Updated all files with Acrolinx suggestions. Added forward link to exercise.

Author: ChrisHowd

learn-pr/wwl-azure/configure-customize-github-copilot-visual-studio-code/7-knowledge-check.yml

@@ -36,12 +36,12 @@ quiz:
       explanation: "Incorrect. Neither file type affects inline code completions. Both types of instruction files apply to Copilot Chat and other high-level requests."
   - content: "What is a custom agent in Visual Studio Code's Copilot Chat?"
     choices:
-    - content: "A built-in VS Code extension that adds new programming language support."
+    - content: "A built-in Visual Studio Code extension that adds new programming language support."
       isCorrect: false
-      explanation: "Incorrect. Custom agents are specialized AI configurations for Copilot Chat, not VS Code extensions for language support."
+      explanation: "Incorrect. Custom agents are specialized AI configurations for Copilot Chat, not Visual Studio Code extensions for language support."
     - content: "A specialized AI persona defined by a `.agent.md` file that encapsulates a set of instructions and tool permissions for a particular development role."
       isCorrect: true
-      explanation: "Correct. Custom agents are defined by `.agent.md` Markdown files that include YAML frontmatter for metadata and tool configuration, plus a body with instructions that steer Copilot's behavior when that agent is selected."
+      explanation: "Correct. Custom agents are defined using `.agent.md` Markdown files that include YAML frontmatter for metadata and tool configuration, plus a body with instructions that steer Copilot's behavior when that agent is selected."
     - content: "A remote server that processes Copilot requests on behalf of the developer."
       isCorrect: false
       explanation: "Incorrect. Custom agents are locally defined configurations in `.agent.md` files, not remote server processes."
@@ -53,31 +53,31 @@ quiz:
     - content: "Which actions the agent can perform, such as reading files, editing code, searching the workspace, or running terminal commands."
       isCorrect: true
       explanation: "Correct. The `tools` field restricts or empowers the agent with specific capabilities. For example, a planning agent might only have `['search', 'read']` tools, while an implementation agent would include `['search', 'read', 'edit', 'terminal']`."
-    - content: "The list of third-party extensions that the agent depends on."
+    - content: "The list of non-Microsoft extensions that the agent depends on."
       isCorrect: false
       explanation: "Incorrect. The `tools` field specifies built-in Copilot capabilities (like read, edit, search), not third-party extension dependencies."
   - content: "What is a handoff in the context of custom Copilot agents?"
     choices:
     - content: "A mechanism for transferring Copilot Chat conversations to a human developer."
       isCorrect: false
       explanation: "Incorrect. Handoffs transition between custom agents within Copilot Chat, not from the AI to a human developer."
-    - content: "A transition that switches from one custom agent to another, carrying over context and optionally pre-filling a prompt to continue a multi-step workflow."
+    - content: "A transition that switches from one custom agent to another, carrying over context and optionally prefilling a prompt to continue a multi-step workflow."
       isCorrect: true
-      explanation: "Correct. Handoffs are configured in the YAML frontmatter of an agent file and create buttons that, when selected, switch to a target agent with the conversation context preserved and an optional prompt pre-filled."
+      explanation: "Correct. Handoffs are configured in the YAML frontmatter of an agent file and create buttons that, when selected, switch to a target agent with the conversation context preserved and an optional prompt prefilled."
     - content: "A process that automatically saves the agent's output to a file in the workspace."
       isCorrect: false
       explanation: "Incorrect. Handoffs are transitions between agents in a workflow, not file-saving operations."
-  - content: "When configuring a handoff, what does setting `send: false` mean?"
+  - content: "When a developer configures a handoff, what does setting `send: false` mean?"
     choices:
     - content: "The handoff button is hidden from the user interface."
       isCorrect: false
       explanation: "Incorrect. The `send` field controls whether the prompt is automatically submitted, not whether the handoff button is visible."
-    - content: "The prompt is pre-filled in the chat input for the developer to review and edit before sending, rather than being submitted automatically."
+    - content: "The prompt is prefilled in the chat input for the developer to review and edit before sending, rather than being submitted automatically."
       isCorrect: true
-      explanation: "Correct. When `send` is `false` (the default), the handoff pre-fills the prompt text but waits for the developer to review and submit it manually, keeping the human in the loop."
-    - content: "The handoff is disabled and the agent cannot transition to the target agent."
+      explanation: "Correct. When `send` is `false` (the default), the handoff prefills the prompt text but waits for the developer to review and submit it manually, keeping the human in the loop."
+    - content: "The handoff is disabled and the agent can't transition to the target agent."
       isCorrect: false
-      explanation: "Incorrect. The `send: false` setting doesn't disable the handoff. It simply requires the developer to manually submit the pre-filled prompt rather than having it sent automatically."
+      explanation: "Incorrect. The `send: false` setting doesn't disable the handoff. It simply requires the developer to manually submit the prefilled prompt rather than having it sent automatically."
   - content: "What is the purpose of a `.prompt.md` file in the `.github/prompts/` folder?"
     choices:
     - content: "It defines a custom agent persona with specific tool permissions."
@@ -97,6 +97,6 @@ quiz:
     - content: "Subagents are invoked automatically within an agent's processing and return results to the calling agent, while handoffs transfer control to the user to approve a transition to another agent."
       isCorrect: true
       explanation: "Correct. Subagents are called programmatically by the parent agent during its response processing—the result flows back to the calling agent automatically. Handoffs, by contrast, present a button to the user who decides whether to transition to the next agent."
-    - content: "Subagents can only run in the cloud, while handoffs run locally in VS Code."
+    - content: "Subagents can only run in the cloud, while handoffs run locally in Visual Studio Code."
       isCorrect: false
       explanation: "Incorrect. Both subagents and handoffs can run in either the client or the cloud, depending on the agent's `target` configuration."

learn-pr/wwl-azure/configure-customize-github-copilot-visual-studio-code/includes/1-introduction.md

@@ -1,17 +1,17 @@
-GitHub Copilot in Visual Studio Code offers powerful AI-assisted coding out of the box, but its true potential emerges when you customize it to match your team's specific workflows and project requirements. By providing custom instructions and creating specialized agents, you can transform Copilot from a general-purpose assistant into a set of tailored AI collaborators that understand your codebase, follow your conventions, and handle multi-step development tasks.
+GitHub Copilot in Visual Studio Code offers powerful AI-assisted coding out of the box, but its true potential emerges when you customize it to match your team's specific workflows and project requirements. By providing custom instructions and creating specialized agents, you can transform GitHub Copilot from a general-purpose assistant into a set of tailored AI collaborators that understand your codebase, follow your conventions, and handle multi-step development tasks.
 
-Imagine you're a developer on a team that maintains a large C# application. Your team has specific coding standards, architectural patterns, and review processes that every pull request must follow. Instead of repeating these guidelines in every Copilot Chat prompt, you can embed them directly into instruction files that Copilot reads automatically. You can also create custom agents—specialized AI personas like a "Planner" that designs implementation strategies or a "Code Reviewer" that checks for security issues—and chain them together so one agent's output flows into the next.
+Imagine you're a developer on a team that maintains a large C# application. Your team has specific coding standards, architectural patterns, and review processes that every pull request must follow. Instead of repeating these guidelines in every GitHub Copilot Chat prompt, you can embed them directly into instruction files that GitHub Copilot reads automatically. You can also create custom agents—specialized AI personas like a "Planner" that designs implementation strategies or a "Code Reviewer" that checks for security issues—and chain them together so one agent's output flows into the next.
 
 The topics covered in this module include:
 
-- Using repository-based custom instructions and prompt files to influence Copilot's coding style and guidelines.
-- Defining and using custom Copilot agents for specialized tasks such as planning, coding, and testing.
+- Using repository-based custom instructions and prompt files to influence GitHub Copilot's coding style and guidelines.
+- Defining and using custom GitHub Copilot agents for specialized tasks such as planning, coding, and testing.
 - Configuring subagents, agent skills, and organization-level agents.
 - Chaining agents through handoffs to accomplish multi-step development scenarios.
 
 After completing this module, you'll be able to:
 
-- Customize Copilot's responses using instruction files and prompt files to enforce project-specific coding styles, best practices, and guidelines.
+- Customize GitHub Copilot's responses using instruction files and prompt files to enforce project-specific coding styles, best practices, and guidelines.
 - Create custom GitHub Copilot agents in Visual Studio Code with tailored instructions, tool permissions, and subagent configurations.
 - Configure and use agent handoffs to orchestrate multi-step development workflows.
 - Implement a real-world scenario by defining custom instructions and chaining multiple custom agents to collaboratively complete a coding task.

learn-pr/wwl-azure/configure-customize-github-copilot-visual-studio-code/includes/2-examine-benefits-customize-github-copilot.md

@@ -2,23 +2,23 @@ GitHub Copilot provides helpful code suggestions and chat responses right away,
 
 ## Examine the benefits of customization
 
-Out-of-the-box, Copilot generates suggestions based on its general training data and the immediate code context. While this produces useful results for many common tasks, it can miss project-specific nuances. For example, your team might enforce specific architectural patterns like the repository pattern for data access, require particular naming conventions such as prefixing private fields with an underscore, or follow security practices that mandate input validation on every API endpoint. Without customization, Copilot doesn't know about these requirements and may generate code that needs significant manual editing to meet your standards.
+Out-of-the-box, GitHub Copilot generates suggestions based on its general training data and the immediate code context. While the base capability produce useful results for many common tasks, it can miss project-specific nuances. For example, your team might enforce specific architectural patterns like the repository pattern for data access, require particular naming conventions such as prefixing private fields with an underscore, or follow security practices that mandate input validation on every API endpoint. Without customization, GitHub Copilot doesn't know about these requirements and might generate code that needs significant manual editing to meet your standards.
 
 Customization addresses this challenge through several complementary approaches:
 
-- **Custom instruction files**: Markdown files stored in your repository that provide Copilot with always-on guidelines. These files describe your coding standards, preferred libraries, naming conventions, and other project-specific rules. Copilot reads these instructions and incorporates them into every chat response, producing suggestions that are aligned with your team's expectations from the start.
+- **Custom instruction files**: Markdown files stored in your repository that provide GitHub Copilot with always-on guidelines. These files describe your coding standards, preferred libraries, naming conventions, and other project-specific rules. GitHub Copilot reads these instructions and incorporates them into every chat response, producing suggestions that are aligned with your team's expectations from the start.
 
-- **Prompt files**: Reusable prompt templates (`.prompt.md` files) that you can invoke as slash commands in Copilot Chat. While instruction files shape how Copilot responds, prompt files define what to ask—standardizing common tasks like generating tests, creating documentation, or performing code reviews so they run consistently across your team.
+- **Prompt files**: Reusable prompt templates (`.prompt.md` files) that you can invoke as slash commands in GitHub Copilot Chat. While instruction files shape how GitHub Copilot responds, prompt files define what to ask—standardizing common tasks like generating tests, creating documentation, or performing code reviews so they run consistently across your team.
 
-- **Custom agents**: Specialized AI personas that you define for particular development roles. Each agent has its own set of instructions, tool permissions, and behavioral guidelines. For instance, a "Planner" agent can focus on analyzing requirements and producing implementation plans without editing code, while an "Implementer" agent can focus on writing code that follows your project's guidelines. Agents give you fine-grained control over how Copilot behaves in different stages of your workflow.
+- **Custom agents**: Specialized AI personas that you define for particular development roles. Each agent has its own set of instructions, tool permissions, and behavioral guidelines. For instance, a "Planner" agent can focus on analyzing requirements and producing implementation plans without editing code, while an "Implementer" agent can focus on writing code that follows your project's guidelines. Agents give you fine-grained control over how GitHub Copilot behaves in different stages of your workflow.
 
 - **Agent skills**: Structured descriptions of specific capabilities or expertise areas (defined in `SKILL.md` files) that agents can draw on to provide specialized knowledge. Skills complement agent instructions by exposing metadata about what an agent knows and can do.
 
-Together, these features allow you to guide Copilot toward producing output that matches your team's conventions, reducing the editing overhead and improving consistency across your codebase.
+Together, these features allow you to guide GitHub Copilot toward producing output that matches your team's conventions, reducing the editing overhead and improving consistency across your codebase.
 
 ## Modern AI-assisted workflows
 
-Software development increasingly involves AI assistants in various roles beyond basic code completion. Developers use AI to plan features, generate tests, review code for security issues, automate refactoring, and more. Tailoring Copilot ensures these AI-assisted activities understand your project's context and produce results that integrate smoothly into your existing processes.
+Software development increasingly involves AI assistants in various roles beyond basic code completion. Developers use AI to plan features, generate tests, review code for security issues, automate refactoring, and more. Tailoring GitHub Copilot ensures these AI-assisted activities understand your project's context and produce results that integrate smoothly into your existing processes.
 
 Consider a typical feature development workflow:
 
@@ -34,4 +34,4 @@ This approach mirrors how experienced development teams already work—with diff
 
 ## Summary
 
-Customizing Copilot allows developers to infuse project-specific knowledge into AI suggestions and leverage multiple AI agents in a coordinated way. By combining custom instruction files with specialized agents and agent handoffs, teams can build AI-assisted workflows that span the full development lifecycle—from planning through implementation to review—while maintaining the coding standards and practices that matter to their projects.
+Customizing GitHub Copilot allows developers to infuse project-specific knowledge into AI suggestions and apply multiple AI agents in a coordinated way. By combining custom instruction files with specialized agents and agent handoffs, teams can build AI-assisted workflows that span the full development lifecycle—from planning through implementation to review—while maintaining the coding standards and practices that matter to their projects.