Skill, MCP, Workflow, Rules, and Memory: How They Divide Work in AI Coding Assistants

Opening: Config files handed you a test

When switching among mainstream AI coding assistants, I often have this experience: the goal is simple—just ask the AI to help implement a page—but the config file immediately presents a row of terms: Skill, MCP, Workflow, Rules, and Memory. They look like a set of certificates that must be understood before getting started, making it hard to know where to begin.

These concepts are not parallel items of the same kind. They approach the question “how does AI complete a task” from different dimensions. If you mix them together, it is natural to feel confused. Below, I use several real development tasks to explain how they divide work.

1. First, give the five concepts clear definitions

To distinguish them, we cannot only look at their names. The key is to identify what kind of problem each concept is meant to solve.

• Rules: Within an applicable scope, which conventions must always be followed?
• Skill: When facing a certain type of task, what method should be used?
• MCP: How do you connect to external tools and data through a unified protocol?
• Workflow: What should happen first, what should happen later, and when should execution pause for confirmation?
• Memory: Which old information is worth preserving so that it can be retrieved again in the future?

A simple comparison:

Concept	Core problem it solves	Typical example in development
Rules	Conventions that must always be followed within a scope	Do not commit code without authorization; API errors must not be disguised as success
Skill	Methodology for handling a type of task	Processing design mockup images, troubleshooting page issues, organizing delivery reports
MCP	Connecting to external tools and data through a unified protocol	Reading browser state, design files, and document services
Workflow	Execution order and pause conditions	Show sample frames first, and render the full piece only after confirmation
Memory	Information that needs to be retained and retrieved later	User preferences, confirmed decisions, special constraints in a certain environment

2. Why they are easy to confuse: names overlap

Naming across products is not unified, and it can even overlap. A Skill may contain a Workflow internally. A client may also place rule files under a “Memory” menu. If you only recognize the menu name, it is easy to treat things with different responsibilities as the same category.

A more reliable judgment standard is not what something is called, but three points:

• Loading timing: Is it always present from the start of a task, or is it triggered only when a specific scenario appears?
• Maintenance method: Is it a fixed piece of text, an executable script, or writable storage that can be updated dynamically?
• Execution behavior: Does it constrain behavior boundaries, operational steps, or information access?

With these three questions, it becomes much easier to identify what a feature actually does.

Also, two terms are worth clarifying here: In this article, an Agent refers to an AI assistant that can read and write files and call tools. The host refers to the client that runs this Agent.

3. A real task: from design mockup to page assets

Take “implementing a page based on a design mockup” as an example. You can clearly see how the five concepts cooperate:

• MCP connects to the design mockup and reads external design data.
• Skill defines how images should be handled, such as compression, naming, and placement.
• Workflow arranges the order of “confirmation” and “execution.”
• Rules restrict which files cannot be modified and ensure that keys do not appear in code or logs.
• Memory preserves conventions that may be needed again, such as asset specifications for this project.

In other words, they are not replacements for each other. Each manages one part, and only when combined do they form a complete process.

4. Making it more concrete: the asset processing pain point

When developing a mini program, I have an unavoidable task: export assets from a design tool, compress PNG files, upload them to an object storage service, obtain access links, and then put those links back into the code. The page itself has not even been written yet, but I am already acting as a “delivery worker” for images.

What makes it more troublesome is that one page often has multiple images, so this sequence has to be repeated many times. I therefore turned it into an “automated asset processing skill.” It includes two scripts: one is responsible for compressing PNG files, and the other is responsible for uploading them to object storage. I then connected this asset processing stage to the page development workflow.

In this example, the responsibilities are divided as follows:

• Use MCP to read the design mockup and bring in image information.
• Skill explains how images should be compressed, where they should be uploaded, and how the links should be returned.
• “Confirm the asset list first, then upload in batches” belongs to the Workflow of this task.
• Keys must never appear in code or logs, which is suitable for long-term Rules.

The scripts themselves call the compression service and the object storage SDK. The Agent determines which steps are needed for the current task, and then runs them through the tools provided by the host.

5. When to upgrade from direct use to a reusable capability

A practical rule: if an existing script can complete the task and the Agent can run commands, just use it directly. There is no need to rush into packaging it.

It becomes worthwhile to turn it into a Skill or Workflow when the same type of operation appears repeatedly, the steps are fixed, and collaborators also need to reuse it.

Abstracting too early brings maintenance costs. Abstracting too late allows repetitive work to continue consuming time. The key is whether this logic is stable, whether it will be triggered repeatedly, and whether it needs to be shared across tasks.

6. Summary: classify by responsibility, not by name

Returning to that initial “test”: these concepts are confusing because we are used to remembering them by name, while names keep drifting across products. A better approach is to build a mental model based on responsibility:

• Use Rules when you need long-term constraints on behavior boundaries.
• Use Skill when you need to preserve the methodology for a type of task.
• Use MCP when you need to connect to external data and tools.
• Use Workflow when you need to orchestrate steps and pause conditions.
• Use Memory when you need to preserve information and retrieve it later.

Once you classify by these five dimensions, even more configurations will not feel messy. The next time you see a row of unfamiliar terms, ask yourself first: what exactly is this thing managing? The answer will often become clear.