Lately, people in the AI space have started calling neglected agents, automations, and skills orphans. Specifically, they use the term orphaned agents or orphaned skills, and I did not come up with this phrase myself. The security industry already uses it, and it captures exactly how dangerous it has become to neglect your automation tools or agent skills.
So here is a strange question to open with. Are you the parent of a skill? Somewhere in your workflow, something you built and forgot about might already be an orphan.
Picture this. A creator moves to a different department, but the automation tools or skills they built stay right where they left them. Nobody adopts them. They just keep running quietly, burning tokens on nothing or triggering tasks nobody asked for.
I use AI myself for fact checking, research, and grammar checking while writing these pieces. I saved each of those roles as a skill and automated the process so the work kicks off the moment I send the data. Mine are small enough that they never trigger anything unless I personally give them a task, so I am not the one you need to worry about here.
But picture someone running far more advanced agents this way, then leaving the company. Those orphaned agents can keep acting without anyone even noticing. And the real danger that follows is access. You are quietly handing out access rights the whole time, and once the person controlling that agent is gone, all that access just sits there wide open.
Why Skills Turn Into Monsters
I already covered how to save Claude tokens in How I Stopped Running Out Of Claude Tokens, and one of the tips was building skills. If you save your frequently used prompts as a skill in an MD file, this general purpose AI agent can pick up the context instantly and save you a ton of tokens along the way.
Here is why that works. If you cram everything into a single giant file, the AI ends up reading through third party rules and other people's projects just to handle one small task. That makes everything slower, far more expensive, and stuffed with the wrong context. It is the exact same reason I split my own log file into a separate log.md. Once logs keep stacking up in one place, the AI wastes resources rereading months of history every single time you ask it something. The same logic applies just as cleanly to skills, and Anthropic's own documentation says exactly this, recommending you split claude.md into separate skill files specifically to save tokens.
So here is the natural result. The more you optimize your workflow, the more skills you end up creating. It works the same way experience does in a career, the longer you work, the more you can handle at once. So generally speaking, accumulating skills is a solid sign.
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| Solo use looks fine, right up until it doesn't |
But here is where the real problem begins. Using your own skills solo is one thing. The moment multiple people start sharing the skills they built, the story changes completely. If everyone just keeps working off their own personal accounts, every one of those skills turns into an orphan within about six months.
The Chaos Of Duplicated Work
Fast forward six months and here is what you get. Similar skills scattered across every single department, each with a totally different name, and nobody able to say how many duplicates even exist. I have already seen a smaller version of this play out among a few coworkers, each quietly building their own version of the exact same shortcut without knowing the others existed. This is exactly where the chaos begins.
Two things matter here before it gets that far. The first is the name. You should be able to tell exactly what a skill does just by reading its name. Naming something properly is really just setting a rule specific to your team. The second is the purpose. A name alone is not enough, since you also need a clear line stating exactly when to use it. Without both of these, you will never be able to judge later on which ones are safe to delete.
These two elements matter more than they sound like they should, because general purpose AI agents read skills in three distinct stages. They always read the name and the description first. They only read the body once they actually decide to use that skill. And they only pull the attached data when it becomes necessary. That means even with a hundred skills sitting there, the AI is still going to read every single name and one line description no matter what. If that name and description are weak, humans will fail to find it, and the AI will miss it too. It becomes invisible even while sitting right there in plain sight.
So if you are working on a group project, you need to assign someone as the skill master to manage all of this separately. A skill with no clear owner will always end up rotting.
Assets Easily Turn Into Warehouses
Part of why ownership is so hard to enforce right now comes down to a simple platform gap. Claude does technically offer organization-wide skill management now on Team and Enterprise plans, but plenty of smaller teams are still working off personal Pro accounts, so in practice everyone still ends up uploading things individually on their own. If your team is one of them, you have to manually build that shared structure yourself, through a project folder, a workspace, or whatever separate system you can put together, until you make the jump to a plan that handles it natively.
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| Everyone is digging, nobody agreed on where |
One of the most common problems on any team project is that everyone stays locked into their own tasks, which makes organizing and integrating the final work genuinely difficult. Managing skills properly, and adopting general purpose AI agents responsibly, is a conversation worth having seriously. Handled well, the skills piling up in your organization become real, valuable assets. Left alone, that same space just turns into a dusty warehouse. And unused skills never sit there quietly either. They actively get in the way of finding the skills you actually need, and they quietly drain money out of your budget the whole time.
Rules Are The Ultimate Context
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| The forecast is real. The reason behind it is the point |
I came across an interesting prediction from Gartner recently. They estimate that by the end of 2027, more than forty percent of agent projects will get canceled outright. My honest take is that this will not happen because the AI itself fails. It will happen because management fails. It is the same pattern I already broke down in Why Most Apps Fail Before A Single Line Of Code, where the failure was never really about the technology.
If you are just using AI on your own, you honestly do not need rules. But the moment you are working as an organization, the rules become the context itself. This particular topic matters enormously for how development actually plays out, so I plan to keep updating my thinking here as I learn more.
Before you build your next automation, ask yourself who is actually going to take care of it once you are gone.
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