Introduction

I’m Mia Sato, an AI Research Lead.

In this article, I will walk through ChatGPT Skills as introduced by OpenAI. Skills are a way to package reusable workflows for specific tasks so that ChatGPT can automatically use them when needed. They are easier to understand if you think of them not simply as a “collection of useful prompts,” but as a way to reduce repeated explanations of procedures and assumptions. This article looks at the basics of Skills, how they differ from GPTs, and where they may be useful in actual business operations.

What you’ll learn in this article

• The basics of ChatGPT Skills
• The difference between GPTs and Skills
• Where to start using Skills in e-commerce operations
• Common ways Skills implementations can go wrong

This article is based on public information from the OpenAI Help Center and OpenAI Academy. Rather than summarizing one specific article, it reorganizes publicly available information from a practical business perspective.

Why GDX

In the course of supporting e-commerce operations, GDX often sees similar issues arise on the client side. For example, teams may have to re-check required fields and wording rules every time they register a product. Banner replacements, price updates, and inventory updates may each live in different places, which slows down confirmation. Reporting and customer inquiry handling may also begin with the same repeated explanations each time, making rework more likely.

In many cases, it is not that the data or procedures do not exist. The problem is that the necessary information is scattered, so the same checks and explanations have to be repeated over and over again. Skills become easier to understand when viewed as a way to reduce this repeated “explanation cost” and make it easier to translate existing work into practical workflows.

What has changed with ChatGPT Skills?

What exactly was introduced?

OpenAI describes Skills as reusable, shareable workflows. A Skill can include not only instructions, but also examples, reference materials, and code. Once created and installed, ChatGPT may automatically use one or more Skills when needed.

In practical terms, this makes it easier to avoid re-explaining the same process every time. For example, instead of repeatedly telling someone, “Look at the numbers in this order when creating an ad report,” “Do not use these expressions when writing product descriptions,” or “Classify the inquiry first, then draft a reply,” the workflow itself can be packaged and reused.

This is a meaningful shift from a setup where only people who are especially good at prompting can use AI effectively. The major change is that the workflow itself becomes easier to share across a team.

The difference between GPTs and Skills

This is a point that is often confused. GPTs are custom versions of ChatGPT created for a specific purpose. According to OpenAI, GPTs combine instructions, knowledge, and capabilities to tailor ChatGPT for a given task or theme.

Skills, by contrast, are not about creating a separate version of ChatGPT itself. They are more like reusable components for specific workflows.

The difference between GPTs and Skills

Role
GPTs: Create a dedicated version of ChatGPT for a specific purpose
Skills: Make specific workflows reusable

What they define
GPTs: A combination of behavior, knowledge, and available capabilities
Skills: Steps, decision criteria, and output format

Best use case
GPTs: When you want a dedicated assistant for a certain theme or purpose
Skills: When you want to standardize work that follows the same flow each time

Simple way to think about it
GPTs: Define “who it works as”
Skills: Define “how it works”

Seen this way, GPTs are custom versions of ChatGPT, while Skills are reusable workflow units that can be applied within or alongside those setups.

Availability and ways to create them

Availability

As of March 2026, Skills are available in beta for ChatGPT Business, Enterprise, Edu, Teachers, and Healthcare. Skills are also supported in Codex and the API. However, they are not automatically synced across products. For example, a Skill created in ChatGPT does not automatically appear in another product.

How to create them

According to OpenAI, there are several main ways to create Skills.

• Create them through conversation
• Create them in the Skills editor
• Install a shared Skill
• Upload one from a local file

In other words, Skills are not just a personal setting. They are meant to be created, shared, and reused across a team.

Points to keep in mind

An important point is that Skills are still a beta feature. In particular, for Enterprise and Edu, they are off by default during the beta period and must be enabled by an administrator. Sharing and installation can also be controlled by admins.

That means Skills are not necessarily something to roll out immediately across every team and every workflow. They are more realistic to start with in work that has the following characteristics:

• The procedure is already defined
• The work follows a similar flow each time
• You want to reduce variability in outputs

Three e-commerce situations where Skills are especially useful

ChatGPT Skills are particularly well suited to work in e-commerce operations that follows almost the same review flow each time. Below are three practical examples where they are easy to imagine using.

1. Product registration, image registration, and price updates

When to use it:
When you want to reduce missed checks before a product page goes live

This is one of the easiest types of work to turn into a Skill. Product registration usually involves repeated checks for product name, product description, images, price, SKU, and identification codes. At the same time, different team members often check these items in different orders or use different standards for deciding when to stop publication.

What to include in the Skill

Required checks at the time of product registration

• Product name
• Product description
• Whether images are available
• Price
• Foreign currency price
• SKU or identification code
• Prohibited expressions

Image-related checks

• Size
• Whether it is an old image or a new image
• Whether it is the target for replacement

Pre-publication checklist items

How it would be used

When a new or updated product comes in, the person in charge asks:
“Review this product information and turn it into a pre-registration checklist.”

Expected output

• A list of required registration items
• Missing information
• The person or team that needs to confirm missing items
• Items that should block publication
• A registration-ready formatted output if everything is complete

Why this kind of work is a good fit

Product registration is a type of work where processes easily drift depending on the individual in charge. If a Skill fixes both the order of checks and the criteria for stopping publication, it becomes easier to reduce missed checks and inconsistent decisions.

2. Initial sorting of return requests and refund-related inquiries

When to use it:
When you want to speed up handoffs between customer support and operations

This is another strong match for Skills. Rather than trying to automate customer support end to end, it is more realistic to first stabilize the classification of inquiries and the creation of first-draft replies.

What to include in the Skill

Inquiry classification

• Return request
• Exchange request
• Refund status inquiry
• Shipping-related issue

Responsibility routing

• Handled by customer support
• Sent for refund processing
• Requires system confirmation

Reply draft templates
Escalation conditions for exceptions

How it would be used

You paste in the inquiry text and ask:
“Classify this according to the returns and refunds workflow, and create a first reply draft.”

Expected output

• Inquiry category
• Responsible team
• Information that needs to be confirmed
• Draft reply for the customer
• Special notes

Why this kind of work is a good fit

In inquiry handling, stable initial classification and routing matter a great deal. Even just standardizing the first sort and the initial draft can reduce rework significantly.

3. Regular KPI summaries for replenishment and out-of-stock performance

When to use it:
When you want to standardize how inventory performance is reviewed before meetings

This kind of recurring reporting is highly compatible with Skills. If the KPIs you review and the style of comments you write are mostly consistent each time, a Skill makes it easier to align the structure of the report in advance.

What to include in the Skill

KPIs reviewed each time

• Out-of-stock rate
• Missed replenishment
• Excess replenishment
• Sales impact

Report heading order
How to interpret the numbers
How to write commentary

How it would be used

You input the weekly report figures and ask:
“Summarize this in the format used for replenishment review.”

Expected output

• This week’s anomalies
• High-impact SKUs or stores
• Whether the issue is under-replenishment or over-replenishment
• Key points to watch next week
• A short meeting-ready summary

Why this kind of work is a good fit

If the way people read the numbers is mostly the same every time, using a Skill can make meeting preparation much faster.

If you were to try just one Skill first, what should it be?

There is no need to start broadly. One of the following is enough for an initial trial:

• Inventory discrepancy review flow
• Pre-publication product registration check
• Initial sorting of returns and refund inquiries
• Weekly summary of replenishment KPIs

Once you choose one, define just these four things:

Input
What information needs to be provided for the Skill to work

Output
What format the result should be returned in

Restrictions
What judgments or expressions should not be produced

Human review conditions
In what cases a person should review the output

Examples of wording you can use as-is

The examples below are instruction text you can enter when creating a Skill. They are not internal explanatory text. They are instructions for how you want the Skill to behave.

There are mainly two places to enter them:

• If creating a Skill in a conversation: enter the text directly into the ChatGPT chat box
• If creating a Skill in the Skills editor: enter it in the Instructions field, not in the name or description field

In other words, these are not titles or summaries. They are the actual instruction body that defines how the Skill should work.

Example 1: Product registration check

“As a Skill for product registration checks, review the input product information and list missing required items, image checks, price checks, and pre-publication cautions. If any required items are missing, return the result on the assumption that the product should not be published.”

Example 2: Initial sorting for returns and refunds

“As a Skill for return and refund handling, classify the inquiry content, determine whether it should be handled by customer support or sent for refund processing, and create a first draft reply for the customer. If compensation judgment is required, route it for human review.”

Example 3: Inventory discrepancy review

“As a Skill for inventory discrepancy review, organize which system should be checked first among Shopify, OMS, and SCS, and list possible causes of discrepancies along with the recommended order of investigation. If there is a risk of overselling, display it as the highest priority.”

Three common ways Skills implementations fail

1. Turning an undefined workflow directly into a Skill

Skills are designed to make workflows reusable. That also means if the workflow itself is not yet settled in the real world, turning it into a Skill will simply carry over the inconsistency.

For example, if product registration checks vary by person, or if exception-handling standards in inquiry management are still vague, building a Skill around that situation can produce outputs that look polished but actually increase review burden.

The first thing to define is not the whole automation. It is the order of checks and the criteria for when the process should stop.

2. Defining only the output format, but not the input conditions or stop conditions

A common mistake is to decide only on the output, such as “I want a checklist” or “I want a summary.” In reality, a Skill will not be stable unless you also define what inputs are required and what kinds of missing information should cause it to stop.

For example, in a product registration check, you need to decide whether product name, description, price, images, and SKU are the minimum required inputs, and exactly which missing items should block publication. In initial sorting for returns and refunds, you also need a stop condition such as “cases requiring compensation judgment must be routed to a person.”

Skills can look like they will return something reasonable no matter what you give them. In practice, they become more reliable when you first decide the conditions under which they should not proceed.

3. Using Skills for draft support, then letting them make the final judgment

Skills are well suited to drafting, summarizing, classifying, and organizing review points. But if you let them handle the final go/no-go decision or exception processing all at once, they are much more likely to create operational risk.

In e-commerce operations especially, there are still areas where a person should remain responsible: publication approval, refund approval, prioritization during stock shortages, or compensation decisions. If those boundaries are left unclear, the workflow can drift into “we moved forward because the AI said so.”

It is safer to begin by limiting Skills to initial sorting and draft creation, while keeping human final review built into the process.

The GDX perspective

Skills are easier to use in practice when they are seen not as a flashy way to “have AI do everything,” but as a way to reduce work that involves the same explanations over and over again.

In the context of GDX’s e-commerce support, the best candidates are tasks such as product registration, first-pass customer support sorting, and weekly report summarization, where the inputs and review points are already somewhat consistent. On the other hand, if judgment criteria are still unclear or exception handling is frequent, it is usually better to organize the workflow itself before turning it into a Skill.

Conclusion

ChatGPT Skills are easier to apply in practice when viewed not as a dramatic feature for handing everything over to AI, but as a mechanism for reducing repeated explanations, repeated formatting, and repeated sharing of assumptions. In environments like e-commerce operations, where recurring reports, product registration, first-pass customer support handling, and pre-meeting summaries are common, the benefits are especially visible.

There is no need to roll them out in a large way from the start. The best first step is to choose one task that involves the same request multiple times every week. Then align the input, output, restrictions, and review criteria. Skills make it easier to turn that kind of quiet operational cleanup into a shared team asset. In the end, the biggest benefit may not be AI accuracy itself, but the reduction of human rework.

Bibliography

OpenAI Help Center, “Skills in ChatGPT”
Source used for the overview of Skills, supported plans, creation methods, sharing methods, admin settings for Enterprise and Edu, data residency, and audit log references.
https://help.openai.com/en/articles/20001066-skills-in-chatgpt

OpenAI Help Center, “GPTs in ChatGPT”
Source used for the definition of GPTs, explanations of instructions, knowledge, and capabilities, and the basis for comparing GPTs and Skills.
https://help.openai.com/en/articles/8554407-create-a-custom-gpt

OpenAI Help Center, “ChatGPT Business FAQ” and “What is ChatGPT Business?”
Sources used for ChatGPT Business eligibility, the requirement of two or more users, pricing, and treatment of workspace data.
https://help.openai.com/en/articles/8542115-chatgpt-business-faq
https://help.openai.com/en/articles/8792828-what-is-chatgpt-business

OpenAI Academy, “Skills”
Source used for the explanation of the difference between Skills and custom GPTs, and for the framing of Skills as repeatable workflows.
https://academy.openai.com/public/clubs/work-users-ynjqu/resources/skills

OpenAI Academy, “Building custom GPTs to scale AI adoption”
Source used for the idea of starting small with repeatable work and standardizing it gradually in real operations.
https://academy.openai.com/public/clubs/champions-ecqup/resources/building-custom-gpts-to-scale-ai-adoption

Parts of this article were created with support from ChatGPT and then edited and revised by the author. The content reflects the author’s personal views and does not represent the official position or statement of GDX Co., Ltd. This information is provided for reference purposes only. Please consult official announcements and primary sources.