Claude Skills vs. Custom GPTs: Which is Better for Automating Internal Processes

If you've been using AI in your company for a few months, you've noticed something: a generic assistant is good for everything, but mediocre at everything. When you need it to follow your conventions, consult your data, and execute tasks exactly the way your team does, you need something more.

The two major market answers to this problem are Claude Skills (Anthropic) and Custom GPTs (OpenAI). Both allow you to package instructions, knowledge, and tools into a "specialist agent" that any member of your team can invoke. But they are very different architectures, with vastly different implications for privacy, portability, cost, and what they can truly achieve.

This article compares both options with technical and business criteria, so you can choose the one that best suits your needs.

What are they?

Claude Skills

Skills, as explained in our dedicated guide, are modular packages of instructions in Markdown format with YAML frontmatter. They live in file system folders, work in Claude Code, Claude Cowork, and Claude.ai, and use a progressive disclosure system: they are only loaded into context when Claude detects they are relevant to the task.

A Skill is basically an SKILL.md file with instructions, to which you can optionally attach scripts, references, and examples. They are version-controlled with Git, shared with git clone, and can include executable code.

Custom GPTs

A GPT is a customized version of ChatGPT that you configure within the OpenAI ecosystem. It is created from the ChatGPT web interface (or via API using Assistants), given a name, a description, natural language instructions, knowledge files, and actions (calls to external APIs). It is published in the GPT Store or kept private for your organization.

Everything lives within OpenAI's infrastructure: the instructions, the uploaded files, the conversations. Users access them via the ChatGPT interface or through enterprise integrations.

Quick Comparison

Feature	Claude Skills	Custom GPTs
Where they live	Local files / Git repos	OpenAI servers
Format	Markdown + YAML	UI Configuration
Version Control	Native Git	Limited (history within UI)
Privacy	Data never leaves your environment	Passes through OpenAI infrastructure
Portability	Total (just files)	Tied to OpenAI
Code Execution	Local scripts, MCP tools	Code Interpreter in sandbox
Access to Company Data	Via MCP, scripts, local RAG	Via "actions" (external APIs)
Automatic Activation	Progressive disclosure	User must select GPT
Base Price	Included in Claude plan	ChatGPT Team/Enterprise plan
Ideal for	Development, technical flows, sensitive data	Non-technical business agents

Key Differences in Depth

1. Where the Knowledge Lives

This is the most important difference and the one with the biggest implications.

Claude Skills live as files in your system: a Git repository, a project folder, or a Claude Code configuration directory. This means that:

• You can version them with Git just like any other code.
• You can share them with your team via PRs.
• You can perform diffs, revert, and branch.
• If you stop using Claude, you take the Skills with you. They are standard Markdown files.

GPTs live within OpenAI. There are no files you can clone, version, or migrate. If you ever decide to switch providers, all the work of building the GPT remains within the platform. This is a classic case of vendor lock-in.

For companies that value portability and control over their digital assets, Skills win clearly.

2. Privacy and Sensitive Data

With GPTs, every time someone uses them, the prompt and attached files pass through OpenAI servers. Enterprise plans include commitments not to train with your data and limited retention, but the data leaves your infrastructure. For legal documents, contracts, medical records, or sensitive financial information, this can be a problem according to GDPR and the European AI Act 2026.

With Claude Skills, especially in Claude Code with local models via Ollama or self-hosted, you can build a flow where the data never leaves your machine. Even with Claude in the cloud, the scripts and reference files execute locally; only the synthesized prompt reaches the model. Combined with self-hosted MCP servers and local RAG, it is an architecture suitable for sensitive data.

3. Real Capabilities

GPTs excel at:

• Business agents created without programming (literally configured on a web page)
• Publishing in the GPT Store for any ChatGPT user to access
• Simple integration with external APIs via "actions" (OpenAPI spec)
• Integrated Code Interpreter for data analysis, graphs, and Python execution in a sandbox

Skills excel at:

• Executing local commands (modifying files, running tests, compiling)
• Complex flows with multiple steps orchestrated by sub-agents
• Integration with system tools (Git, Docker, databases, build systems)
• Composition: one Skill can invoke other Skills and specialized sub-agents
• Progressive disclosure: thousands of Skills without penalizing context

In practice, GPTs are more accessible for non-technical teams, and Skills are more powerful when you need to touch files, execute code, or integrate with infrastructure.

4. Automatic Activation

A subtle but important difference. With GPTs, the user has to explicitly choose which GPT to open. If your company has 40 internal GPTs, the employee must remember which one to use at any given moment.

With Claude Skills, Claude automatically detects which Skill applies based on the description and context of the conversation. If you have a Skill to generate React components and another to review PRs, Claude activates the correct one without the user having to choose. It is an ergonomic difference that changes how the team works in the medium term.

5. Development and Maintenance

GPTs are edited in a web interface. Simple but limited: there is no serious change history, you cannot branch, and you cannot do code reviews. If three people touch the same GPT, someone overwrites another's changes.

Skills are edited like any file: in VS Code, in Cursor, in Claude Code. Pull requests, reviews, branches, tests. For companies with an engineering culture, this is night and day.

6. Cost

Custom GPTs are included in ChatGPT Team plans (around €25/user/month) and Enterprise. For occasional users, it is affordable. For thousands of employees using several GPTs a day, the cost grows quickly.

Claude Skills are included in Claude.ai or Claude Code at no extra cost per Skill. You pay for model usage, not for the Skills themselves. If you self-host with open-source models, the Skills are free to operate (you only pay for infrastructure).

When to Choose Which

Choose Custom GPTs if:

• Your team is non-technical and needs to create agents quickly
• Use cases are mainly conversational (Q&A, brainstorming, writing)
• You already have a ChatGPT Team or Enterprise subscription
• You want to publish agents for external use or in the GPT Store
• The data handled is not critical in terms of privacy
• You do not need deep integration with your infrastructure

Choose Claude Skills if:

• You handle sensitive data (health, legal, financial)
• You want total control over the code and the ability to version it
• Your team already works with Git and development workflows
• You need to execute real code (not just conversation)
• The agents must integrate with your technical stack (repos, databases, internal APIs)
• You value portability and want to avoid lock-in
• You are automating software development or technical tasks

Real-World Use Cases

Working Custom GPT Example

A digital marketing agency created a GPT called "Brand Copy Reviewer." They uploaded the style guide, examples of approved pieces, and tone guidelines. Any team member, technical or not, can paste text and receive suggestions aligned with the brand. No configuration or installation is required. It works from ChatGPT web, app, and Slack. Cost: the Team plan they already had.

Working Claude Skill Example

A development company created a Skill called "deploy-pr" that orchestrates the entire pull request deployment process: runs tests, builds the container, deploys to staging, executes smoke tests, and notifies Slack. The Skill lives in the repository, is updated with PRs, and any developer invokes it with /deploy-pr. It works because Skills can execute real scripts and connect with Docker, AWS, and Slack. A GPT could not do this: it cannot execute arbitrary code on your infrastructure.

What about native agents from other platforms?

Microsoft has Copilot Studio and Google has Gemini Gems. Both are valid alternatives if you are already deeply embedded in those ecosystems.

Copilot Studio excels in integration with Microsoft 365 and Power Platform. If your company lives in Teams, SharePoint, and Dynamics, it makes sense. It is philosophically close to Custom GPTs: UI configuration, internally publishable agents.

Gemini Gems is the equivalent in Google Workspace. Use it if you work with Gmail, Docs, Sheets, and Drive. Less mature than the other two but progressing quickly.

None offer the portability of Skills (they all live within their respective platforms) or the technical flexibility of a file-based Markdown system.

Can they be combined?

Yes. The norm in mature companies is to use both:

• GPTs for business agents used by non-technical teams: writing, internal customer service, Q&A on public documentation
• Skills for technical agents used by development, operations, data, finance with sensitive data

It is not an exclusive decision. The useful criterion is: if the agent touches sensitive data or infrastructure, use Skills; if it is conversational and accessible, use GPT.

Common Mistakes to Avoid

Choosing by Brand, Not by Use Case

"We use OpenAI because it was first" or "We use Claude because it's from Anthropic." The correct question is: what does the agent do, with what data, with which users, and in what flow. The brand is secondary.

Underestimating Future Migration Costs

GPTs remain within OpenAI. If you decide to change in two years, you start from scratch. Skills travel with a git clone. When building corporate assets long-term, this detail matters.

Ignoring Privacy Until Someone Asks

If your GPT handles personal data and a client asks where it is processed, you must have the answer. In many industries (banking, healthcare, legal), the answer "on OpenAI servers in the US" is unacceptable. Thinking about it beforehand saves explanations later.

Building a Giant GPT Instead of Several Specialized Ones

The same mistake as with Skills: an agent that does everything mediocrely performs worse than three that do one thing well. Divide by task, not by team.

The Future: Interoperability via MCP

The Model Context Protocol (MCP) that powers Anthropic is already an open standard and has been adopted by OpenAI, Google, Microsoft, and dozens of tools. As it matures, the line between "Claude Skill" and "OpenAI GPT" will blur: both will be able to call the same MCP servers to access data and tools.

This means that, in the medium term, the critical thing is not choosing Claude or OpenAI, but building your data and tool layer in a portable way via MCP. This makes switching providers trivial.

Our recommendation: build the MCP layer first, use the Skills and GPTs that fit best at the moment, and keep portability as the guiding principle.

How We Can Help

At Navel Digital, we design and implement both Claude Skills and Custom GPTs, depending on the client's use case. We analyze which processes in your company are candidates for automation, what technology best fits your stack and privacy requirements, and build the agents with an MCP-portable architecture to prevent lock-in.

If you are evaluating how to start packaging your team's knowledge into specialist agents, let's talk. The first consultation is without obligation.