Tired of Repeating Yourself to Claude? When to Use Prompts vs. Skills

Imagine we use Claude to help with our work every day. Today, we ask it to review code, and we have to tell it to check for security, check error handling, and summarize only the key points. Tomorrow, there's a new Pull Request, and we type something similar again. Next week, we have to write documentation, and we have to explain again what structure the team's documents use, how to write them, and what shouldn't be included. As we continue using AI, many people will likely start facing the same question: Do we really have to explain the same working methods to the AI every single time?

If it's a short command, a Prompt seems to do the job well enough. But if it starts becoming a reusable Workflow with specific steps or team rules that must be applied the same way every time, this is where concepts like Agent Skills, or what many people call Claude Skills, start to become interesting.

What are Claude Skills?

The official name is Agent Skills. It is a format developed by Anthropic to allow AI Agents to access specific knowledge, Instructions, and Workflows prepared for certain types of tasks. Instead of putting everything into a Prompt every single time, we can gather reusable working methods into a Skill, and then have the Agent load the relevant Skill when it encounters that type of task.

Anthropic describes Agent Skills as folders that can combine Instructions, Scripts, and Resources, which the Agent can discover and load to use according to the task at hand. In December 2025, Anthropic released Agent Skills as an Open Standard so that this format can be applied to any supported Agent system, not limiting the concept to Claude alone.

Simply put, if a Prompt is "Please do it this way for this task," a Skill is closer to "If you encounter this type of task, this is how we normally want it done." The two might look similar, but they are actually suited for different situations.

Why not just save good Prompts to copy and use every time?

Actually, you can, and in many cases, it might be the easiest method. If we use Claude to review a function just once and want it to focus on SQL Injection and Error Handling, writing a clear Prompt is sufficient. Or if we have a short Prompt Template that we copy and use once a month, creating a Skill might just add unnecessary complexity.

However, the situation starts to change when our Prompt is no longer just 2-3 lines long. Suppose a team's Code Review requires checking the company's Coding Convention, reading additional Documentation, checking multiple items on a Checklist, and summarizing the results in the same Format every time. At that point, what we are saving might not just be a "Prompt" anymore; it is starting to become the team's working method.

This is what Agent Skills attempt to solve, paving the way for us to gather Procedural Knowledge, or the knowledge of "how this task must be done," into a reusable Resource. Anthropic compares creating a Skill to preparing an Onboarding Guide for a new hire, because instead of explaining the Process from the beginning every single time, we prepare what is necessary in advance.

Will Skills replace Prompts?

This is a point that is easily misunderstood, because Skills do not render Prompts unnecessary. In actual use, we still need to tell the AI what we want it to do right now, such as "Please review this Pull Request." This Prompt is still the command for the current task, while the Skill might be the background information telling it what to look for during a Code Review, which standards to use, or how it should reply.

Therefore, Prompts and Skills are not exactly competitors. Prompts are suited for stating the Intent of the current task, while Skills are suited for storing reusable working methods. Some tasks only need a Prompt, some tasks are more convenient with a Skill, and many tasks use both together.

The interesting point for Devs might not be about Prompts?

Looking at it from another angle, the interesting aspect of Skills might not be the question of how many lines of Prompt typing we save, but rather what kind of Workflow within the team can be made clear enough for an Agent to utilize. Think about the routine tasks developers do, such as: what does this team's Code Review need to look at? When encountering a Bug, what should be checked first? How should the company's Documentation be written? What needs to be checked before a Release? What kind of Migration requires extra Review? Or what kind of Pull Request requires additional Tests?

Many times, these matters do not exist solely within the Code, but in Documentation, Checklists, or sometimes in the team members' experience. Agent Skills pave the way to organize this type of knowledge into a format that the Agent can use to assist in its work. The standard supports Instructions, Reference Materials, Assets, and Scripts as necessary.

Therefore, for Devs, the interesting question might shift from "How do we write a Prompt to make the AI smarter?" to "What working methods do we have that are clear enough for the AI to reuse?"

Does Claude need to know all Skills all the time?

No, and this is one of the key concepts of Agent Skills. The system is designed to use an approach called Progressive Disclosure. This means the Agent doesn't need to load all the details of every Skill right from the start. Instead, it can first see basic information about what each Skill does, and then when it encounters a relevant task, it loads the Instructions of that Skill and accesses additional Resources when necessary.

This is important because if an organization has dozens of Skills, throwing all the manuals into the Context every time wouldn't be an efficient method. The concept is similar to the Agent first knowing "what manuals are available" and then opening the manual relevant to the task at hand.

Are Skills Claude's Memory?

They are not the same. The phrase "using Skills" might make it feel like Claude has learned and will permanently remember our behavior. But Agent Skills do not mean Training a new model, and it doesn't mean the model changes its weights or learns the Skill into itself.

A Skill is a Resource that the Agent can discover and load to use during work. According to the Agent Skills Specification, the core of a Skill is SKILL.md, which contains Metadata and Instructions, and it can include other accompanying files as needed. Therefore, it's better to view Skills as "manuals the Agent can open and use" rather than "Claude remembering everything we've ever told it."

If we already have MCP, do we still need to care about Skills?

Both of these relate to the world of AI Agents, but they solve different problems. MCP, or Model Context Protocol, is an Open Protocol for connecting AI Applications with external data and tools, such as APIs, Databases, or various Services. The MCP Server can expose Tools for the model to discover and call upon.

Skills, on the other hand, focus on gathering knowledge and Workflows on how the Agent should perform that task. Suppose the AI needs to help manage a Bug Report; MCP might allow the AI to read the Issue or call a Tool from an external system. The Skill, however, might dictate what data to check next after reading the Issue, how to categorize the Severity, or how to summarize it back to the team.

Therefore, in actual work, MCP and Skills can be used together. There is no need to choose one or the other.

Should we turn every Prompt into a Skill?

Not necessarily. If it's a one-off task, a short command, or something that constantly changes, a Prompt is still the most straightforward approach. Skills become interesting when we find ourselves telling the AI the same things repeatedly, or when we have a relatively fixed Workflow that we want to save for future use.

For instance, every time you write Documentation, you have to explain the same 10 rules. Every time you Review Code, you have to attach the same Checklist. Or the Agent has to reference the exact same set of manuals every time it encounters certain types of tasks. These are the situations that might make us ask ourselves: should the thing we are copying every day remain a Prompt, or is it time to organize it into a Skill?

Skills Don't Guarantee the AI is Always Right

Another matter that shouldn't be overlooked is that having a Skill doesn't mean the Agent will perform correctly 100% of the time. Skills help make the Instructions and Context of the task clearer, but ultimately, the model still has to interpret and execute based on the information it receives.

Therefore, high-impact Workflows, such as Deployments, changing Infrastructure, modifying Production Databases, or managing sensitive data, still should have appropriate Permissions, Validations, and Human Reviews. This isn't an issue exclusive to Skills, either; Agent systems capable of calling Tools or taking Actions on external systems should also have access controls and give users the opportunity to verify their operations.

Skills from Others Shouldn't Necessarily Be Used Immediately?

Because an Agent Skill can contain Executable Scripts according to the Specification, bringing in a Skill from an unknown source should be treated with the same caution as using third-party Code or Packages.

Before using it in an actual Environment, you should know what Instructions the Skill has, what Scripts it calls, what kind of Permissions or Network Access it requires, and what data it can access. Skills help make the Agent work in a more specialized way, but the more Actions an Agent can take, the more critical Permissions and Security become.

How interesting is this for Developers?

If we still only use AI to ask about Errors occasionally or to Generate short Code snippets, Skills might not change our Workflow very much. But if we start using Agents to help with multi-step tasks, using Claude Code or Agentic Coding tools as part of our daily routine, what we need to think about next might not just be choosing which Model is smarter. Rather, it is about how we will pass on the Context, Process, and the team's working methods to the Agent.

Agent Skills are one of the approaches attempting to answer this question. What is interesting is that Anthropic has already released this format as an Open Standard, meaning Skills don't need to be viewed solely as a Claude Feature, but can also serve as a format for storing Workflows supported by other Agent systems.

It might be too early to say whether Skills will become the standard method every team uses. But for Devs who start finding their Prompts getting longer and longer, and having to explain the same Process to the Agent repeatedly, it at least raises an interesting question: Perhaps the problem isn't that we aren't writing good enough Prompts, but rather that what we are writing shouldn't have just been a Prompt in the first place?

FAQ: Frequently Asked Questions about Claude Skills

Are Claude Skills and Agent Skills the same thing?

In this context, yes. What many people call Claude Skills are the Agent Skills developed by Anthropic. However, the official name of the format is Agent Skills, and it has currently been published as an Open Standard that other Agent systems can adopt and support as well.

If I am already using Prompts, do I need to switch to using Skills?

Not necessarily. Prompts are still highly suitable for immediate commands, one-off tasks, or tasks that constantly change. Skills will start becoming interesting when it involves a Workflow or Instructions that need to be reused frequently.

Do Skills make Claude remember our working methods permanently?

Not in the sense of Training or altering the model itself. A Skill is a set of Instructions and Resources that the Agent loads to use when relevant to the task. The Skill is therefore much closer to a callable manual or Workflow than a Memory learned into the model.

Can Skills and MCP be used together?

Yes, and they serve different functions. MCP helps AI Applications connect with external Contexts and Tools, while a Skill can store Instructions and Workflows on how to manage the task. There are many cases where both concepts can complement each other.

Conclusion: Should we use Prompts or Skills?

Ultimately, there is no definitive answer that every Dev should stop writing Prompts and switch to using Skills. If the task changes every time, a Prompt might be easier. But if you start finding yourself explaining the same Process, the same rules, or the same Workflow to the AI over and over again, Skills are an interesting alternative because they help turn "what we repeatedly type" into "a working method the Agent can reuse."

And it might be another sign that the way Developers work with AI is shifting from just "writing great Prompts" to designing Contexts and Workflows so Agents can work with us better. If it were you, do you currently have any Prompts or working methods that you have to copy repeatedly to the point where you think, "Should this be turned into a Skill by now?"

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