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

What each actually is.

A Custom GPT is OpenAI's packaging format for a task-specific assistant inside ChatGPT. It wraps a system prompt, optional knowledge files, and optional Actions (HTTP calls to external APIs) behind a named assistant. Users invoke it inside ChatGPT or, if configured, by mention in a shared workspace. Distribution runs through the GPT Store.

A Claude Skill is Anthropic's packaging format for a self-contained capability. It is a directory with a SKILL.md instructions file, optional scripts, optional references, and optional MCP server bindings. Skills run inside Claude Code, the Claude Agent SDK, Claude.ai, or any harness that loads them. The model decides when to invoke a Skill based on its description and the user's request.

The decision table.

The fastest way to pick is to read the table once and see where your constraints land.

Dimension	Custom GPT	Claude Skill
Best for	Public-facing assistants, consumer-style use, GPT Store distribution	Internal operations, code workflows, multi-tool pipelines
Deployment surface	ChatGPT (web, mobile, desktop)	Claude Code, Claude.ai, Agent SDK, custom harnesses
Customization depth	System prompt, knowledge files, Actions	Full directory: prompts, scripts, references, MCP bindings
Code execution	Code Interpreter (sandboxed Python)	Arbitrary shell, reads/writes local files, full tool access
External tool integration	Actions (REST API calls via OpenAPI)	MCP servers (stateful, typed, streaming)
Memory / context	ChatGPT conversation memory	Claude context window plus file-based state
Cost model	Bundled into ChatGPT seat pricing	Pay-per-token on Claude API
Ecosystem maturity	Older, larger GPT Store, more public discovery	Newer, smaller, growing fast inside engineering teams
Privacy / data control	Routes through OpenAI; enterprise opt-outs available	Runs anywhere Claude API runs (Anthropic, Bedrock, Vertex)

When to build a Custom GPT.

Build a Custom GPT when the primary use pattern is conversational and the audience lives in ChatGPT already.

1. Public-facing assistants. A "talk to our product" bot that lives on the GPT Store.
2. Consumer-style tools. A meal planner, a writing coach, a legal-document explainer. One system prompt, light tool use.
3. Teams standardized on ChatGPT Enterprise. The administrative path is shorter when the assistant lives inside the same seat license as the team.
4. Lightweight Actions against a REST API. One or two external calls against an OpenAPI-defined endpoint.

When to build a Claude Skill.

Build a Claude Skill when the capability needs to run inside an agentic workflow, touch files, or orchestrate multiple tools.

1. Code-adjacent workflows. Anything that reads and writes files, runs shell, or commits to git. Claude Code is the native home.
2. Multi-tool pipelines. A Skill that orchestrates Firecrawl, DataForSEO, and a local writer. MCP makes this clean.
3. Internal operations with engineering support. Teams that can version-control a Skill directory and ship updates through git.
4. Privacy-sensitive use cases. Running Claude on Amazon Bedrock or Google Cloud Vertex keeps data inside your cloud boundary.
5. Agent SDK deployments. If you are building a product that embeds Claude, Skills are the official packaging format.

When to build both.

Some needs have two audiences. Build both when the same capability has a public-facing consumer variant and an internal power-user variant.

1. A brand that wants a public "ask our methodology" GPT and an internal "run our methodology" Skill.
2. A team split between sales (ChatGPT-heavy) and engineering (Claude Code-heavy) on the same underlying knowledge base.
3. A product launch where the consumer preview lives in the GPT Store and the enterprise tier runs the full Skill via the Agent SDK.

A worked example.

The use case: a sales enablement assistant for a 50-person SaaS team. The assistant needs to answer product questions, draft outreach, and pull current pricing from an internal API.

Option A: Custom GPT

The team already pays for ChatGPT Enterprise. Most reps are in ChatGPT daily. Build pattern:

Name: SaaS Co Sales Buddy
System prompt: |
  You are the SaaS Co sales assistant. You help reps draft
  outreach, answer product questions, and pull live pricing.

  Voice: direct, specific, no jargon. Cite the source
  document when you use knowledge-base content.

Knowledge files:
  - product-messaging-v7.pdf
  - objection-handling-playbook.md
  - pricing-approval-matrix.md

Actions:
  - GET /pricing/{plan}   (internal pricing API, OAuth)
  - POST /crm/log-call    (Salesforce lead update)

Option B: Claude Skill

The team runs a lot of deal-prep workflows that pull from Salesforce, LinkedIn, and the public web simultaneously. Build pattern:

# SKILL.md

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

You are the SaaS Co sales assistant.

## Instructions
1. On "prep me for {account}", run the account-research
   sub-agent with Common Room + LinkedIn MCP.
2. On "draft outreach to {person}", load the
   references/objection-handling.md file and the voice guide,
   then produce a 3-paragraph email.
3. On pricing requests, call the pricing MCP server.

## References
- references/product-messaging-v7.md
- references/objection-handling.md
- references/voice-guide.md

## Scripts
- scripts/pull-salesforce-context.py
- scripts/generate-outreach.py

Which to pick

For this team, the answer is probably both. The Custom GPT handles the daily 80%. Answering product questions, drafting quick outreach, looking up pricing. The Claude Skill handles the weekly 20%. Deep account prep runs that span multiple tools and take longer than a chat turn.

If you have to pick only one: the Custom GPT wins on adoption velocity because the reps are already in ChatGPT. The Skill wins on capability ceiling. The right question is where the bottleneck is. Reps not adopting the tool, or the tool not being powerful enough.