I was at a wedding this past weekend, so I was a little quiet on posting. Spent a few days in Dallas, Texas celebrating some really great friends. It was a nice reminder to slow down, be present, and enjoy the moment. Back at it this week.

I've been exploring one of the most significant developments in AI assistance technology, and I need to share this with my network. Anthropic has introduced Skills in Claude a modular system that transforms a general-purpose AI into a specialized expert on demand. Think of it as giving your AI assistant the ability to load exactly the right expertise, workflows, and tools for any specific task. Why This Changes Everything: Traditional AI assistants are generalists. They know a bit about everything, but lack the procedural depth for specialized work. Skills solve this elegantly by providing Claude with: → Specialized workflows for multi-step domain-specific processes → Tool integrations for specific file formats, APIs, and systems → Domain expertise including company-specific knowledge and business logic → Bundled resources like scripts, templates, and reference materials What's Available Out of the Box: The current skill library is impressive. Professional document creation (Word, PowerPoint, Excel, PDF), production-grade frontend design that avoids generic "AI slop" aesthetics, algorithmic art generation, MCP server building, and sophisticated theme systems—all with best practices built in. The Real Game-Changer: Custom Skills Here's where it gets exciting for businesses. You can create your own skills that encode your company's brand guidelines, internal processes, database schemas, and proprietary workflows. I've implemented custom skills for Plexaris that automatically apply our brand identity—colors, typography, visual standards—to any deliverable Claude creates. Imagine asking Claude to create a pitch deck, and it automatically knows your exact brand colors, your preferred fonts , and your glass morphism card styling. That's not a template that's institutional knowledge embedded in your AI assistant. The Technical Elegance: What impressed me most is the progressive disclosure architecture. Skills use a three-level loading system: metadata always present (~100 words), the skill body loaded only when triggered (<5k words), and bundled resources pulled only as needed. This means efficiency without sacrificing capability.

Level: Foundational Category: AI System Categories This term introduces the major categories of AI systems and what they are designed to do in practical use. 🪄 Simple Definition: A Large Language Model (LLM) is an AI system trained to understand, interpret, and generate human language. 🌟 Expanded Definition: LLMs are built using deep learning and trained on massive collections of text. This enables them to recognize patterns, understand context, and produce writing that feels natural and human-like. Examples include ChatGPT, Claude, Gemini, and Grok.LLMs can summarize documents, answer questions, write content, support research, and assist in decision-making.They don’t “think” like people—they generate responses based on statistical patterns learned during training. ⚡ In Action: You type: “Draft a follow-up message for customers who missed their service appointment.” The LLM produces a polished, professional message in seconds. 💡 Pro Tip: Clear instructions produce stronger results. Define the role, purpose, tone, and audience to guide the model effectively. This term is part of the Classroom Course - AI Fundamentals

There’s a lot of discussion about how overwhelming AI can feel—especially with the sheer breadth of products and services, and the speed at which new revisions and updates keep rolling out. For many people, it creates a constant sense of playing catch-up. So whether you’re just starting out, or you feel like you’re simply keeping pace, the best place to start—or recenter—is prompting. Prompting is the foundation of working with AI. It’s the way we express intent, provide context, and guide direction when interacting with intelligent systems. Not as a trick. Not as a hack. But as the underlying mechanism that determines whether AI feels helpful—or frustrating. 💎 Why Prompting Comes First 💎 Every AI interaction follows the same basic loop: You give input. AI responds. You react, refine, or redirect. No matter the tool, that loop doesn’t change. If your intent is unclear, the output will be too. If your context is thin, the response will be shallow. If your direction is vague, results will feel inconsistent. Better tools don’t fix that. Clear prompting does. 💎 Prompting Is About Thinking, Not Typing 💎 It’s easy to think prompting is about what words you use. It’s not. It’s about: - Knowing what you’re actually trying to achieve - Providing enough context for AI to work intelligently - Setting boundaries and expectations - Being willing to refine instead of restarting The strongest prompts usually come from clearer thinking—not longer instructions. 💎 Why This Transfers Across Tools 💎 This is why prompting shows up everywhere. Once you learn how to: - Frame a request clearly - Ask follow-up questions - Adjust direction through iteration You’ll notice something interesting happen. New AI tools start to feel familiar. Different interfaces. Different outputs. Same underlying conversation. That’s not coincidence. That’s the foundation at work. 💎 The Diamond in the Rough 💎 Prompting is often taken for granted. Because it feels simple, people assume it’s basic.

We gave AI Bits & Pieces logo a little visual refresh. It stands out a bit more. And honestly… we think it’s fun. Total marketing effort? We asked our daughter which one she liked better. She picked this one. Bingo — new logo. You’ll start seeing it everywhere.