OpenAI Codex 插件：自动化投资分析工作流

The OpenAI public equity investing plugin helps investing teams change the way they work. It addresses one of the biggest pain points in public equity research, turning scattered company, market, and web data into a clear actionable investment view inside one streamlined workflow. Here we're going to jump into a post earnings analysis and understand what changed, what matters, and what to do next. The plugin leverages skills for tasks like normalizing financials, updating models, comparing comps, and bringing in key facts for post earnings deep dives like actuals versus estimates, transcript insights, and more.

Now Codex takes the plugin to the next level by bringing in trusted third-party data, leveraging Quarter for earnings calls, Delupa for metrics, S&P for estimates, alongside public web search for market reaction and outside commentary. Instead of scattered inputs, the team gets a structured output they can actually use and trust. The output is a highly detailed interactive dashboard built with the expert judgment of a seasoned analyst.

Covering what changed, what still needs proof, what's priced in, and what action discipline they should follow. It turns quarter data into decision support with quality of print, reoccurring evidence, operating trajectory, and source-linked analysis. It also pressure tests the thesis with durable checks and bull, base, and bear scenarios. So, what would typically be a slow manual research process is streamlined into one place, helping teams move from scattered information to decisions they can review or find and use, thanks to the OpenAI equity investing plugin.