The answer depends entirely on where you do your work. Custom GPTs live in ChatGPT, a web interface optimized for conversation. Claude Code skills live in Claude Code, a terminal and IDE tool optimized for development workflows. If your workflow happens in a browser and does not touch code or local files, Custom GPTs are the faster path. If your workflow happens in a terminal and needs access to your codebase, SKILL.md files are the right choice.
TL;DR: Use a Custom GPT for conversational, web-based workflows that non-developers can access without setup. Use a Claude Code skill for development workflows that need to interact with a codebase, run in a terminal, or integrate with local tools. At AEM, we build Claude Code skills for precisely that second category.
What is a Custom GPT?
A Custom GPT is a preconfigured ChatGPT assistant deployed on OpenAI's platform. You set a system prompt, optionally add files for knowledge retrieval, configure available tools (web browsing, DALL-E image generation, Code Interpreter), and publish it. Users access it through the ChatGPT web interface or mobile app. No installation, no terminal, no configuration files.
Custom GPTs are accessible: anyone with a ChatGPT account can use a published GPT without knowing what a system prompt is. This accessibility is their primary advantage for non-technical workflows — customer support scripts, content generation, document analysis — where the users are not developers. OpenAI reports that more than 3 million Custom GPTs were created within the first two months of the feature being available, reaching 1,500 new additions per day at peak growth (OpenAI, 2024).
The limitation is the environment. Custom GPTs operate entirely within ChatGPT. They cannot read files from a developer's local machine, run commands in a terminal, or integrate with IDE tools unless the user manually pastes content into the chat. They are conversational tools by design.
What is a Claude Code skill?
A Claude Code skill is a SKILL.md file that Claude Code reads at startup and activates when a matching request appears in a coding session. It contains a description for trigger control, step-by-step instructions for the workflow, and optional reference files for domain knowledge. It lives in .claude/skills/ in a project directory or the user's home directory.
At AEM, skills are built for workflows that developers execute repeatedly inside their development environment: code reviews, documentation generation, debugging sequences, deployment checklists. The skill activates in the context where the work already happens, without requiring the developer to switch to a browser.
The tradeoff is accessibility. A Claude Code skill requires Claude Code CLI or an IDE extension, a project directory structure, and familiarity with the SKILL.md format. It is not a tool for non-technical users. The payoff for that setup cost is meaningful: research by Gloria Mark at UC Irvine found developers take an average of 23 minutes to regain deep focus after a single context switch (University of California, Irvine, 2023). A skill that runs in the terminal eliminates the switch entirely.
"Developers don't adopt AI tools because they're impressive, they adopt them because they reduce friction on tasks they repeat every day." — Marc Bara, AI product consultant (2024)
The friction distinction: a Custom GPT requires opening a browser. A skill is already running in the terminal where the developer is working.
What can each tool do that the other cannot?
Custom GPTs have one capability Claude Code skills cannot match: web-accessible deployment. Any ChatGPT account holder can use a published GPT immediately, with no setup on their end. Claude Code skills have the inverse advantage: direct access to local files, terminal tools, and automatic workflow triggering. The gap is environmental, not a quality difference.
Custom GPTs can:
- Deploy to non-developer users with no setup requirement on their end
- Provide a web-based, mobile-accessible interface
- Use Code Interpreter to run Python code in a sandboxed environment
- Browse the web in real time during a conversation
- Share a consistent assistant experience across a team via a shared GPT link
Claude Code skills can:
- Trigger automatically when the developer's request matches the skill's description, without the developer explicitly invoking anything
- Access local files, codebases, and terminal tools directly via MCP server integration
- Carry detailed workflow instructions (2,000+ words) without impacting performance on unrelated tasks, because they load only when triggered
- Work offline, within a local development environment, without sending data to a web service
- Integrate with Claude Code hooks and subagents for complex multi-step workflows
The MCP integration point is significant. The MCP public server registry grew from 1,200 servers in Q1 2025 to 9,400 servers by April 2026, and average time to connect a SaaS tool to an AI agent fell from 18 hours of custom code to 4.2 hours with MCP (Pento, 2025). Claude Code skills inherit that ecosystem directly.
For more on how skills interact with Claude Code's broader toolset, see How Do Claude Code Plugins Relate to Skills?.
How do you decide which to use?
The decision turns on three variables: who the users are, where the work happens, and whether automatic triggering is required. Non-developers on the web point to Custom GPT. Developers in a terminal with local file access point to a Claude Code skill. Work through the four questions below to confirm.
Who are the users? If the users are non-developers who access the tool through a web interface, Custom GPT. If the users are developers who work in a terminal or IDE, Claude Code skill.
Where does the work happen? If the workflow involves reading or modifying files in a local codebase, running commands, or integrating with local tools, Claude Code skill. MCP tools give Claude Code skills direct access to local environments that Custom GPTs cannot reach.
Does the workflow need to trigger without explicit invocation? Claude Code skills can activate automatically when a developer's request matches the description. Custom GPTs require the user to open ChatGPT and select the GPT. If automatic triggering matters, skills win.
Does the workflow require web access or image generation? Claude Code does not have native web browsing in the same conversational mode Custom GPTs have. If the workflow requires real-time web research or image generation as part of the core task, Custom GPT is the more natural fit. An industry survey found 94% of workers perform repetitive, time-consuming tasks that could be automated (Kissflow, 2024): for the developer subset, the type of repetition determines which tool applies.
The honest case for Custom GPTs: they are production-ready without configuration. A Custom GPT can be deployed to a team in 20 minutes. In our skill-engineering work at AEM, we have measured the build time for a production-ready Claude Code skill at 2 to 4 hours of focused work: the trigger condition, instruction quality, and output contract each require explicit testing before the skill is reliable.
When does neither fully fit?
One case: you want a non-developer audience but your workflow requires deep code access. Neither Custom GPTs (no local file access) nor Claude Code skills (requires developer tooling) are the right fit here. The solution is building an application with Claude's API that provides its own interface and its own tool integrations.
Another case: you use both Claude Code and ChatGPT and want the workflow to work in both places. A SKILL.md file can be adapted into a Custom GPT system prompt and vice versa, but the two formats are not directly compatible. You maintain two separate configurations.
This limitation applies to single-workflow portability. For organizations that need cross-platform consistency, the practical answer is picking one platform and standardizing on it rather than maintaining parallel configurations. OpenAI's 2025 enterprise report found that BBVA maintains over 4,000 active GPTs internally, which suggests that platform standardization at scale is the norm, not the exception (OpenAI State of Enterprise AI, 2025).
For a broader look at which workflows suit skills versus other Claude Code component types, see the pillar article Claude Code Skills vs Agents vs Prompts: When to Use Which.
Frequently Asked Questions
The two tools cover different primary use cases, but they are not mutually exclusive. Custom GPTs fit non-developer teams with web-accessible conversational workflows. Claude Code skills fit developers who need terminal integration, local file access, and automatic triggering. Most developer teams can use both, as long as the workflow is matched to the right environment.
Can a Custom GPT and a Claude Code skill do the same workflow? Usually yes, with modifications. The underlying instructions can be adapted from one format to the other. The difference is in the execution environment: Custom GPTs run in ChatGPT, skills run in Claude Code. A skill that requires local file access cannot be directly ported to a Custom GPT.
Which is cheaper to build and maintain? Custom GPTs are faster to build: write a system prompt, publish, done. Claude Code skills require more upfront investment in description writing, trigger testing, and output contract definition. Maintenance is similar once both are established: update the instructions when the workflow changes. The Stack Overflow Developer Survey 2024 found that 61% of developers spend more than 30 minutes per day searching for answers to problems, which is exactly the kind of friction a well-built skill eliminates without ongoing maintenance cost (Stack Overflow, 2024).
Can I use Claude models in a Custom GPT? Custom GPTs use OpenAI models, not Claude. If you want Claude-powered workflow assistance in a web interface, you would need to use Claude.ai or build a web application on Anthropic's API.
Do Custom GPTs have a character limit equivalent to SKILL.md? Custom GPT system prompts have a character limit, though it is not published officially. In practice, very long system prompts (5,000+ words) reduce response quality in Custom GPTs for the same reason they reduce quality in always-on Cursor rules: too much context loaded unconditionally. Claude Code skills avoid this via progressive disclosure. The Claude Code skill description index allocates 1% of the context window for all skill descriptions combined, with a per-entry cap of 1,536 characters, so a library of 50 skills stays well under 3% of total context (Claude Code documentation, 2025).
Which tool gives me more control over the workflow? Claude Code skills give more precise control: trigger conditions, output contracts, reference file loading, and hook integration all give you specific control points. Custom GPTs give you a system prompt and tool toggles. For complex workflows with multiple branching paths and quality checks, skills are more expressive.
Is there a Claude equivalent to Custom GPTs? Claude Projects on Claude.ai serve a similar function to Custom GPTs: a preconfigured Claude assistant with a system prompt and uploaded documents, accessible through a web interface. If you want the web-accessible conversational format with Claude as the model, Claude Projects is closer to a Custom GPT than Claude Code skills are.
Last updated: 2026-05-06