The Great Agent Skills Land Grab — Den Odell

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The Great Agent Skills Land Grab

10 June 2026 8 min<br>Thousands of AI agent skills have flooded GitHub recently, most of them teaching models what they already know. The land grab is on.<br>by Den Odell

Den is the author of three technical books, and a Staff<br>Engineer at Canva.

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Like many developers, I now lean on AI as a big part of my day-to-day tooling. With appropriate steering, I can have any number of coding agents working with me to accelerate my engineering work. For tasks where more specialized knowledge is needed, I can load in an agent skill to do something the AI model can’t reliably do on its own.

That’s the theory, anyway.

I installed a popular collection recently. A handful of skills about good engineering practice, well-organized, published by a developer I respect.

I loaded them into Claude Code and ran through some tasks I’d normally do by hand. The output was decent.

Then, as an experiment, I removed all the skills and ran the same tasks again. The output was identical.

That’s when I started looking more carefully at what was actually inside some of these repos. What I found there made me rethink why some of them even exist. My conclusion: creating agent skills collections has become a land grab.

A land grab is when new territory opens up, everyone rushes to claim as much of it as possible, and nobody stops to check whether what they’re claiming is actually worth anything to anybody.

An agent skill, as Anthropic designed the format, is a folder containing a SKILL.md file with instructions, optional bundled scripts the agent can execute, and optional reference material loaded only when needed. It’s a sound idea, and it gives an agent enough information to understand when to apply a skill without having to load the entire skill into context.

What’s appeared on GitHub in the past few months since the format was defined is the land grab in action. Thousands of skills, most of them instructions only, with no bundled code and no platform-specific reference material. Well-known developers and organizations are publishing collections of 50, 100, 200+ skills. Platform companies are shipping marketplaces and leaderboards. An “awesome-agent-skills” list is tracking the whole thing, covering everything from Solana development to AI fitness coaching. The developers with the biggest followings are staking the biggest claims, and their followers are installing whatever they publish.

But look at some of the code in these repos and you notice something: many of them look to have been written by the same models they’re supposed to be teaching. And, of course, that’s what makes the land grab possible. Nobody can realistically publish 200 skills in a weekend by writing them by hand.

Which leads to a simple test for any instructions-only agent skill: could an LLM have written it? If yes, the skill is almost certainly useless, because the knowledge it contains is already in the model’s training data. Loading it into context just spends tokens telling the model something it already knows. That test disqualifies a lot of what’s being published right now.

Open any popular general-purpose skills repo and try the test. You’ll find advice like “write tests before code,” “use semantic HTML,” “measure before optimizing,” and “code-split your bundles.” This is good engineering guidance. It’s also information that LLMs have already absorbed from thousands of blog posts and books. A skill repeating this knowledge adds nothing, and it takes up space that could hold information the model actually needs. If you can generate a skill with a prompt, you don’t need the skill.

The tells are right there in the first few commits: the same structure across dozens of files and a kind of polished completeness with no rough edges. When an LLM writes a skill that gets loaded into another LLM’s context, nothing new enters the system. It’s a closed loop, and handing the output back as context is a no-op.

One developer made the generation loop visible by accident. The first commit of a large and widely starred skills repository contains a CLAUDE.md file: inside, a set of instructions telling Claude Code to generate eight categories of skills across a twelve-week roadmap, complete with projected ROI numbers. The file’s own instructions say to exclude itself from version control, but the commit went through before the .gitignore file was in place; that was added in the second commit. The generation prompt was supposed to be hidden. It wasn’t. I’ll leave the repo unnamed here, since the point isn’t to name-and-shame anyone. The pattern is common enough...