You just spent twenty minutes staring at a page that explains a “Skills Catalog.” You read about “graph executors,” “composable abstractions,” and something about a “workflow engine.” Your brain hurts. You close the tab and whisper: I’m not smart enough for this.
Stop. That’s exactly what they want you to think.
Colors’ Package Skills Catalog launched this week, and the response has been a mix of awe and confusion. One user nailed it: “It took me some time to figure out what the heck it was, given the documentation was either written by AI or a galaxy brain functional programmer.” That’s not a bug report. That’s a feature.
The most powerful tools don’t explain themselves to humans. They explain themselves to machines.
Here’s the uncomfortable truth most people are missing: the real product isn’t the workflow engine. It’s the communication layer. Colors’ Skills Catalog will succeed or fail based on whether its “skills” can be understood and composed by target users — and those target users might not be you.
You’ve probably felt that frustration before. A tool promises to simplify automation, to package complexity into neat little boxes. But when you open the docs, it’s like reading a PhD thesis on category theory. The gap between promise and reality is so wide you can hear the echo.
That’s not an accident. It’s a deliberate design choice.
If you can’t explain it to a colleague, it won’t scale. But if an AI agent can parse it, it will scale infinitely.
Think about who benefits from a perfectly precise, abstract, machine-readable specification. Not a marketing manager. Not a customer support lead. An AI agent. A bot that can ingest that galaxy-brain documentation and translate it into action without ever needing a human hand-holder.
This is the twist: the catalog may be positioning not for human users at all, but for the AI agents that will soon be the primary consumers of API documentation, workflow definitions, and integration catalogs. The poor human readability? That’s a signal that the abstraction layer is optimized for machine composition. It’s a feature, not a bug.
Does that make you uncomfortable? Good. It should.
We’re in a moment where the line between tools for humans and tools for machines is blurring. The next generation of platforms will be designed to be read by algorithms first, and people second. If you’re evaluating AI workflow tools, you need to ask one question above all others: Can I explain this system to a colleague? If the answer is no, the tool’s power doesn’t translate into real organizational leverage. No matter how sophisticated the graph executor is.
The real product isn’t the workflow engine. It’s the communication layer. And if you can’t read it, you’re not the customer.
So what do you do? Stop trying to understand the docs. Start asking who they were written for. If the answer is “an AI agent,” then you’re either building for the same audience — or you’re building the wrong tool.
Choose your side.
FAQ
Q: Isn't this just bad documentation?
A: No, it's intentional. The catalog is designed for machine parsing, not human reading. The real users are AI agents that can consume the abstraction natively.
Q: How do I evaluate if this tool is for me?
A: Ask: Can I explain it to a colleague? If not, the tool's power doesn't translate into organizational leverage. For human teams, clarity is king.
Q: Should we embrace tools that aren't human-friendly?
A: Only if you're building for AI-to-AI workflows. For human collaboration, tools must be understandable. But the future may belong to platforms that speak machine first.