本文报道了 OpenAI 悄然在 ChatGPT 的代码解释器和 Codex CLI 中引入“技能”机制，与 Anthropic 早期的实现方式类似。“技能”允许大型语言模型 (LLM) 与外部工具和资源交互，从本质上为它们提供了新的功能。在 ChatGPT 中，用户可以访问一个包含用于处理电子表格、DOCX 和 PDF 文件的技能的 `/home/oai/skills` 文件夹。值得注意的是，PDF 技能将文档转换为 PNG 图像，供视觉增强型 GPT 模型进行视觉处理，从而保留布局信息。Codex CLI 也支持技能，可以从 Git 仓库本地安装。作者展示了两种实现方式，展示了 ChatGPT 生成详细的 PDF 报告，以及 Codex CLI 创建 Datasette 插件，突出了这些技能在扩展大型语言模型能力方面的灵活性和强大功能。OpenAI 这样的大型参与者采用这种轻量级技能规范，突显了其作为人工智能交互方面重大进步的潜力。
12th December 2025
One of the things that most excited me about Anthropic’s new Skills mechanism back in October is how easy it looked for other platforms to implement. A skill is just a folder with a Markdown file and some optional extra resources and scripts, so any LLM tool with the ability to navigate and read from a filesystem should be capable of using them. It turns out OpenAI are doing exactly that, with skills support quietly showing up in both their Codex CLI tool and now also in ChatGPT itself.
Skills in ChatGPT
I learned about this from Elias Judin this morning. It turns out the Code Interpreter feature of ChatGPT now has a new /home/oai/skills folder which you can access simply by prompting:
Create a zip file of /home/oai/skills
I tried that myself and got back this zip file. Here’s a UI for exploring its content (more about that tool).
So far they cover spreadsheets, docx and PDFs. Interestingly their chosen approach for PDFs and documents is to convert them to rendered per-page PNGs and then pass those through their vision-enabled GPT models, presumably to maintain information from layout and graphics that would be lost if they just ran text extraction.
Elias shared copies in a GitHub repo. They look very similar to Anthropic’s implementation of the same kind of idea, currently published in their anthropics/skills repository.
I tried it out by prompting:
Create a PDF with a summary of the rimu tree situation right now and what it means for kakapo breeding season
Sure enough, GPT-5.2 Thinking started with:
Reading skill.md for PDF creation guidelines
Then:
Searching rimu mast and Kākāpō 2025 breeding status
It took just over eleven minutes to produce this PDF, which was long enough that I had Claude Code for web build me a custom PDF viewing tool while I waited.
Here’s ChatGPT’s PDF in that tool.
(I am very excited about Kākāpō breeding season this year.)
The reason it took so long is that it was fastidious about looking at and tweaking its own work. I appreciated that at one point it tried rendering the PDF and noticed that the macrons in kākāpō were not supported by the chosen font, so it switched to something else:
Skills in Codex CLI
Meanwhile, two weeks ago OpenAI’s open source Codex CLI tool landed a PR titled feat: experimental support for skills.md. The most recent docs for that are in docs/skills.md.
The documentation suggests that any folder in ~/.codex/skills will be treated as a skill.
I used Claude Opus 4.5’s skill authoring skill to create this skill for creating Datasette plugins, then installed it into my Codex CLI skills folder like this:
git clone https://github.com/datasette/skill \
~/.codex/skills/datasette-plugin
You have to run Codex with the --enable skills option. I ran this:
cd /tmp
mkdir datasette-cowsay
cd datasette-cowsay
codex --enable skills -m gpt-5.2
Then prompted:
list skills
And Codex replied:
- datasette-plugins — Writing Datasette plugins using Python + pluggy (file: /Users/simon/.codex/skills/datasette-plugin/SKILL.md)
- Discovery — How to find/identify available skills (no SKILL.md path provided in the list)
Then I said:
Write a Datasette plugin in this folder adding a /-/cowsay?text=hello page that displays a pre with cowsay from PyPI saying that text
It worked perfectly! Here’s the plugin code it wrote and here’s a copy of the full Codex CLI transcript, generated with my terminal-to-html tool.
You can try that out yourself if you have uvx installed like this:
uvx --with https://github.com/simonw/datasette-cowsay/archive/refs/heads/main.zip \
datasette
Then visit:
http://127.0.0.1:8001/-/cowsay?text=This+is+pretty+fun
Skills are a keeper
When I first wrote about skills in October I said Claude Skills are awesome, maybe a bigger deal than MCP. The fact that it’s just turned December and OpenAI have already leaned into them in a big way reinforces to me that I called that one correctly.
Skills are based on a very light specification, if you could even call it that, but I still think it would be good for these to be formally documented somewhere. This could be a good initiative for the new Agentic AI Foundation (previously) to take on.
