Claude Skills: The NEW Way to Build AI Agents (Live Tutorial)

October 27, 2025

AI Summary

5 min read

Claude Skills: Building Deterministic AI Agents That Actually Work

In this episode of The Startup Ideas Podcast, Amir walks through a live tutorial on Claude Skills, demonstrating how to build digital employees that follow precise, repeatable instructions rather than generating unpredictable outputs. The key insight: most people who complain that AI "doesn't work" are actually failing to give it the right constraints and context.

What Claude Skills Actually Are

Claude Skills sit between two existing Anthropic features: projects and subagents. Projects are workspaces with custom instructions, relevant context files, and tools—useful for team collaboration but requiring constant manual updates as business data changes. Subagents, available in Claude Code, let you spin up specialized agents for complex multi-step workflows, like delegating frontend work to one agent and backend to another.

Skills are different. They are automated workflows that apply globally across projects or individual chats. A skill is a markdown file containing precise instructions, reference files, and optionally custom scripts that run actual code. The critical difference: skills only load context when it's relevant to the specific task, rather than having the LLM decide which context to retrieve. This makes them more deterministic and less prone to hallucination.

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What you'll learn

1 (00:00) **Introduction to Claude Skills** - Amir introduces the concept of Claude Skills as a way to build digital employees, covering A/B testing, marketing insights, and a live build.
2 (01:04) **Claude Projects vs. Subagents vs. Skills** - Amir explains the foundational differences between these three Claude features.
3 (03:37) **What Makes Skills Different** - Skills are laser-focused, repeatable instructions that pull context as needed and can run scripts.
4 (05:58) **The Problem Skills Solve: Context Rot** - Amir explains how too much context can degrade LLM performance and lead to hallucinations.
5 (08:00) **How Custom Skills Work** - A markdown file defines the skill, with optional reference files and custom scripts for deterministic execution.
6 (11:10) **Live Demo: Artifact Builder Skill** - Amir shows how to use a pre-built skill to create a UTM link generator web app.
7 (17:43) **Live Demo: A/B Testing Idea Generator** - Using a custom skill to scrape a URL and generate A/B test experiments with an ICE score framework.

+ Full timestamped outline available in the app

Show Notes

In this episode, Amir takes us through how to use Claude Skills to build digital employees. We cover practical demos including an A/B testing idea agent, marketing insight analyzer, and a live build of a tweet-to-newsletter converter. You'll learn what Claude Skills actually are, why they represent the biggest leap since sub-agents, and how to build them yourself—even if you've never written a custom AI workflow before.

Timestamps

00:00 – Intro

01:05 – What are Claude Projects

02:40 – Sub-agents in Claude Code explained

03:34 – Introducing Claude Skills

05:58 – Context rot and the performance degradation problem

08:01 – Why matter Claude Skills Matter

11:08 – Building a UTM link generator with Artifact Builder

17:41 – Claude Skill Demo: A/B test generator for website optimization

20:32 – Claude Skill Demo: Marketing analytics insights from campaign data

23:40 – Building a Claude Skill: Creating a tweet-to-newsletter converter skill

30:32 – Final Thoughts on Claude Skills

30:58 – Why AI adoption is falling and how better prompting solves it

Key Points

Claude Skills are automated workflows that apply globally or per-project, pulling context only when relevant to specific tasks
Skills solve the "context rot" problem where too much context degrades LLM performance and increases hallucination
You can create custom Skills using markdown files with instructions, reference documents, and executable scripts
The tweet-to-newsletter converter built live demonstrates Skills' ability to match tone and style with minimal training
Poor AI fluency and prompting—not the tools themselves—explain why enterprise AI adoption is declining

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