Extending Ai With Agent Skills 2026
Published 2/2026
Created by Anthony Alicea
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 33 Lectures ( 1h 28m ) | Size: 811 MB
What you'll learn
✓ Explain how context windows work and why AI agents lose focus in long conversations (context rot)
✓ Apply progressive disclosure to load skill instructions only when needed, keeping agent context lean
✓ Create a production-ready SKILL file and folder with proper metadata, instructions, scripts, and resources
✓ Design portable skills that work across Claude Code, GitHub Copilot, Cursor, and OpenAI Codex
Requirements
● Basic familiarity with any AI coding agent (Claude Code, Copilot, Cursor, or similar) =
● A computer with access to at least one AI coding agent to follow along with examples and the final project
Description
NEW COURSE! AI coding agents like Claude Code, GitHub Copilot, Cursor, and Codex are powerful, but they don't know your workflows, your standards, or your domain expertise.
You've probably experienced the frustration: the agent "forgets" your instructions mid-session, produces inconsistent outputs, or requires you to repeat the same context every single time.
This isn't a prompting problem. It's a context problem.
And there's now an official standard that solves it.
What Are AI Agent Skills?
Agent Skills is a new open standard, maintained by Anthropic and adopted by Claude Code, GitHub Copilot, Cursor, OpenAI Codex, and others, that lets you extend any compatible AI agent with portable, reusable capabilities.
Instead of stuffing everything into a bloated system prompt and hoping the agent pays attention, Skills use progressive disclosure: the agent loads only what it needs, when it needs it.
The result? Leaner context, more consistent outputs, and expertise that travels with you across tools.
Write a skill once. Use it everywhere.
What You'll Learn
The Context Problem
Why AI agents struggle with long conversations, what "context rot" actually is, and why bigger context windows don't solve the problem.
How Skills Work
The progressive disclosure pattern: discovery, metadata loading, task matching, activation, and execution. You'll understand why skills are architected this way, not just how to use them.
Skill Anatomy
SKILL file and folder structure, YAML frontmatter, instructions, scripts, references, and assets. What goes where and why.
Skill Authoring
How to write skills that actually work: good metadata for discoverability, clear instructions, token budget awareness, and designing for portability across agents.
Final Project
You'll create a production-ready skill for your own workflow, something you can actually use the day you finish this course.
How This Course Teaches
If you've taken my other courses, you know the approach: Don't Imitate, Understand.
This isn't a tutorial where you copy what I type and hope it works. We'll go under the hood. You'll learn why context windows create problems for agents, why progressive disclosure solves them, and why the skill specification is designed the way it is.
When you understand the mechanics, you can adapt. You can debug. You can build skills the documentation doesn't cover.
Who This Is For
Developers using Claude Code, GitHub Copilot, Cursor, or Codex who want consistent, repeatable results.
Team leads who want to encode standards and workflows that agents follow automatically.
Anyone tired of repeating the same instructions every session.
Prerequisites: Basic familiarity with AI coding agents. No specific programming language required. Skills are language-agnostic.
Why Now?
Agent Skills is new. The specification was open-sourced in late 2025 by Anthropic, and adoption is accelerating fast. OpenAI, Google, and Microsoft have all added support.
The developers who understand this standard early will be the ones building the skills everyone else uses.
This is a short course. You can finish it in an afternoon and have a working skill by dinner.
Who this course is for
■ Developers using Claude Code, GitHub Copilot, Cursor, or Codex who want consistent results without repeating instructions
■ Team leads who want to encode coding standards, workflows, and domain expertise that AI agents follow automatically
■ Anyone frustrated by AI agents "forgetting" context mid-session or producing inconsistent outputs across conversations
■ Early adopters who want to understand emerging AI agent standards before they become mainstream requirements
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