What is ground-first?

ground-first is a portable AI skill designed to make chatbots like Claude, GPT, and Gemini check what you actually mean before answering, especially when a question touches something recent, trending, slang based, or specific to a small community. Normal AI models understand almost everything except what is happening right now, so when a prompt leans on something current, they often answer confidently with an outdated or generic interpretation instead of admitting they might be missing context. The creator built this after noticing the same problem repeatedly in their own work: describing an idea using a term tied to something happening that week, and having the AI respond fluently but incorrectly, using an older or more generic meaning of the words. The model was not unintelligent, it simply had no way of knowing what it did not know about recent events, so it filled that gap with the most statistically common meaning rather than the one actually intended. ground-first works as a guardrail step the model runs before answering. It checks whether the prompt depends on something the model might be behind on, and if so, it searches the web and shows a short summary of its interpretation of what you meant, giving you the chance to correct it in a few words before it commits to a full answer. If nothing in the prompt looks time sensitive or niche, it stays out of the way and adds almost no overhead. The README frames this as useful for creators trying to talk about a trend accurately, indie builders describing an app idea in their own words, solo entrepreneurs discussing their specific market, and developers burned by a model assuming the wrong version of a framework. In one worked example, an AI without ground-first responds to a request to ride the demure trend with a generic dictionary explanation about modesty, while with ground-first installed, the model first searches, correctly identifies the specific TikTok trend being referenced, and shows that identification before continuing with relevant advice. The project describes itself as a lightweight, portable skill with zero dependencies that works inside Claude Code, OpenAI's Codex, or in principle any large language model. It is still actively being tested: the README documents an ongoing benchmark across multiple AI vendors, tracking accuracy numbers openly as the tests get harder each round, rather than claiming the skill is finished. The project is released under the MIT license.