这个判断正在成为现实。2026年2月12日，一日内在互联网Infra领域发生了几件事：Cloudflare 发布了Markdown for Agents，为Agent解决Web内容读取问题；Google Chrome 发布 WebMCP，支持Agent 跳过人类 UI，直连网站内核；Coinbase发布了Agentic Wallets，专为 AI Agent 设计的自主加密钱包；这些Infra都是为Agent原生适配，标志着Agent不再是“伪装用户”，而是Web一等公民。
The outcome component is an score with at initialization, weighting recall sixteen times more than precision. This bias reflects Context-1's role as a retrieval subagent feeding a downstream answering model: missing a critical document is often worse than including an irrelevant one, since the downstream model can still filter but cannot recover information that was never retrieved.
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The peculiar reality is that we already know this. We've always known this. Every physics textbook includes chapter exercises, and every physics instructor has declared: you cannot learn physics through observation. You must employ writing instruments. You must attempt problems. You must err, contemplate errors, and identify reasoning failures. Reading solution manuals and agreement creates understanding illusions. It doesn't constitute understanding. Every student who has skimmed problem sets through solutions then failed examinations knows this intuitively. We possess centuries of accumulated educational wisdom confirming that attempts, including failed attempts, represent where learning occurs. Yet somehow, regarding AI systems, we've collectively decided that perhaps this time differs. That perhaps approving automated outputs substitutes for personal computations. It doesn't. We knew this before LLMs existed. We apparently forgot the moment they became convenient.
简而言之，技能是智能代理的能力模块，包含提示词与脚本组合，可实现特定功能（如SEO内容创作、PPT生成、周报撰写等）。
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