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Vercel 与 OpenAI 等多家公司联合推出 Agent Plugins 开放标准 Agent Plugins 是一个跨平台、中立的AI Agent插件标准，旨在解决 Agent Skills和 MCP 服务器在不同客户端间格式不统一、难以复用的问题。 规范非常轻量，插件根目录下只需包含一个包含基本元数据的 plugin.json 文件，并使用固定目录存放组件（如存放 Skill 的 skills/ 目录和配置 MCP 的 mcp.json） 由 Vercel 发起，并与 AWS、Anysphere、GitHub、Microsoft 及 OpenAI 等公司联合制定。支持包括 ChatGPT、Codex、Cursor、GitHub Copilot、Kiro 以及 VS Code 在内的主流平台与工具。 没有Anthropic参与，首发也不支持Claude Code。Skill和MCP标准都是Anthropic推出的，这些公司联合有对抗Anthropic的意思。 详细介绍：

Agent Plugins 是由 OpenAI、AWS、Cursor、GitHub、VS Code 和 Vercel 等厂商共同推动的开放标准，把 Agent Skills 和 MCP 服务器配置打包成一次构建、多个 AI Agent 客户端间通用的可移植插件。

Build a plugin once and use it across compatible agent clients. Introducing Agent Plugins, an open standard developed with @awsdevelopers, @cursor_ai, @github, @code, and @vercel that packages Agent Skills and supports MCP server configurations in a shared format.

自从 @ewind_dev 使用钞能力飞出了 PocketJS 并且蹬上不同硬件，我就在想能不能把 agent 也跑在这些设备上。🚴 最新的结果就是在 ESP32 上能运行的满血 pi harness！ 这个 pi core harness 可以正常对话、 调用 tools、管理自己的 workspace，还能设置 schedule，通过自己的 agent loop 再次醒来，并且也自带 UI！ Agent 后续可以作为整个系统的一等公民，自己创造迭代各种 plugins/apps 无痛跑在任何 PocketJS 支持的硬件上。感觉有超多好玩的应用场景可以蹬了！

mattpocock/skills v1.2 is out! We're now the 19th most-starred repo of all time. 13.5m downloads on skills​.sh. Thanks for your support! Here's what's new: - Docs: the community's biggest ask. Every skill documented, with explanations of the main flows + troubleshooting - Claude Plugin: install via Claude's official marketplace - Codex Support: full Codex support via agents/openai.yaml files Updated Skills: - /grilling now asks you questions in rounds, not one-by-one - /prototype now uses HTML instead of a TUI for building logic prototypes - easier to share and far richer - /writing-for-agents renamed from /writing-great-skills, use it for ANYTHING your agents read (AGENTS.md, system prompts, docs) New Skills: - /wizard: tired of provisioning infra? Get your agent to build you a TUI to walk you through it - /to-questionnaire: hit a grilling question you can't answer? Turn the session into a doc you can walk through on a call with a colleague - /wait-what: no idea what the model said? Refocus it in your domain language and simplify with ASD-STE100 Full changelog + docs below. Video soon!

卧槽，这哥们把Codex从零到一的完整路径全给摊开了。 Annotate、Fork、Archive、Plan、Plugin、Skill……一套组合拳。 说真的，你少刷10分钟短视频，把这套东西啃明白。 明天起来可能就多了个能搞钱的手艺。 最骚的是，人家真不卖课。 纯纯白给，手把手教你玩。 这哪是教程，这是赛博菩萨吧。

如果您在做 Agent Harness 相关的开源项目，希望在 DeepSeek Harness 发布的第一时间进行接入支持，请回复您的 GitHub id 以及 GitHub 项目地址，包括但不限于 plugin, skill, MCP, orchestrator, aggregator, UI 等等。 我们会选择一些开源项目的作者邀请参与DSH内测，并赠送部分API额度，让您可以做到在DSH发布时第一时间接入支持。 请同时私信我您的邮箱，我们会使用 email 联系您。

RAG system that skips HTML parsing entirely! PixelRAG is an open-source visual RAG framework that renders documents as screenshots instead of parsing them into text. Most RAG pipelines start by converting HTML to text. Tables flatten into unstructured rows. Charts disappear. Layout context is gone before the LLM ever sees it. The paper measured this directly: HTML-to-text conversion accounts for 36.6% of retrieval failures on SimpleQA. PixelRAG skips that step entirely. It renders pages as screenshot tiles using Playwright, embeds those tiles with a fine-tuned Qwen3-VL-Embedding model, builds a FAISS index, and passes retrieved images directly to a VLM reader. No text abstraction in between. Benchmarked across six datasets against the strongest text-based baselines: - SimpleQA: 78.8% vs 71.6% (+7.1 points) - NQ-Tables: 48.8% vs 42.5% (+6.3 points) - EVQA: +15.5 points - LiveVQA: +11.3 points One honest caveat from the authors: this requires Qwen3-VL-4B class models or larger to see the benefit. Smaller models trail text retrieval. The authors also recommend using PixelRAG as an enhancement layer alongside existing text systems rather than a full replacement. Ships with a pre-built Wikipedia index covering 8.28M articles across 28.1M screenshot tiles. A Claude Code plugin lets Claude take screenshots of any URL and reason over the visual content directly. Key capabilities: • Renders web pages, PDFs, and images as screenshot tiles via Playwright • Fine-tuned Qwen3-VL-Embedding model for visual retrieval • FAISS index for fast vector search • Pre-built Wikipedia index: 8.28M articles, 28.1M tiles • 3x token cost reduction via image compression • Claude Code plugin for direct URL screenshot and visual reasoning • LoRA fine-tuning support via pixelrag-train 100% open source. I've shared the link in the replies!

grok plugin marketplace add 7etsuo/write-legible-c grok plugin install write-legible-c --trust Grok Build with Grok 4.5 is producing the most beautiful C I have ever seen come out of a model. Every function under 25 lines with one job. Nesting never past depth 2. Zero naked literals. Every failure greps to a single producing line and the happy path runs straight down the left margin. Code you can read at 2am with the pager going off. It took a standard to get there. I shipped it as a Grok Build plugin. write-legible-c gates Grok with strict C11 machine legibility every time it touches a .c or .h file. Every function is an orchestrator, a leaf, or an adapter, never a mix. A TRY macro that makes the forgotten check bug impossible to hide. An 18 item checklist the model runs before it shows you code. Clean code is a performance feature too: rewrites that cut model perceived complexity lift LLM task performance by up to 21 percent. Full 18 section standard inside.