# Custom GPTs vs. Claude Skills: when to use which

<span class="byline">by John Morabito · April 18, 2026 · 9 min read</span>

**TL;DR**
- Custom GPTs win for public-facing use, GPT Store distribution, and teams already on ChatGPT.
- Claude Skills win for internal ops, Claude Code integration, code-heavy workflows, and MCP pipelines.
- If your team lives in Claude Code, build Skills. If they live in ChatGPT, build GPTs. If elsewhere, pick by integration surface.

## What each actually is

**Custom GPT.** OpenAI's packaging format for a task-specific assistant inside ChatGPT. Wraps a system prompt, optional knowledge files, and optional Actions (HTTP calls via OpenAPI) behind a named assistant. Distribution runs through the GPT Store.

**Claude Skill.** Anthropic's packaging format for a self-contained capability. A directory with SKILL.md, optional scripts, optional references, and optional MCP server bindings. Runs inside Claude Code, the Claude Agent SDK, Claude.ai, or any harness that loads them.

## The decision table

| Dimension | Custom GPT | Claude Skill |
|---|---|---|
| Best for | Public-facing, consumer | Internal ops, code workflows |
| Deployment surface | ChatGPT | Claude Code, Agent SDK, Claude.ai |
| Customization depth | Prompt + knowledge + Actions | Full directory, scripts, MCP |
| Code execution | Sandboxed Python | Arbitrary shell, local files |
| External tools | Actions (REST via OpenAPI) | MCP servers |
| Cost model | Bundled in ChatGPT seat | Pay-per-token |
| Ecosystem | Older, larger | Newer, engineering-heavy |
| Privacy | OpenAI infra by default | Anthropic / Bedrock / Vertex |

## When to build a Custom GPT

1. Public-facing assistants on the GPT Store.
2. Consumer-style tools. One system prompt, light tool use.
3. Teams standardized on ChatGPT Enterprise.
4. Lightweight Actions against an OpenAPI-defined REST endpoint.

## When to build a Claude Skill

1. Code-adjacent workflows. Reads/writes files, runs shell, commits to git.
2. Multi-tool pipelines orchestrating Firecrawl, DataForSEO, writers.
3. Internal operations with engineering support (git-versioned skills).
4. Privacy-sensitive use cases on Bedrock or Vertex.
5. Agent SDK product deployments.

## When to build both

1. Public "ask our methodology" GPT plus internal "run our methodology" Skill.
2. Teams split between sales (ChatGPT) and engineering (Claude Code) on the same knowledge base.
3. Consumer preview on GPT Store, enterprise tier on Agent SDK.

## A worked example: sales enablement for a 50-person SaaS team

### Custom GPT version

```
Name: SaaS Co Sales Buddy
System prompt: You are the SaaS Co sales assistant...
Knowledge files:
  - product-messaging-v7.pdf
  - objection-handling-playbook.md
Actions:
  - GET /pricing/{plan}
  - POST /crm/log-call
```

### Claude Skill version

```
---
name: saasco-sales-buddy
description: Sales enablement for SaaS Co reps. Triggers
  on "prep me for", "draft outreach", "pricing for".
---

## Instructions
1. On "prep me for {account}" → account-research sub-agent.
2. On "draft outreach" → load references/objection-handling.md
   and voice guide, then produce a 3-paragraph email.
3. On pricing → call pricing MCP server.
```

### Which to pick

For this team: probably both. GPT for the daily 80%, Skill for the weekly 20% of deep account prep. If forced to one, the GPT wins on adoption because reps already live in ChatGPT. The Skill wins on capability ceiling.

## FAQ

**Can I migrate from a Custom GPT to a Claude Skill?**

Yes. The system prompt becomes SKILL.md. Knowledge files become references/. Actions become scripts or MCP calls.

**Do Claude Skills work outside Claude Code?**

Yes. Skills are a packaging format. They run anywhere the Claude API is called with the right loader. Agent SDK, MCP, Claude.ai, custom harnesses.

**How is data privacy handled differently?**

GPTs route through OpenAI by default. Skills run wherever you run the Claude API, including Bedrock and Vertex.

**Which is cheaper to run at scale?**

Volume matters more than per-token rate. Small teams with heavy usage often win on ChatGPT seat bundling. Larger teams with selective power users often win on pay-per-token Claude.

**What about Google Gems?**

Closest to GPTs in shape. Lands in organizations already on Google Workspace with Gemini as default.