AI skills explained: How to build your own and save time on everyday tasks
Whether you use ChatGPT, Claude or Gemini, skills can help automate recurring work without requiring you to start from scratch every time. This guide covers creation, customization, cross-platform use and security.
If you use ChatGPT, Claude or Gemini regularly, chances are there are tasks you ask them to perform again and again: drafting emails in a particular style, summarizing meetings, analyzing documents or preparing reports. Each time, you find yourself explaining exactly what you want, what the result should look like, what to pay attention to and what to avoid. Sometimes the AI gets it right on the first try. Other times, it takes several rounds of corrections to reach the desired result.
This is where skills come in. They let you give an AI tool customized capabilities through packages of instructions, knowledge and resources that it can activate when needed. Instead of starting from scratch in every conversation, you can teach the tool a specific way of working and reuse it whenever the same task comes up.
Anthropic, the developer of Claude, introduced its Skills feature in October 2025, initially across its chatbot, Claude Code and developer platform. OpenAI and Google have since adopted the approach in their own products, although the features available and the ways users can create and deploy skills differ between platforms.
So how do you create a skill? Do you need to know how to code to use one? And how can skills save time and simplify everyday tasks? To answer these questions, we spoke with Alex Choroshin, a software engineer and owner of the software house Yootech. Choroshin founded and maintains Skills IL, a volunteer-run library of skills tailored to Israeli users. The library contains more than 200 skills covering tasks ranging from filling out National Insurance Institute forms and comparing insurance policies to handling income tax matters and assisting developers.
With his help, we put together a guide for both newcomers who want to create their first skill and more experienced users looking to refine their skills and discover new ways to use them.
So what is a skill, anyway?
A skill is a package of instructions and, sometimes, specialized knowledge, examples, files or code that teaches an AI model to perform a particular task in a defined way. Choroshin describes it as a “knowledge pill.” “You give it to the AI, and suddenly it becomes an expert in that field,” he says.
A common format for skills is a file called SKILL.md, which contains the instructions and metadata that tell the AI what the skill does and when to use it. Skills can also include supporting files and, in some cases, executable code. The format is designed to be portable across compatible tools, although not every platform supports every feature.
According to Choroshin, there are two broad types of skills: process skills and knowledge skills. A process skill teaches the model how to perform an action or sequence of actions, such as preparing a weekly summary from information supplied by the user. A knowledge skill provides specialized information that may not otherwise be available to the model, such as rules, tables or procedures in a particular field.
The distinction is not absolute. A single skill can combine knowledge and process. As Choroshin puts it, “The smarter the models become, the fewer knowledge skills they need, because they themselves hold more knowledge.”
Why use a skill instead of a regular prompt or a project?
Skills offer several advantages. First, they let you package a recurring task into a reusable set of instructions. Rather than writing a long prompt every time, you can use a few words to signal that the AI should apply the skill.
Second, a skill can define an entire workflow. It can specify not only the style of the response but also the order in which the model should perform actions, how it should check its work and what to do if something goes wrong. You can also combine multiple skills. One might analyze a document, for example, while another turns the findings into a presentation.
Finally, skills are designed to be portable. Unlike some projects and custom configurations that are tied to a particular service, the open skills format can make it easier to move instructions and supporting resources between compatible AI tools. Some adjustments may still be necessary when switching platforms.
When should you create a skill?
Not every task calls for a skill. A one-off question or a simple request may require nothing more than a regular prompt. Skills become useful when you repeatedly perform the same task or need to follow a complex process.
A simple way to decide whether you need one is to consider how much effort it takes to get the desired result. If a short prompt does the job, a skill may be unnecessary. If you repeatedly find yourself correcting the model, explaining the same preferences or guiding it through the same sequence of steps, packaging those instructions into a skill could save time.
Does it cost money?
The answer depends on the platform and the type of skill. Claude supports custom skills on paid plans, while Google is rolling out skills for personal Gemini accounts. In ChatGPT, standalone skills are available in the desktop app and developer-focused environments, while skills can also be packaged into plugins. Access and creation options vary by plan, workspace and product.
It is therefore worth checking the current requirements for the specific service you use. Even when creating or using a skill is included in your plan, advanced capabilities, API usage or connections to external services may involve additional costs.
So how do you create a skill?
One of the simplest ways is to ask the AI to help you create one. First, perform the task in a regular chat and refine the result until it matches what you want. Then ask the model to turn the process into a reusable skill, explaining which instructions, examples and preferences it should preserve.
Depending on the platform, the result may be a skill you can save directly or a package of files, often provided as a ZIP archive. You can then review the instructions, make changes and upload or install the skill using the relevant interface. Some platforms also let you start with a template or create a skill from scratch.
Once the skill is saved, test it in a new conversation. Check whether it activates when expected and whether the output meets your requirements. If it does not, refine the description and instructions, then test it again.
The exact steps differ by platform. In Gemini, for example, the Skills page is available through the Customize section for eligible users. In ChatGPT, skills and plugins have their own interfaces, and the available options depend on the product surface and account. Claude's custom skills are managed through its supported settings and developer tools.
What are the key rules for creating a successful skill?
First, Choroshin says, clearly define what the skill does and, just as importantly, when it should be used. The description should include the circumstances that call for the skill and the words or requests likely to trigger it.
For example, if you create a meeting-summary skill, specify that it should be used when a meeting transcript is attached or when someone asks for a meeting summary. A broad description such as “a skill for summarizing texts” may not be enough to make its intended use clear.
Second, avoid unnecessary instructions and explanations of things the model already knows. “It’s true that the models today are capable of holding a lot of information in the context window, but we still want the skill to be focused. If there is a word, sentence or section that isn’t needed, it’s better to remove it,” Choroshin explains.
There is no need to explain how to summarize a text in general, for example. It is more useful to specify what the summary should contain: the decisions made, the tasks assigned and the people responsible for carrying them out. You can also include examples of good meeting summaries to show the model the structure and style you want.
Another tip is to keep the information the skill relies on up to date. Choroshin recommends documenting sources and, where relevant, dates after which information should be reviewed. You can also instruct the model to return to the original source to check whether the information has changed.
Finally, do not build a skill that tries to do everything. A good skill should have a clear purpose. If a process involves several distinct tasks, it may be better to divide it into separate skills and then combine them into a larger workflow.
I already use skills. How can I take things to the next level?
You can build a skill that coordinates other skills, or package multiple skills and connected tools into a plugin or workflow. For example, one skill could analyze information, a second could turn the analysis into a report, and a third could draft an email based on the findings.
You can also connect AI tools to external services through integrations known as connectors or through the Model Context Protocol, or MCP. These connections can allow an AI tool to access information and perform supported actions in systems such as Gmail, Google Sheets and other business services.
That means a skill that prepares a weekly summary does not have to stop at displaying the results in a chat. With the right connections and permissions, it could gather data from several sources, process it, save the results in a spreadsheet or send them by email.
You can also automate recurring tasks. Depending on the platform and its available features, tools such as Claude Cowork, Claude Code, ChatGPT and Gemini can support scheduled or repeated workflows. Instead of asking the AI to analyze your expenses every time, for example, you might configure a process to run weekly or monthly, prepare an updated report and deliver it through a connected service.
Similar workflows could collect work-related information each morning, update tables or prepare periodic reports. The options depend on the tool, your subscription, the integrations available and the permissions you grant.
Can you move a skill between platforms, for example from Claude to ChatGPT or Gemini?
Yes, in many cases. The shared format is intended to make skills portable, but transferring one does not guarantee that it will work identically everywhere.
According to Choroshin, adjustments may be needed because platforms impose different limits on skill descriptions, support different types of files or offer different integrations. A connection to an external tool that works in one environment may not be available in another.
If you are building a skill for use across several platforms, try to avoid relying on features unique to one service. If the skill fails after being transferred, you can give the model the error message and ask it to identify which instructions or files need to be adapted. You should then test the revised version rather than assuming that the change has solved the problem.
I want to install a skill someone else created. How can I tell if it is safe?
A skill is not necessarily just a collection of harmless instructions. Depending on the platform and how it is built, it may also contain supporting files or code that an AI agent can execute. If the agent has access to files on your computer or to connected external services, a malicious skill could misuse that access to carry out actions you did not intend.
For example, a skill advertised as a tool for analyzing documents could contain a hidden instruction to send their contents to an external address, or code that changes or deletes files on your computer.
Choroshin recommends starting with skills from sources you trust, but says that is not enough on its own. Before installing a skill, open its SKILL.md instruction file and review any accompanying files. Look for instructions or access requests that have little to do with the task the skill claims to perform. A meeting-summary skill, for example, should not need access to your bank account, and a text-editing skill should not need to send your files to an unfamiliar server.
If you cannot understand why a skill requests a particular permission or what its accompanying code does, do not install it until you can verify that it is safe. Even a careful review cannot guarantee complete security, so limit permissions to the minimum necessary and be especially cautious when granting an AI agent access to sensitive documents, local files or external accounts.
