Notes on "Anthropic"

Here’s what I missed in the AI field this week — I was on holiday in Tokyo.

Two new models dropped within about 15 minutes of each other: Claude Opus 4.6 and GPT-5.3-Codex. Amp immediately adopted Opus 4.6 for its smart mode, but GPT-5.3-Codex is only available in their Codex app, not yet via the API. I believe Amp will adopt it for its deep mode once it’s generally available.

Amp is sunsetting the editor extension next month. It hasn’t been officially announced yet, but the team mentioned it in their latest Raising An Agent podcast episode. I use Amp exclusively through the editor extension, so unfortunately I’ll have to switch to the TUI version and get used to it.

Ghostty’s author Mitchell Hashimoto has been busy lately:

• Ghostty’s updated AI usage policy for contributions. More and more open source projects are drowning in AI-generated issues and PRs submitted without human review — the slop. He proposed a new policy for dealing with this trend. It’s not against AI, but makes every AI-generated contribution accountable to a human.
• Vouch, a community trust management system. A tool that puts the policy above into practice. To mitigate the slop burden, open source projects should build a network to identify trustworthy contributors.
• My AI Adoption Journey. Mitchell’s reflections on his AI adoption journey. Most of it resonates with me — and probably with every thoughtful developer.

Agent Skills (via). Anthropic published Agent Skills as an open standard yesterday¹, just a few days after they co-founded the Agentic AI Foundation and donated the MCP (Model Context Protocol) to it². Now, along with the widely adopted AGENTS.md, there are three major agentic AI patterns for managing context and tools.

Among the three, AGENTS.md is the simplest and most straightforward one, which is essentially a dedicated README.md for coding agents. It is usually loaded in the context window when starting a session, providing general instructions to help coding agents know the user and the workspace better.

It originated from OpenAI to unify the chaotic name conventions of agent instruction files, before which we had .cursorrules for Cursor, .github/copilot-instructions.md for GitHub Copilot, GEMINI.md for Gemini CLI, etc. It has been gradually adopted by almost all coding agents, except Claude Code, which still insists on its CLAUDE.md. (There’s an open issue though.)

Agent Skills is another neat practice. Introduced by Anthropic in October 2025³, it is a composable and token-efficient way to provide capabilities to agents. LLMs can call tools, and Agent Skills is just a simple and standardized way to define a set of tools. A skill is a set of domain-specific instruction files, which can be loaded on demand by the agent itself. Besides instructions in Markdown, a skill can also bundle a set of scripts and supplementary resource files, enabling the agent to run deterministic and reproducible tasks.

Amp, my current coding agent choice, just released the support for Agent Skills earlier this month⁴. Along with Agent Skills becoming an open standard, GitHub Copilot and VS Code announced their support for it⁵. Also, Dax, one of OpenCode maintainers, committed to adding support in the upcoming days⁶. Though, the skills folder name convention is still not unified, .claude/skills for Claude Code, .github/skills for GitHub Copilot, and .agents/skills for Amp. I’d like to see the neutral .agents/skills win.

Compared with these two approaches, MCP is way more complex. It uses a server-client architecture and JSON-RPC to communicate, instead of natural language — the native language of LLMs. An MCP server can provide remote tools, resources and pre-built prompts to the MCP client baked in an agent, enhancing the agent’s capabilities. It was introduced by Anthropic at the end of 2024⁷, and after one year of adoption, its limitations like authorization overhead and token inefficiency have started to emerge, not to mention its difficulty to implement and integrate. In fact, the only MCP server that is still catching my eye is Playwright MCP, which simply gives the browser automation superpower to coding agents. Honestly I didn’t manage to find a chance to try out MCP deeply. Opinions here are merely my observations and largely shaped by discussions on it, like Simon Willison’s post.

Personally, I’m already adopting AGENTS.md globally and in my personal projects. Since Agent Skills becomes more and more promising, I’m looking forward to trying it out, diving deeply, and building my own set of skills.