Your Skills Folder Is a Junk DrawerYour Skills Folder Is a Junk Drawer<br>I live in these tools. AI coding assistants are most of my workflow at this point. I have a few skills. I don't like them. And I think the energy the community is pouring into building and collecting them is mostly wasted.

Not a shortage of skills, an excess. People are building skills for everything. Frontend design skills. Auth skills. Testing skills. Deployment skills. Code review skills. There are curated lists of "20 skills you should install" and tutorials on building skill libraries for your team. Whole marketplaces now. Community repos. Essential skill packs.

Most of these skills shouldn't exist.

I build and maintain LLM-powered tools used by thousands of real users at Enrollment Resources and VirtuOHS.

What AI Coding Skills Actually Are<br>Let's be honest about what a skill is. It's a prompt you got tired of typing. You kept pasting the same instructions into the chat, "use our component patterns," "follow this test structure," "review for these things," and at some point you saved it to a markdown file so you wouldn't have to keep repeating yourself. That's all a skill is. A saved prompt with a name.

I'm going to talk about this in terms of Claude Code skills because that's what I use every day, but the problem isn't specific to Claude Code. Cursor has rules files. Windsurf has its own configuration. Claude.ai's chat interface has custom instructions and project knowledge. Every AI tool now has some mechanism for "write markdown, shape behavior," and every community around these tools is building and sharing libraries of them. The failure mode is the same everywhere.

Skills are supposed to be smart about it. The pitch is that the LLM recognizes when to invoke a skill based on its description. You're working on frontend code, it notices it has a frontend-design skill, and reaches for it automatically. This doesn't work. Skills get ignored when they're relevant and occasionally triggered when they're not. The only reliable way to use a skill is to invoke it manually, which makes the "auto-invocation" promise meaningless. You don't have an intelligent tool. You have a slash command with extra steps.

And the chat interface variants are even worse. Custom instructions and project knowledge don't have any invocation mechanism at all. They're either always loaded into context or they're not. You're burning context window on instructions that may or may not be relevant to what you're doing right now, with no way to scope them to specific tasks.

The feature isn't broken in some dramatic way. It just doesn't do what people think it does. And that gap between expectation and reality is driving a lot of misplaced effort.

The Skill Accumulation Problem<br>Here's what happens: someone discovers skills, gets excited, and starts building them for everything. "I keep giving the AI the same instructions for code review" → skill. "It doesn't follow our design system" → skill. "I need it to handle deployments" → skill.

Before long you've got a .claude/skills/ directory with fifteen markdown files, a personal ~/.claude/skills/ with ten more, and you're browsing GitHub repos for skill packs to install. It feels productive. You're customizing your tools. You're building a workflow.

And the dynamic is the same one that gave us left-pad as an npm package: someone solved a problem, packaged it, and now thousands of people are importing it without asking whether they actually have that problem, or whether it's even hard to solve. If the first thing you do when creating a new project is npm install lodash, skill marketplaces are going to feel like home. That's not a compliment.

But step back. You now have twenty-five markdown files that the LLM will almost certainly never invoke at the right time. You have to remember which skills exist and when to use them. You have to maintain them as your codebase changes. And the instructions in those files are doing work that should be happening somewhere else entirely.

Skills are the junk drawer of AI coding tool configuration. They're the path of least resistance for "I want the AI to do X better," so everything gets thrown in there without asking whether a skill is actually the right solution.

The Workflow You're Actually Orchestrating<br>Think about what a skill-heavy workflow looks like in practice:

/understand-project<br>/implement-feature XYZ<br>/security-review XYZ<br>/code-review XYZ<br>/write-tests XYZ<br>/deployYou're the orchestrator. You're deciding what to do next, in what order, invoking the right command at the right time. The LLM does the work inside each step, but you're managing the process.

You've become a human scheduler for an AI worker. You've turned an autonomous agent back into a manual tool. The same failure mode as building a system full of human checkpoints when the agent should be running the process.

My sessions don't look like that. I describe what I want, the feature, the problem, the goal,...