TIL about a large moss green flightless parrot :)

Will Kākāpō be riding bicycles soon?

> Skills are a keeper #

Good thinking, I agree actually, however..

> 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.

Like a lot of posts around AI, and I hope OP can speak to it, surely you can agree that while when used for a good cool idea, it can also be used for the inverse and probably to more detrimental reason. Why would they document an unmanageable feature that may be consumed.

Shareholder value might not go up if they learnt that the major product is learning bad things.

Have you or would you try this on a local LLM instead ?

No I don't believe so. Cursor is usually pretty behind other agentic coding tools in my experience.

VS Code Copilot just announced experimental skill support in their November release: https://code.visualstudio.com/updates/v1_107#_reuse-your-cla...

CLIs are really good when you can use them. self-documenting, agents already have shell tools, they tend to solve fine-grained auth, etc.

feels like the right layer of abstraction for remote APIs

If only there was a way to progressively disclose the API in MCP instead of presenting the full laundry list up front.

Looking for a way to do this with ADK as well, looks like skills can be a sweet spot between giant instruction and sprawling tools/subagents.

Yeah, there are a whole lot of AI wrapper applications that could be a folder with a markdown file in at this point!

Yes. Prompt engineering is like a shittier verson of writing a VBA app inside Excel or Access.

Bloat has a new name and its AI integration. You thought Chrome using GB per tab was bad, wait until you need a whole datacenter to use your coding environment.

Gemini seems to be firmly in the lead now. OpenAI doesn't seem to have the SoTA. This should have bearing on whether or not LLMs have peaked yet.

This might be actually be better in a certain way: if you change a real customer-facing API then customers will complain when you break their code. An LLM will likely adapt. So the interface is more flexible.

But perhaps an LLM could write an adapter that gets cached until something changes?

I think really more than anything it’s become clear that AGI is an illusion. There’s nothing there. It’s the mirage in the desert, you keep waking towards it but it’s always out of reach and unclear if it even exists.

So companies are really trying to deliver value. This is the right pivot. If you gave me an AGI with a 100 IQ, that seems pretty much worthless in today’s world. But domain expertise - that I’ll take.

Literally yesterday we had a post about GPT-5.2, which jumped 30% on ARC-AGI 2, 100% on AIME without tools, and a bunch of other impressive stats. A layman's (mine) reading of those numbers feels like the models continue to improve as fast as they always have. Then today we have people saying every iteration is further from AGI. It really perplexes me is how split-brain HN is on this topic.

Yes, definitely. I've had a lot of success already showing LLMs short examples of coding libraries they don't know about from their core training data.

I think it’s also important to think of skills in the context of tasks, so when you want an agent to perform a specialized task, then this is the context, the resources and scripts it needs to perform the task.

It's a folder with a markdown file in it plus optional additional reference files and executable scripts.

The clever part is that the markdown file has a section in it like this: https://github.com/datasette/skill/blob/a63d8a2ddac9db8225ee...

  ---
  name: datasette-plugins
  description: "Writing Datasette plugins using Python and the pluggy plugin system. Use when Claude needs to: (1) Create a new Datasette plugin, (2) Implement plugin hooks like prepare_connection, register_routes, render_cell, etc., (3) Add custom SQL functions, (4) Create custom output renderers, (5) Add authentication or permissions logic, (6) Extend Datasette's UI with menus, actions, or templates, (7) Package a plugin for distribution on PyPI"
  ---

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.

Yes. I find these very useful for enforcing e.g. skills like debugging, committing code, make prs, responding to pr feedback from ai review agents, etc. without constantly polluting the context window.

So when it's time to commit, make sure you run these checks, write a good commit message, etc.

Debugging is especially useful since AI agents can often go off the rails and go into loops rewriting code - so it's in a skill I can push for "read the log messages. Inserting some more useful debug assertions to isolate the failure. Write some more unit tests that are more specific." Etc.

Looks like they added it to the Gemini CLI public roadmap last week: https://github.com/google-gemini/gemini-cli/issues/11506#eve...

ChatGPT has had a full Linux container system available to it for nearly three years now.

OpenAI keep changing their mind on what to call it. I like the original name, "ChatGPT Code Interpreter", but they've also called it "advanced data analysis" at various points.

Claude added the same feature in September this year: https://simonwillison.net/2025/Sep/9/claude-code-interpreter...

In both ChatGPT and Claude you can say things like "use your Python tool to calculate total mortgage payments over a 30 year period for X and Y" and it will write and execute code to do so - but you can also upload files (including CSVs or even SQLite database files) into that container file system and have them write and execute python code to process those in different ways.

Skills are just folders full of markdown files that are saved in that container when it first boots up.