Skill, MCP, Workflow, Rules, and Memory: A Practical Guide to the Five Core Concepts in AI-Assisted Development

Why These Concepts Are So Confusing

When using AI-assisted programming, many developers have had a similar experience: you only want the tool to help build a page, but the configuration interface first presents a row of terms—Skill, MCP, Workflow, Rules, and Memory. Each one seems to require understanding and configuration, as if you need to pass a certification exam before you can get anything done.

The root of the confusion is that all five concepts answer the question “How can AI complete tasks better?” But they answer that question at different layers. If you treat them as similar configuration items and fill them in casually, you end up with a messy setup: information that should be remembered is not remembered, constraints that should be enforced are not enforced, and steps that should be split up run all at once.

A useful analogy is to think of these concepts as roles and systems within a development team:

• Rules are the team’s regulations: they must never be violated.
• Skill is a team member’s professional expertise: when a certain type of task arrives, the standard approach is known.
• MCP is the connection channel between office systems and external systems: when data is needed, it can be retrieved.
• Workflow is the project execution process: what comes first, what comes next, and where the checkpoints are.
• Memory is the project archive: decisions made in the past can be reused next time.

Breaking Down the Five Concepts

1. Rules: Always-On Constraints

Rules answer the question: Which agreements must always be followed within the applicable scope?

Rules do not care what task you are currently working on. As long as they are in effect—for example, within a code repository—they continuously constrain the AI’s behavior. Typical Rules include:

• Do not commit code without authorization.
• When an API call fails, do not disguise the failure as a successful response.
• Do not read or modify sensitive configuration files such as keys and environment variables.
• Follow the repository’s established code style and directory structure.

The key characteristics of Rules are unconditionality and persistence. They are not “follow this for the current task”; they mean “follow this as long as work is being done in this project.” Good Rules should be concise and verifiable, and should avoid vague slogans.

2. Skill: Methodology for a Type of Task

Skill answers the question: When a certain type of task appears, what method should be used?

When the AI identifies that the current task falls within the scope of a skill, it loads the corresponding method description. Examples include:

• A skill for handling design mockup images: how to slice images, how to identify spacing and font sizes, and how to handle multiple-resolution assets.
• A skill for troubleshooting page issues: first check console errors, then inspect network requests, and finally verify component props.
• A skill for preparing delivery reports: always include three parts—a change list, self-test conclusions, and remaining risks.

The difference between Skill and Rules is this: Skill is the “how to do it” method, activated only for specific task types; Rules are the “what must not be done” baseline, always in effect.

3. MCP: A Unified Protocol for Connecting External Tools

MCP answers the question: How can external tools and data be connected through a unified protocol?

An AI model itself runs in a relatively closed environment. It cannot see your browser, read your design platform, or access your internal documentation service. MCP provides a standardized integration layer that allows the AI to call these external capabilities in a consistent way:

• Read page state and error information from the browser.
• Pull annotations, sliced assets, and style data from the design platform.
• Query API definitions from the internal documentation service.

The value of MCP lies in protocol uniformity: tool providers expose capabilities according to the specification, and the AI side calls them according to the specification, so there is no need to write a private integration for every tool. It answers “Can the data be obtained?” but does not handle “How should the data be used after it is obtained?”—that is the job of Skill.

4. Workflow: Orchestration of Task Execution

Workflow answers the question: What comes first, what comes next, and under what conditions should execution pause?

Many development tasks cannot be completed in a single step and require human checkpoints. Consider “rendering a video clip based on a design mockup.” A reasonable process would be:

1. Render one or two sample frames first.
2. Submit them to the developer to confirm the style and details.
3. Only after explicit confirmation, render the full clip.

Without a Workflow, the AI might render the entire clip directly. If the style is wrong, everything has to be redone, wasting significant time and compute. Workflow explicitly writes “where humans intervene and confirm” into the execution path. In essence, it adds controllable checkpoints to automation.

The difference from Skill is: Skill tells you how to do a certain step well, while Workflow determines the order in which those steps are connected and where execution stops to wait for a person.

5. Memory: Information Retention Across Sessions

Memory answers the question: Which old information is worth saving for later retrieval?

Much information during development appears only in a single conversation, but its value can extend to later tasks:

• User preferences: naming style, comment density, preferred technology choices.
• Confirmed decisions: an API approach or component breakdown agreed upon after a discussion.
• Special environment constraints: a test environment accessible only from a specific network segment, or a dependency that must use a specified version.

The difficulty of Memory is not “storing” but “what to store and when to retrieve it.” What is worth storing is stable agreements and conclusions, not one-off process data. Retrieval should be relevant to the current task; otherwise, it only pollutes the context.

How the Five Work Together in a Complete Task

Take “implementing a page based on a design mockup” as an example. The complete collaboration chain is as follows:

1. MCP connects to the design platform and retrieves the design image and annotation data—solving “Where does the data come from?”
2. Skill defines how to process the design mockup: identify the layout structure, extract colors and font sizes, and handle sliced assets—solving “How should this type of work be done?”
3. Workflow arranges the execution order: first output a page skeleton for the developer to confirm, and after confirmation, complete the interactions and details—solving “What are the steps and checkpoints?”
4. Rules provide continuous guardrails: do not commit code without authorization; if the implementation clearly differs from the design mockup, explain it rather than gloss over it—solving “What must not be touched?”
5. Memory leaves a record after the task ends: the design fidelity standards confirmed this time and the component library version limitations discovered can be reused directly next time a similar page is built—solving “How does experience accumulate?”

As this shows, the five are not mutually exclusive options. They form a layered collaboration system: Memory retains the past, Rules constrain the baseline, MCP connects data, Skill provides methods, and Workflow orchestrates the process.

Three Practical Configuration Recommendations

1. Write Rules first, then discuss the rest. Baseline constraints offer the highest return on configuration effort. A few clear rules can prevent most accidents.
2. Build Skills by task type, and write Workflows by project characteristics. Put general methodologies in Skills. Only processes that involve multi-person confirmation and multi-stage execution are worth explicitly orchestrating. Do not force simple tasks into a workflow.
3. Clean up Memory regularly. Outdated preferences and invalid environment information can mislead later tasks. Treat it as a project archive that needs maintenance, not an endlessly appended log.

Once you understand the core question each concept answers, configuration no longer feels like last-minute exam preparation. It feels more like building a well-structured development team with clear responsibilities and sound governance.