# Talking about Skills, Optimizing Prompts and building MCP servers in apps
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## Description
We’re diving deep into the latest paradigms in AI development, starting with the difference between traditional context files (Gemini.md) and the new "Agent Skills" dynamic.
We also share a story about using the Vertex AI Prompt Optimizer to automate our YouTube descriptions. It took 5 hours and nearly 100 million tokens, but the results were surprisingly consistent. Finally, we geek out on the Model Context Protocol (MCP), experimenting with exposing Flutter application state as local tools using Unix sockets.
In this episode:
0:00 - Intro: Why we are sitting today.
0:30 - What are Agent Skills? (Anthropic & Antigravity support) .
1:49 - Skills vs. Context: The difference between lazy loading and concatenating context files.
4:59 - How the API discovers and uses Skills.
7:25 - The open ecosystem for sharing and installing Skills.
10:30 - Deep Dive: Using Vertex AI Prompt Optimizer for YouTube descriptions.
13:28 - Converting CSVs to JSONL for data-driven optimization.
15:25 - The 100 Million Token experiment.
19:20 - Exploring MCP (Model Context Protocol) with Flutter.
22:03 - Using Unix Sockets vs. HTTP for local app control.
24:25 - Implementing the MCP spec in Antigravity.
Resources mentioned:
Open Skills Domain: https://agentskills.io/
Vertex AI Prompt Optimizer (Colab Notebooks): https://cloud.google.com/blog/products/ai-machine-learning/announcing-vertex-ai-prompt-optimizer
Boost accuracy with the prompt optimizer blog: https://firebase.blog/posts/2026/01/boost-accuracy-with-the-prompt-optimizer
Connect with us: Let us know what you want us to talk about next!
#AI #Gemini #AgentSkills #MCP #Flutter #VertexAI #PromptEngineering #TechPodcast