AI Coding Agents: Rules, Commands, Skills, MCP and Hooks Explained : AntonioCortes.com

If you’re using tools like Claude Code, GitHub Copilot Workspace, or similar, you’ve probably noticed there’s technical jargon that goes beyond simply “chatting with AI”. I’m talking about terms like rules, commands, skills, MCP, and hooks.

These concepts are the architecture that makes AI agents truly useful for software development. They’re not just fancy marketing words — each one serves a specific function in how the agent works.

Let’s break them down one by one in a clear way.

First things first: What is an AI Coding Agent?

An AI coding agent is a system that doesn’t just answer questions, but executes actions in your codebase. It can read files, modify code, run commands, and make decisions based on context.

The key difference from a normal chatbot: an agent has tools and can operate autonomously within certain limits.

Now, let’s dive into the concepts that make these agents work.

Rules: Permanent Instructions

Rules are instructions that you configure once and the agent respects always, in every interaction. Think of them as the agent’s global configuration.

What they’re for

Rules define:

What the agent should do
What it should NOT do (limitations)
How to behave in specific situations
Preferred output formats
Security precautions

Real example

Imagine you’re working on a Laravel project and you have a rule that says:

Always use Laravel factories to generate test data, never use hardcoded data or direct SQL inserts.

This means that every time the agent needs to create tests or sample data, it will automatically use factories without you having to remind it.

When to use rules

Use rules when:

✅ You have restrictions that apply always
✅ You want consistency across all responses
✅ You need guard rails to prevent unwanted behavior
✅ You have format preferences that must always be respected

Where to configure them

Depending on the tool, rules can be in:

Configuration files (eg: CLAUDE.md for Claude Code)
Application settings
Specific project files

Commands: Shortcuts for Recurrent Tasks

Commands are predefined shortcuts that execute a specific task. They’re like macros: a command that triggers a complex sequence of actions.

How they work

When you invoke a command, the agent:

Expands the command into its full prompt
Executes the associated task
Returns the result to you

Real example

If you have a /commit command, the agent could:

Read git status
Analyze changes
Generate a descriptive commit message
Execute git commit with that message

All this with a single command: /commit.

Types of commands

There are two main categories:

1. Built-in commands (come with the tool)

/help - Shows help
/clear - Clears context
/commit - Creates commits (in some tools)

2. Custom commands (you define them)

/review - Reviews current code
/test - Generates tests for what you’re editing
/deploy - Runs your deployment pipeline

Advantages of using commands

Speed: You don’t have to write the same prompt over and over
Consistency: It’s always done the same way
Documentation: Commands serve as documentation of your workflow
Hidden complexity: You can chain many actions in a simple command

When to create commands

Create commands for:

✅ Tasks you repeat frequently
✅ Multi-step processes that are always done the same way
✅ Complex workflows you want to simplify

Skills: Specialized Capabilities

Skills are packages of knowledge and capabilities that an agent can load on demand. Think of them as plugins or extensions: the agent has them available, but only uses them when you need them.

Difference vs commands

Commands: Execute a specific task
Skills: Add new capabilities to the agent

A skill can include multiple commands, rules, and specialized logic.

Real example

A “code-review” skill could include:

Knowledge about clean code patterns
Rules for what to check first
Commands for security analysis
Logic to prioritize critical issues
Specific report format

Common skills in current tools

Git management: Commits, branches, PRs
Testing: Test generation, execution, coverage
Documentation: Generating docs, updating READMEs
Security: Vulnerability scanning
Refactoring: Improving existing code
Database: Migrations, queries, optimization

Why skills matter

Skills allow:

Specialization: The agent knows about specific domains
Composability: Combine skills for complex tasks
Updates: You can improve skills independently
Sharing: You can distribute skills to your team

When to use skills

Use skills when:

✅ A domain requires specialized knowledge
✅ You want to share capabilities across different agents
✅ You need to update knowledge without changing the core

MCP: Model Context Protocol

MCP (Model Context Protocol) is an open standard that allows AI agents to connect with external tools and data sources in a standardized way.

The problem it solves

Before MCP, each AI tool had its own format for connecting with things:

Reading filesystem files
Querying databases
Making HTTP calls
Running commands

It was chaos. Every integration was ad-hoc.

The MCP solution

MCP defines:

How the agent communicates with a tool
What information is passed (inputs/outputs)
How errors are handled
How available capabilities are discovered

Real example

Imagine an MCP server that exposes database access:

# The MCP server exposes tools
tools:
  - name: "query_database"
    description: "Executes SQL queries in the database"
    input_schema:
      type: "object"
      properties:
        query:
          type: "string"
          description: "SQL query to execute"

The agent can:

Discover that this tool exists
Know how to use it (what inputs it accepts)
Execute queries safely
Receive structured results

MCP Servers

An MCP server is a program that exposes functionality through the MCP protocol. It can be:

Local: Runs on your machine
Remote: Runs on a server
Hybrid: Mix of both

Examples of MCP servers:

Filesystem: Read/write files
Database: SQL queries
Git: Repository operations
API: Calls to external services
Memory: Persistent storage for the agent

Advantages of MCP

Open standard: You’re not vendor-locked
Community: There are hundreds of MCP servers already created
Security: You control what tools the agent can use
Composability: Combine multiple MCP servers
Extensible: Easy to create new servers

Why MCP is important for AI Coding Agents

MCP allows agents to:

Read your code (filesystem server)
Understand your git history (git server)
Query documentation (docs server)
Run tests (test runner server)
Connect to APIs (HTTP server)

All this in a standardized and secure way.

Hooks: Event-Based Customization

Hooks are insertion points where you can execute custom logic in response to specific events in the agent’s lifecycle.

How they work

A hook is defined as:

When to execute (which event)
What to execute (command or script)
How to use the result (modify behavior)

Real example

A pre-commit hook could:

Detect that the agent is about to commit
Run a linter on changed files
If there are errors, prevent the commit
If everything is fine, allow the commit

Types of common hooks

1. Lifecycle hooks

before-work - Before starting a task
after-work - After completing a task
before-edit - Before modifying a file
after-edit - After modifying a file

2. Event hooks

on-error - When something fails
on-success - When something succeeds
on-retry - When something is retried

3. Tool-use hooks

before-command - Before executing a command
after-command - After executing a command
before-tool-use - Before using a tool
after-tool-use - After using a tool

What to use hooks for

Hooks are useful for:

Validation: Verify that the agent does the right thing
Logging: Register what the agent does
Modification: Change behavior based on context
Integration: Connect with external tools
Security: Prevent dangerous actions

Audio hook example (my personal configuration)

A practical use of hooks is receiving audio feedback when the agent performs actions. In my Claude Code configuration I have hooks that play sounds:

{
  "hooks": {
    "Notification": [
      {
        "matcher": "*",
        "hooks": [
          {
            "type": "command",
            "command": "afplay /System/Library/Sounds/Funk.aiff"
          }
        ]
      }
    ],
    "Stop": [
      {
        "matcher": "*",
        "hooks": [
          {
            "type": "command",
            "command": "afplay /System/Library/Sounds/Hero.aiff"
          }
        ]
      }
    ]
  }
}

This means:

Every time there’s a notification, a “Funk” sound plays
When the agent finishes its work, a “Hero” sound plays

It’s a simple but effective way to know what the agent is doing without constantly looking at the screen.

Security hook example

hooks:
  before-tool-use:
    - if: tool.name == "bash" && command.contains("rm -rf")
      then: block("rm -rf not allowed without explicit confirmation")

This hook prevents the agent from executing dangerous commands without approval.

Hooks vs Rules

Aspect	Rules	Hooks
When applied	Always, in every interaction	At specific events
Purpose	General behavior	Response to events
Flexibility	Static	Dynamic, can evaluate context
Example	“Don’t use words in images”	“Before running bash, verify the command”

How everything works together

The interesting thing isn’t each concept separately, but how they work together.

Example: A complete workflow

Imagine you tell your agent: “Add authentication to this API endpoint”.

This could trigger:

Hook before-work: Verifies you have the necessary permissions
Rule: The agent knows it should use JWT, not sessions (global configuration)
Command /auth-boilerplate: Expands an authentication code template
Skill laravel-auth: Applies Laravel-specific knowledge
MCP server docs: Queries official Laravel documentation
MCP server git: Verifies you’re not modifying protected files
Hook after-edit: Runs automatic tests after each modified file
Hook before-command: Before executing php artisan migrate, asks for confirmation

All this without you having to specify each step.

Final analogy: The agent as a junior developer

To understand it all together, think of an AI agent as a very capable junior developer:

Rules = Your team’s guidelines (coding standards, processes)
Commands = The scripts and shortcuts you use daily
Skills = The specialized technologies they know (React, Laravel, Docker)
MCP = The tools they have available (IDE, terminal, APIs)
Hooks = Code reviews and checkpoints during development

The difference is this “junior”:

Works 24/7
Doesn’t get tired
Executes instantly
Has access to your entire codebase at once
Can read documentation faster than any human

Tools that use these concepts

Claude Code

Rules: CLAUDE.md and CLAUDE.local.md files
Commands: /commit, /help, etc.
Skills: Built-in, with more coming
MCP: Full support for MCP servers
Hooks: Configurable for lifecycle events

GitHub Copilot Workspace

Rules: Per-repository configuration
Commands: Predefined workflows
Skills: Integrated with GitHub ecosystem

Cursor

Rules: Per-project configuration
Commands: Customizable shortcuts
MCP: Growing support

Conclusion

Rules, commands, skills, MCP, and hooks are not just technical jargon. They’re the building blocks that make AI agents useful for real software development.

Understanding these concepts allows you to:

Configure your tools better
Automate repetitive workflows
Integrate the agent with your existing stack
Maintain control over what the agent does

AI won’t replace developers. But developers who understand how to configure and use these agents will be much more productive.

In 2026, understanding how to work with AI agents is increasingly valuable.

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AI Coding Agents: Rules, Commands, Skills, MCP and Hooks Explained

First things first: What is an AI Coding Agent?

Rules: Permanent Instructions

What they’re for

Real example

When to use rules

Where to configure them

Commands: Shortcuts for Recurrent Tasks

How they work

Real example

Types of commands

Advantages of using commands

When to create commands

Skills: Specialized Capabilities

Difference vs commands

Real example

Common skills in current tools

Why skills matter

When to use skills

MCP: Model Context Protocol

The problem it solves

The MCP solution

Real example

MCP Servers

Advantages of MCP

Why MCP is important for AI Coding Agents

Hooks: Event-Based Customization

How they work

Real example

Types of common hooks

What to use hooks for

Audio hook example (my personal configuration)

Security hook example

Hooks vs Rules

How everything works together

Example: A complete workflow

Final analogy: The agent as a junior developer

Tools that use these concepts

Claude Code

GitHub Copilot Workspace

Cursor

Conclusion

References