ChatGPT vs Claude Skills: Reusable Workflows Compared

ChatGPT and Claude both support skills: reusable instructions with supporting resources for a particular workflow. Choose between them by where your work runs, which tools it needs, and how you will distribute and test it; sharing a file format does not guarantee identical behavior.

fenbs adds a shared place for reviewed prompts, project decisions, and progress that supported assistants can consult. It is useful when the procedure should outlive one assistant, while the executable skill still needs to be installed and tested in its intended app.

OpenAI’s Skills & Plugins guide↗ (opens in a new tab) describes reusable workflows for ChatGPT and Codex. Anthropic’s skills overview↗ (opens in a new tab) describes instructions, scripts, and resources loaded for relevant tasks. Neither side is limited to a saved paragraph that you must manually paste into every conversation.

What does each assistant reuse?

A useful skill captures a method: what inputs are required, which steps to follow, what a finished result contains, and when to stop or ask. Supporting files can provide templates, examples, or scripts. The method should be specific enough that you can tell whether the assistant followed it.

OpenAI’s Build skills documentation↗ (opens in a new tab) describes a folder containing SKILL.md and optional resources, with a name and description that help select the skill. Anthropic’s custom skill guide↗ (opens in a new tab) similarly describes a skill folder, metadata, instructions, and supporting files. The shared structure makes reusable authoring possible; the actual workflow still depends on its environment.

For example, a weekly status skill can request current tasks, group completed and unfinished work, and produce a short update. The skill defines the procedure. This week’s task statuses are separate information. Packing a current deadline into a reusable procedure creates a maintenance problem when the deadline changes.

The individual ChatGPT skills guide covers that product’s setup, while these Claude skill examples show possible workflows. The comparison here is about choosing and maintaining the reusable method across the two environments.

How does the assistant know when to use a skill?

The name and description are part of the behavior, not just filing labels. “Review meeting notes and produce decisions, open questions, and assigned actions” gives a clearer trigger than “Productivity helper.” Make the description narrow enough to avoid unrelated tasks and broad enough to match normal requests.

Both products describe selecting relevant skills. OpenAI also documents explicit selection, including skill mentions in ChatGPT and Codex. Treat the exact invocation and installation route as a property of the app you are using. A local coding environment and a browser conversation may expose different controls even when they use similar instructions.

Test selection as well as output. Try a direct request that should use the skill, a natural request with different wording, and an unrelated request that should leave it alone. If it activates too broadly, tighten the description. If it misses the intended task, add the words people actually use to describe that work.

Are a skill and a connected tool the same thing?

A skill explains a procedure. A tool provides an action or information source. A review skill might say to retrieve tasks, check outstanding decisions, and draft a summary; a connected service supplies the task records. Good instructions cannot provide access to a service that has not been connected or authorized.

OpenAI’s developer skill concepts↗ (opens in a new tab) separates workflow instructions from an MCP server’s information and controlled actions. MCP is a standard way for an assistant to use an outside tool. Anthropic also distinguishes skills from MCP connections. Keep that distinction in your setup and your expectations.

Before moving a skill, list its dependencies in ordinary language: reads a spreadsheet, creates a PDF, retrieves project tasks, or sends an approved message. Confirm each capability in the destination. If a tool is missing, change the workflow or provide the necessary input rather than letting the assistant pretend it completed the step.

For work held in fenbs, supported assistants connect through the integration guides and read the project record. The connection and the saved instructions do different jobs: one grants access; the other explains how to use the information.

Can you move one skill between ChatGPT and Claude?

Some of the contents can be portable: plain instructions, examples, reference text, and an output template. A common format helps, but portability is a testable property of the whole workflow. Tool names, file paths, scripts, permissions, and packaging may need adjustment before the same method works elsewhere.

Separate the common procedure from the environment details. Keep “Check every factual claim against the supplied source” in the shared instructions. Put “Use this tool to retrieve the source” in the version for the environment that actually has that tool. This avoids pretending that two apps have identical capabilities.

1. Copy the approved procedure and supporting examples, keeping the source version identifiable.
2. List every dependency and verify its equivalent in the destination environment.
3. Adapt installation, invocation, and tool instructions to the destination’s documentation.
4. Run representative tasks and inspect the result against the same acceptance criteria.
5. Record differences that matter, including any step that still requires a person.

Do not treat a successful import as a successful migration. A skill can be recognized while still generating the wrong file, overlooking a required approval, or using stale reference material. The ChatGPT-to-Claude guide covers the wider move; skill portability is one part of that transition.

How should you compare results fairly?

Use the same task, source material, and definition of success. Test the assistant in the environment where you intend to use it. Comparing a connected workflow in one product with an unconnected chat in the other mostly tests the setup, rather than the quality of the reusable procedure.

Suppose a small studio needs meeting notes turned into a project update. Prepare a sample with one decision, one uncertain deadline, and one unassigned action. A good result preserves the uncertainty, names the missing owner, and does not invent agreement. Those are stronger criteria than whether the summary sounds confident.

• Correctness: Does the result match the supplied evidence?
• Completeness: Does it contain every required part without invented details?
• Control: Does it stop at the approval boundary you specified?
• Reliability: Does the method work on ordinary variations, not just the easiest example?
• Maintenance: Can another person understand what to update when the process changes?

Try an incomplete input too. A well-designed workflow should identify what is missing and explain the next useful step. Record the failure and revise the instructions when it reveals a process gap. Do not add several vague warnings when one concrete check would prevent the mistake.

Which option should your team choose?

Choose the native skill environment where the required work and tools already fit. If everyone uses Claude and the workflow runs there, start with a Claude skill. If your work is in ChatGPT and its available capabilities suit the task, start there. Test sharing with the actual recipients before declaring it ready for the team.

Installation and distribution differ by product, surface, and workspace controls. Use each vendor’s current guidance rather than copying an installation recipe from another environment. Review executable scripts and outside connections before sharing them. A repeatable workflow can repeat an unwanted action just as consistently as a useful one.

Use a project feature when your main need is project background, and a skill when your main need is a repeatable method. The Projects comparison covers that separate choice. Avoid duplicating changing project facts in several skills just to make each copy look self-contained.

How to do this with fenbs

1. Create a free account, free for one person with no credit card, and add one project with a recurring task.
2. Save the reviewed procedure and one successful example in Skills and prompts. State the required inputs and acceptance criteria.
3. Record current decisions and rules with the project. Keep those changing facts separate from the reusable procedure.
4. Connect your supported assistants and ask each to read the context and saved prompt before a comparable task.
5. Review the outputs, record useful lessons, and save progress so the next session knows what remains.

fenbs does not install native ChatGPT or Claude skill packages, import chat histories, or make scripts compatible. A native skill alone may be enough when you stay in one app and only need a repeatable output. Shared work records help when methods, decisions, and progress must survive a change of assistant.

Keep the first workflow small. A method you can test and explain is easier to share than an ambitious package nobody can review. Use the AI prompts hub when you need a simpler starting point before packaging the procedure.