and earlier Simon Willison argued[1] that Skills are even bigger deal than MCP.
But I do not see as much hype for Skills as it was for MCP - it seems people are in the MCP "inertia" and having no time to shift to Skills.
They are a bigger deal in a sense because they remove the need for all the scaffolding MCPs require.
E.g. I needed Claude to work on transcripts from my Fathom account, so I just had it write a CLI script to download them, and then I had it write a SKILL.md, and didn't have to care about wrapping it up into an MCP.
At a client, I needed a way to test their APIs, so I just told Claude Code to pull out the client code from one of their projects and turn it into a CLI, and then write a SKILL.md. And again, no need to care about wrapping it up into an MCP.
But this seems a lot less remarkable, and there's a lot less room to build big complicated projects and tooling around it, and so, sure, people will talk about it less.
MCP is completely different, I don’t understand why people keep comparing the two. A skill cannot connect to your Slack server.
Skills are more similar to sub-agents, the main difference being context inheritance. Sub-agents enable you to set a different system prompt for those which is super useful.
Most of my skills connect to APIs.
Skills feel like a non-feature to me. It feels more valuable to connect a user to the actual tool and let them familiarize themselves with it (and not need the LLM to find it in the future) rather than having the tool embedded in the LLM platform. I will carve out a very big exception of accessibility here - I love my home device being an egg timer - it's a wonderful egg timer (when it doesn't randomly play music) and I could buy an egg timer but having a hands-free egg timer is actually quite valuable to me while cooking. So I believe there is real value in making these features accessible through the LLM over media that the feature would normally be difficult to use in.
"Known to work" -- if it has a hardcoded code, it will work 100% of the time - that's the point of Skills. If it's just markdown then yes, some sort of probability will be there and it will keep on improving.
Like with MCP, you can provide a deterministic, known-good piece of code to carry out the operation once the LLM decides to use it.
But a skill can evolve from pure Markdown via inlining some shell commands, up to a large application. And if you let it, with Skills the LLM can also inspect the tool, and modify it if it will help you.
All the Skills I use now have evolved bit by bit as I've run into new use-cases and told Claude Code to update the script the skills references or the SKILL.md itself. I can evolve the tooling while I'm using it.
A fun thing: Claude Code will sometimes fail to find the skill the "proper" way, and will then in fact sometimes look for the SKILL.md file with tools, and read the file with tools, showing that it's perfectly capable of doing all the steps.
You could probably "fake" skills pretty well with instructions in CLAUDE.md to use a suitable command to extract the preamble of files in a given directory, and tell it to use that to decide when to read the rest.
It's the fact that it's such a thin layer that is exciting - it means we need increasingly less special logic other than relying on just basic instructions to the model itself.
Working around their many limitations has been the nature of the game since the original GPT-3.
It’s documentation vs researching how to do something.
Similiar to what humans do.
Even if we expect the LLMs to fully resolve the task, it'll heavily rely on I/O and print statements sprinkled across the execution trace to get the job done.
sandbox is not mandatory here. You can execute the skills on your host machine too (with some fidgeting) but it's a good practice and probably for the better to get in to the habit of executing code in an isolated environment for security purposes.
Aren't skills really just a collection of tagged MCP prompts, config resources, and tools, except with more lock-in since only Claude can use it? About that "agent virtual environment" that runs the scripts.. how is it customized, and.. can it just be a container? Aren't you going to need to ship/bundle dependencies for the tools/libraries those skills require/reference, and at that point why are we avoiding MCP-style docker/npx/uvx again?
Other things that jump out are that skills are supposed to be "composable", yet afaik it's still the case that skills may not explicitly reference other skills. Huge limiting factors IMHO compared to MCP servers that can just use boring inheritance and composition with, you know, programming languages, or composition/grouping with namespacing and such at the server layer. It's unclear how we're going to extend skills, require skills, use remote skills, "deploy" reusable skills etc etc, and answering all these questions gets us most of the way back to MCP!
That said, skills do seem like a potentially useful alternate "view" on the same data/code that MCP is covering. If it really catches on, maybe we'll see skill-to-MCP converters for serious users that want to be able do the normal stuff (like scaling out, testing in isolation, doing stuff without being completely attached to the claude engine forever). Until there's interoperability I personally can't see getting interested though
Tell your agent of choice to read the preamble of all the documents in the skills directory, and tell it that when it has a task that matches one of the preambles, it should read the rest of the relevant file for full instructions.
There are far fewer dependencies for skills than for MCP. Even a model that knows nothing about tool use beyond how to run a shell command, and has no support for anything else can figure out skills.
I don't know what you mean regarding explicitly referencing other skills - Claude at least is smart enough that if you reference a skill that isn't even properly registered, it will often start using grep and find to hunt for it to figure out what you meant. I've seen this happen regularly while developing a plugin and having errors in my setup.
This is wrong and an example magical thinking. AI obviously does not mean that you can ship/use software without addressing dependencies? See for example https://github.com/anthropics/skills/blob/main/slack-gif-cre... or worse, the many other skills that just punt on this and assume CLI tools and libraries are already available
The trivial evidence of this, is that if you have an MCP server available, the skill can simply explain to the agent how to use the MCP server, and so even the absolute worst case for skills is parity.
It is after all a collection of instructions and code that any other llm can read and understand and then do a code execution (via tool call / mcp call)
1. Open-Skills: https://github.com/BandarLabs/open-skills
Just tell your non-Claude agent to read your skills directory, and extract the preambles.