The models are really good at driving those environments now which makes skills the right idea at the right time.
But yes. Other agent platforms will adopt this pattern.
I find it powerful how it can leverage and self-discover the best way to use a CLI and its parameters to achieve its goals
It feels more powerful than providing pre-defined set functions as MCP that will have less flexibility as a CLI
"Skills require the Code Execution Tool beta, which provides the secure environment they need to run."
It is useful in a user-education sense to communicate that it's good to actively document useful procedures like this, and it is likely a performance / utilization boost that the models are tuned or prompt-steered toward discovering this stuff in a conventional location.
But honestly reading about skills mostly feels like reading:
> # LLM provider has adopted a new paradigm: prompts
> What's a prompt?
> You tell the LLM what you'd like to do, and it tries to do it. OR, you could ask the LLM a question and it will answer to the best of its ability.
Obviously I'm missing something.
Maybe I still don't understand the mechanics - this happens "on startup", every time a new conversation starts? Models go through the trouble of doing ls/cat/extraction of descriptions to bring into context? If so it's happening lightning fast and I somehow don't notice.
Why not just include those descriptions within some level of system prompt?
Reading a few dozen files takes on the order of a few ms. They add enough tokens per skill to fit the metadata description, so probably less than 100 for each skill.
> The body can contain any Markdown; it is not injected into context.
It just means it's not injected into the context until the skill is used or it's never injected into the context?
I had thought that once the skill is selected the whole file would be read, but it looks like that's not the case: https://github.com/openai/codex/blob/ad7b9d63c326d5c92049abd...
1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
Reason I ask is because a while back I had similar sections in my CLAUDE.md and it would either acknowledge and not use or just ignore them sometimes. I'm assuming that's more of an issue of too much context and now skill-level files like this will reduce that effect?
Skills are nice because they offload all the detailed prompts to files that the LLM can ask for. It's getting even better with Anthropic's recent switchboard operator (tool search tool) that doesn't clutter the system prompt but tries to cut the tool list down to those the LLM will need.
There's an instruction about that in the Codex CLI skills prompt: https://simonwillison.net/2025/Dec/13/openai-codex-cli/
If SKILL.md points to extra folders such as references/, load only the specific files needed for the request; don't bulk-load everything.can those markdown in the references also in turn tell the model to lazily load more references only if the model deems they are useful?
If you need to write tests that mock
an HTTP endpoint, also go ahead and
read the pytest-mock-httpx.md fileI don’t know what this is and Google isn’t finding anything. Can you clarify?
The clever part is that the markdown file has a section in it like this: https://github.com/datasette/skill/blob/a63d8a2ddac9db8225ee...
On startup Claude Code / Codex CLI etc scan all available skills folders and extract just those descriptions into the context. Then, if you ask them to do something that's covered by a skill, they read the rest of that markdown file on demand before going ahead with the task.