---
title: 'GitHub Copilot Terminology Glossary'
description: 'A quick reference guide defining common GitHub Copilot and platform-specific terms.'
authors:
  - GitHub Copilot Learning Hub Team
lastUpdated: 2025-12-15
estimatedReadingTime: '8 minutes'
tags:
  - glossary
  - terminology
  - reference
relatedArticles:
  - ./what-are-agents-skills-instructions.md
  - ./copilot-configuration-basics.md
---

New to GitHub Copilot customization? This glossary defines common terms you'll encounter while exploring agents, skills, instructions, and related concepts in the Awesome GitHub Copilot ecosystem.

Use this page as a quick reference when reading articles in the Learning Hub or browsing the repository.

---

## Core Concepts

### Agent

A specialized configuration file (`*.agent.md`) that defines a GitHub Copilot persona or assistant with specific expertise, tools, and behavior patterns. Agents integrate with MCP servers to provide enhanced capabilities for particular workflows (e.g., "Terraform Expert" or "Security Auditor").

**When to use**: For recurring workflows that benefit from deep tooling integrations and persistent conversational context.

**Learn more**: [What are Agents, Skills, and Instructions](../what-are-agents-skills-instructions/)

---

### Built-in Tool

A native capability provided by GitHub Copilot without requiring additional configuration or MCP servers. Examples include code search, file editing, terminal command execution, and web search. Built-in tools are always available and don't require installation.

**Related terms**: [Tools](#tools), [MCP](#mcp-model-context-protocol)

---

### Chat Mode

**Deprecated terminology** - This term is no longer used. Use [Agent](#agent) instead.

Previously, "chat mode" was an alternative term for [Agent](#agent) that described how GitHub Copilot Chat could be transformed into domain-specific assistants. The ecosystem has standardized on "Agent" as the preferred terminology.

**See**: [Agent](#agent)

---

### Collection

**Note**: Collections are a concept specific to the Awesome GitHub Copilot repository and are not part of standard GitHub Copilot terminology.

A curated grouping of related skills, instructions, and agents organized around a specific theme or workflow. Collections are defined in YAML files (`*.collection.yml`) in the `collections/` directory and help users discover related customizations together.

**Example**: The "Awesome Copilot" collection bundles meta-skills for discovering and generating GitHub Copilot customizations.

**Learn more**: [What are Agents, Skills, and Instructions](../what-are-agents-skills-instructions/)

---

### Custom Agent

See [Agent](#agent). The term "custom" emphasizes that these are user-defined configurations rather than GitHub Copilot's default behavior. Custom agents can be created by anyone and shared via repositories like Awesome GitHub Copilot.

---

### Custom Instruction

See [Instruction](#instruction). The term "custom" emphasizes that these are user-defined rules rather than GitHub Copilot's built-in understanding. Custom instructions are particularly useful for codifying team-specific standards and architectural decisions.

---

## Configuration & Metadata

### Front Matter

YAML metadata placed at the beginning of Markdown files (between `---` delimiters) that provides structured information about the file and controls its behavior. In this repository, front matter typically includes fields like `name`, `description`, `mode`, `model`, `tools`, and `applyTo`.

The front matter is what controls:
- **Tool access**: Which built-in and MCP tools the customization can use
- **Model selection**: Which AI model powers the customization
- **Scope**: Where the customization applies (e.g., `applyTo` patterns for instructions)

**Note**: Not all fields are common across all customization types. Refer to the specific documentation for agents, skills, or instructions to see which fields apply to each type.

**Example**:
```yaml
---
name: 'React Component Generator'
description: 'Generate modern React components with TypeScript'
mode: 'agent'
tools: ['codebase']
---
```

**Used in**: Skills, agents, instructions, and Learning Hub articles.

---

### AGENTS.md

An emerging industry standard file format for defining portable AI coding instructions that work across different AI coding tools (GitHub Copilot, Claude, Codex, and others). The `AGENTS.md` file, typically placed in a repository root or `.github/` directory, contains instructions for how AI assistants should interact with your codebase.

Unlike tool-specific customization files (`.agent.md`, `.prompt.md`, `.instructions.md`), `AGENTS.md` aims to provide a standardized, platform-agnostic way to define AI behavior that can be consumed by multiple tools.

**Key characteristics**:
- Platform-agnostic format for cross-tool compatibility
- Typically contains project context, coding standards, and architectural guidelines
- Located at repository root or in `.github/` directory

**Learn more**: [AGENTS.md Specification](https://agents.md/)

**Related terms**: [Instruction](#instruction), [Front Matter](#front-matter)

---

### Instruction

A configuration file (`*.instructions.md`) that provides persistent background context and coding standards that GitHub Copilot reads whenever working on matching files. Instructions contain style guides, framework-specific hints, and repository rules that help Copilot align with your engineering practices automatically.

**When to use**: For long-lived guidance that applies across many sessions, like coding standards or compliance requirements.

**Learn more**: [What are Agents, Skills, and Instructions](../what-are-agents-skills-instructions/), [Defining Custom Instructions](../defining-custom-instructions/)

---

## Skills & Interactions

### Persona

The identity, tone, and behavioral characteristics defined for an [Agent](#agent). A well-crafted persona helps GitHub Copilot respond consistently and appropriately for specific domains or expertise areas.

**Example**: A "Database Performance Expert" persona might prioritize query optimization and explain concepts using database-specific terminology.

**Related terms**: [Agent](#agent)

---

### Prompt

**Deprecated** — Prompts (`*.prompt.md`) were reusable chat templates that captured specific tasks or workflows, invoked using the `/` command in GitHub Copilot Chat. Prompts have been superseded by [Skills](#skill), which offer the same slash-command invocation plus agent discovery, bundled assets, and cross-platform portability.

If you have existing prompts, consider migrating them to skills. See [Creating Effective Skills](../creating-effective-skills/) for guidance.

**See**: [Skill](#skill)

---

### Skill

A self-contained folder containing a `SKILL.md` file and optional bundled assets (reference documents, templates, scripts) that packages a reusable capability for GitHub Copilot. Skills follow the open [Agent Skills specification](https://agentskills.io/home) and can be invoked by users via `/command` or discovered and invoked by agents automatically.

**Key advantages**:
- **Agent discovery**: Extended frontmatter lets agents find and invoke skills automatically
- **Bundled assets**: Reference files, templates, and scripts provide richer context
- **Cross-platform**: Portable across coding agent systems via the Agent Skills specification

**Example**: A `/generate-tests` skill might include a `SKILL.md` with testing instructions, a `references/test-patterns.md` with common patterns, and a `templates/test-template.ts` starter file.

**When to use**: For standardizing how Copilot responds to recurring tasks, especially when bundled resources improve quality.

**Learn more**: [What are Agents, Skills, and Instructions](../what-are-agents-skills-instructions/), [Creating Effective Skills](../creating-effective-skills/)

---

## Platform & Integration

### MCP (Model Context Protocol)

A standardized protocol for connecting AI assistants like GitHub Copilot to external data sources, tools, and services. MCP servers act as bridges, allowing Copilot to interact with APIs, databases, file systems, and other resources beyond its built-in capabilities.

**Example**: An MCP server might provide access to your company's internal documentation, AWS resources, or a specific database system.

**Learn more**: [Model Context Protocol](https://modelcontextprotocol.io/) | [MCP Specification](https://spec.modelcontextprotocol.io/) | [Understanding MCP Servers](../understanding-mcp-servers/)

**Related terms**: [Tools](#tools), [Built-in Tool](#built-in-tool)

---

### Hook

A shell command or script that runs automatically in response to lifecycle events during a Copilot agent session. Hooks are stored as JSON files in `.github/hooks/` and can trigger on events like session start/end, prompt submission, before/after tool use, and when errors occur. They provide deterministic automation—linting, formatting, governance scanning—that doesn't depend on the AI remembering to do it.

**Example**: A `postToolUse` hook that runs Prettier after the agent edits files, or a `preToolUse` hook that blocks dangerous shell commands.

**When to use**: For deterministic automation that must happen reliably, like formatting code, running linters, or auditing prompts for compliance.

**Learn more**: [Automating with Hooks](../automating-with-hooks/)

**Related terms**: [Agent](#agent), [Coding Agent](#coding-agent)

---

### Coding Agent

The autonomous GitHub Copilot agent that works on issues in a cloud environment without continuous human guidance. You assign an issue to Copilot, it spins up a dev environment, implements a solution, runs tests, and opens a pull request for review.

**Key characteristics**:
- Runs in an isolated cloud environment
- Uses your repository's instructions, agents, skills, and hooks
- Always produces a PR—it can't merge or deploy
- Supports iteration via PR comments

**When to use**: For well-defined tasks with clear acceptance criteria that can be completed autonomously.

**Learn more**: [Using the Copilot Coding Agent](../using-copilot-coding-agent/)

**Related terms**: [Agent](#agent), [Hook](#hook)

---

### Plugin

An installable package that extends GitHub Copilot CLI with a bundled set of agents, skills, hooks, MCP server configurations, and LSP integrations. Plugins provide a way to distribute and share custom capabilities across projects and teams, with versioning, discovery, and one-command installation via marketplaces.

**Example**: Installing `database-data-management@awesome-copilot` to get a database specialist agent, migration skills, and schema validation hooks in a single command.

**When to use**: When you want to share a curated set of Copilot capabilities across multiple projects or team members, or when you want to install community-contributed tooling without manually copying files.

**Learn more**: [Installing and Using Plugins](../installing-and-using-plugins/)

**Related terms**: [Agent](#agent), [Skill](#skill), [Hook](#hook)

---

### Tools

Capabilities that GitHub Copilot can invoke to perform actions or retrieve information. Tools fall into two categories:

1. **Built-in tools**: Native capabilities like `codebase` (code search), `terminalCommand` (running commands), and `web` (web search)
2. **MCP tools**: External integrations provided by MCP servers (e.g., database queries, cloud resource management, or API calls)

Agents and skills can specify which tools they require or recommend in their front matter.

**Example front matter**:
```yaml
tools: ['codebase', 'terminalCommand', 'github']
```

**Related terms**: [MCP](#mcp-model-context-protocol), [Built-in Tool](#built-in-tool), [Agent](#agent)

---

**Have a term you'd like to see added?** Contributions are welcome! See our [Contributing Guidelines](https://github.com/github/awesome-copilot/blob/main/CONTRIBUTING.md) for how to suggest additions to this glossary.
