chore: sync content to repo (#9590)

Co-authored-by: kamranahmedse <4921183+kamranahmedse@users.noreply.github.com>

src/data/roadmaps/backend/content/agents@w1D3-bSg93ndKK9XJTu7z.md

@@ -1,4 +1,4 @@
-# AI Agents in AI-Assisted Coding
+# AI Agents
 
 AI agents are autonomous programs designed to help developers write code more efficiently. These agents can automate repetitive tasks, suggest code completions, identify errors, and even generate entire code blocks based on natural language descriptions or existing code patterns. They leverage machine learning models to understand code syntax, semantics, and context, enabling them to provide intelligent and relevant assistance throughout the software development lifecycle.
 

src/data/roadmaps/backend/content/mcp@mHnI19RgZ57XDEIDaHxD0.md

@@ -4,9 +4,9 @@ Model Context Protocol (MCP) is a rulebook that tells an AI agent how to pack ba
 
 Visit the following resources to learn more:
 
+- [@course@MCP: Build Rich-Context AI Apps with Anthropic](https://www.deeplearning.ai/short-courses/mcp-build-rich-context-ai-apps-with-anthropic/)
 - [@official@Model Context Protocol](https://modelcontextprotocol.io/introduction)
 - [@opensource@modelcontexprotocol](https://github.com/modelcontextprotocol/modelcontextprotocol)
 - [@article@The Ultimate Guide to MCP](https://guangzhengli.com/blog/en/model-context-protocol)
 - [@article@A Clear Intro to MCP (Model Context Protocol) with Code Examples](https://towardsdatascience.com/clear-intro-to-mcp/)
-- [@article@Model Context Protocol (MCP): A Guide With Demo Project](https://www.datacamp.com/tutorial/mcp-model-context-protocol)
-- [@course@MCP: Build Rich-Context AI Apps with Anthropic](https://www.deeplearning.ai/short-courses/mcp-build-rich-context-ai-apps-with-anthropic/)
+- [@article@Model Context Protocol (MCP): A Guide With Demo Project](https://www.datacamp.com/tutorial/mcp-model-context-protocol)

src/data/roadmaps/backend/content/skills@IWubGe3oquSj882QVnhLU.md

@@ -1,4 +1,4 @@
-# Skills for AI Coding Assistants
+# Skills
 
 AI coding assistants can now leverage "skills" – pre-defined functions or tools exposed to the AI that allow it to perform specific actions. Instead of relying solely on their internal knowledge and large language models, these assistants can call upon these skills (like running tests, deploying code, or querying databases) when needed. This allows the AI to interact with the development environment and execute tasks directly, improving accuracy and efficiency, all while minimizing the context window required for each action.