<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Ai Skills on Vasileios Chroniadis blog</title><link>https://chronvas.github.io/tags/ai-skills/</link><description>Recent content in Ai Skills on Vasileios Chroniadis blog</description><generator>Hugo</generator><language>en</language><lastBuildDate>Mon, 15 Jun 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://chronvas.github.io/tags/ai-skills/index.xml" rel="self" type="application/rss+xml"/><item><title>The Power of Neutral Friction- Why the ‘Grill Me’ Skill is a Game-Changer for AI Coding</title><link>https://chronvas.github.io/posts/2026-6-15-the-power-of-neutral-friction-why-the-grill-me-skill-is-a-game-changer-for-ai-coding/</link><pubDate>Mon, 15 Jun 2026 00:00:00 +0000</pubDate><guid>https://chronvas.github.io/posts/2026-6-15-the-power-of-neutral-friction-why-the-grill-me-skill-is-a-game-changer-for-ai-coding/</guid><description>&lt;p&gt;In the rapidly evolving landscape of AI-assisted software engineering, we routinely fall into the “specs-to-code” cycle: a frustrating endless cycle of prompt engineering, trying to perfect instructions rather than engaging with the messy, architectural reality of our thoughts and of our projects.&lt;/p&gt;
&lt;p&gt;The reality is that the specs-to-code approach currently doesn’t work very effectively. LLM’s lack the contextual knowledge, but until the technology evolves, we must find more deliberate ways to work with LLMs.&lt;/p&gt;</description></item></channel></rss>