Agent Skills let you extend Codex with task-specific capabilities. A skill packages instructions, resources, and optional scripts so Codex can perform a specific workflow reliably. You can share skills across teams or the community, and they build on the open Agent Skills standard.
Skills are available in both the Codex CLI and IDE extensions.
Are we sure that unrestricted free-form Markdown content is the best configuration format for this kind of thing? I know there is a YAML frontmatter component to this, but doesn't the free-form nature of the "body" part of these configuration files lead to an inevitably unverifiable process?
I would like my agents to be inherently evaluable, and free-text instructions do not lend themselves easily to systematic evaluation.
It's so nice that skills are becoming a standard, they are imo a much bigger deal long-term than e.g. MCP.
Easy to author (at its most basic, just a markdown file), context efficient by default (only preloads yaml front-matter, can lazy load more markdown files as needed), can piggyback on top of existing tooling (for instance, instead of the GitHub MCP, you just make a skill describing how to use the `gh` cli).
Compared to purpose-tuned system prompts they don't require a purpose-specific agent, and they also compose (the agent can load multiple skills that make sense for a given task).
Part of the effectiveness of this, is that AI models are heavy enough, that running a sandbox vm for them on the side is likely irrelevant cost-wise, so now the major chat ui providers all give the model such a sandboxed environment - which means skills can also contain python scripts and/or js scripts - again, much simpler, more straightforward, and flexible than e.g. requiring the target to expose remote MCPs.
Finally, you can use a skill to tell your model how to properly approach using your MCP server - which previously often required either long prompting, or a purpose-specific system prompt, with the cons I've already described.
one thing that I am missing from the specification is a way to inject specific variables into the skills. If I create let's say a postgres-skill, then I can either (1) provide the password on every skill execution or (2) hardcode the password into my script. To make this really useful there needs to be some kind of secret storage that the agent can read/write. This would also allow me as a programmer to sell the skills that I create more easily to customers.
I already was doing something similar on a regular basis.
I have many "folders"... each with a README.md, a scripts folder, and an optional GUIDE.md.
Whenever I arrive at some code that I know can be reused easily (for example: clerk.dev integration hat spans frontend and backend both), I used to create a "folder" of the same.
I think Skills could turn into something like open source libraries: standardized solutions to common problems, often written by experts.
Imagine having Skills available that implements authentication systems, multi-tenancy, etc.. in your codebase without having to know all the details about how to do this securely and correctly. This would probably boost code quality a lot and prevent insecure/buggy vibe coded products.
I wonder if generated skills could be useful to codify the outcome of long sessions where the agent has tried a bunch of things and then finally settled on a solution based on a mixture of test failures and user feedback
Something that’s under-emphasized and vital to understand about Skills is that, by the spec, there’s no RAG on the content of Skill code or markdown - the names and descriptions in every skill’s front-matter are included verbatim in your prompt, and that’s all that’s used to choose a skill.
So if you have subtle logic in a Skill that’s not mentioned in a description, or you use the skill body to describe use-cases not obvious from the front-matter, it may never be discovered or used.
Additionally, skill descriptions are all essentially prompt injections, whether relevant/vector-adjacent to your current task or not; if they nudge towards a certain tone, that may apply to your general experience with the LLM. And, of course, they add to your input tokens on every agentic turn. (This feature was proudly brought to you by Big Token.) So be thoughtful about what you load in what context.
Ah, yes, simple text files that describe concepts, and that may contain references to other concepts, or references to dive in deeper. We could even call these something like a link. And they form a sort of... web, maybe ?
Close enough, welcome back index.htm, can't wait to see the first ads being served in my skills
Obviously they are empowering Codex and Claude etc, and many will be open source or free.
But for those who have commercial resources or tools to add to the skills choice, is there documentation for doing that smoothly, or a pathway to it?
I can see at least a couple of ways it might be done - skills requiring API keys or or other authentication approaches, but this adds friction to an otherwise smooth skill integration process.
Having instead a transparent commission on usage sent to registered skill suppliers would be much cleaner but I'm not confident that would be offered fairly, and I've seen no guidance yet on plans in that regard.
Recently there was a submission (https://news.ycombinator.com/item?id=45840088) breaking down how agents are basically just a loop of querying a LLM, sometimes receiving a specially-formatted (using JSON in the example) "request to use a tool", and having the main program detect, interpret and execute those requests.
What do "skills" look like, generically, in this framework?
People are really misunderstanding Skills, in my opinion. It's not really about the .md file. It's about the bundling of code and instructions. Skills assume a code execution environment.
I don't understand how skills are different than just instructing your model to read all the front-matters from a given folder on your filesystem and then decide if they need to read the file body.
34 comments
[ 3.2 ms ] story [ 50.1 ms ] threadSkills are available in both the Codex CLI and IDE extensions.
Anthropic: https://www.anthropic.com/engineering/equipping-agents-for-t...
Copilot: https://github.blog/changelog/2025-12-18-github-copilot-now-...
Easy to author (at its most basic, just a markdown file), context efficient by default (only preloads yaml front-matter, can lazy load more markdown files as needed), can piggyback on top of existing tooling (for instance, instead of the GitHub MCP, you just make a skill describing how to use the `gh` cli).
Compared to purpose-tuned system prompts they don't require a purpose-specific agent, and they also compose (the agent can load multiple skills that make sense for a given task).
Part of the effectiveness of this, is that AI models are heavy enough, that running a sandbox vm for them on the side is likely irrelevant cost-wise, so now the major chat ui providers all give the model such a sandboxed environment - which means skills can also contain python scripts and/or js scripts - again, much simpler, more straightforward, and flexible than e.g. requiring the target to expose remote MCPs.
Finally, you can use a skill to tell your model how to properly approach using your MCP server - which previously often required either long prompting, or a purpose-specific system prompt, with the cons I've already described.
I have many "folders"... each with a README.md, a scripts folder, and an optional GUIDE.md.
Whenever I arrive at some code that I know can be reused easily (for example: clerk.dev integration hat spans frontend and backend both), I used to create a "folder" of the same.
When needed, I used to just copy-paste all the folder content using my https://www.npmjs.com/package/merge-to-md package.
This has worked flawlessly well for me uptil now.
Glad we are bringing such capability natively into these coding agents.
It’s also interesting to see how instead of a plan mode like CC, Codex is implementing planning as a skill.
As of this week, this also applies to Hacker News.
Imagine having Skills available that implements authentication systems, multi-tenancy, etc.. in your codebase without having to know all the details about how to do this securely and correctly. This would probably boost code quality a lot and prevent insecure/buggy vibe coded products.
So if you have subtle logic in a Skill that’s not mentioned in a description, or you use the skill body to describe use-cases not obvious from the front-matter, it may never be discovered or used.
Additionally, skill descriptions are all essentially prompt injections, whether relevant/vector-adjacent to your current task or not; if they nudge towards a certain tone, that may apply to your general experience with the LLM. And, of course, they add to your input tokens on every agentic turn. (This feature was proudly brought to you by Big Token.) So be thoughtful about what you load in what context.
See e.g. https://github.com/openai/codex/blob/a6974087e5c04fc711af68f...
Close enough, welcome back index.htm, can't wait to see the first ads being served in my skills
Obviously they are empowering Codex and Claude etc, and many will be open source or free.
But for those who have commercial resources or tools to add to the skills choice, is there documentation for doing that smoothly, or a pathway to it?
I can see at least a couple of ways it might be done - skills requiring API keys or or other authentication approaches, but this adds friction to an otherwise smooth skill integration process.
Having instead a transparent commission on usage sent to registered skill suppliers would be much cleaner but I'm not confident that would be offered fairly, and I've seen no guidance yet on plans in that regard.
What do "skills" look like, generically, in this framework?