills, and it isn't close: they live in the repo, get reviewed in PRs, version with git, and load in whatever agent each engineer runs — Claude Code, Cursor, Copilot, or Codex. A GPT is one shared chatbot in one vendor's app, managed through a web UI.
- Which is better for reaching an audience?
- A Custom GPT — that's the use case it's genuinely built for. The GPT Store gives a packaged assistant (a tutor, a niche helper) distribution to ChatGPT's enormous user base. Skills have no consumer storefront; they're infrastructure, not products.
- Is there an OpenAI equivalent of skills?
- Yes, literally: OpenAI Codex supports the Agent Skills open standard — directories with a SKILL.md, loaded from .agents/skills, with the same progressive disclosure. For coding agents, the two ecosystems have converged on Anthropic's format.
Related
- What Are Claude Skills? The Complete GuideClaude Skills explained: what a SKILL.md is, how progressive disclosure keeps skills cheap, where they run, and how to install or write your own.
- The Agent Skills Standard: One SKILL.md for Every AI ToolAgent Skills became an open standard in December 2025. Which tools read SKILL.md today — Copilot, Cursor, VS Code, Gemini CLI, Codex — and how to write portable skills.
- Skills vs Agents vs CommandsHow Claude Code's two extension mechanisms — subagents and skills — differ across three invocation patterns, with a decision table for choosing the right one.
- Skills vs MCP Servers: When to Use WhichSkills inject procedure into context; MCP servers expose tools and live data over a protocol. A decision framework, the combine pattern, and examples.
- How to Install Claude SkillsEvery way to install Claude skills: manual copy, the agentscamp CLI, GitHub repos, plugins, team distribution, and uploading to claude.ai.
- Claude Skills Use Cases: 20 Ideas Worth BuildingTwenty concrete Claude skills use cases — for engineers, writers, analysts, and ops — with the pattern behind each and links to installable versions.