zjp1997720/wechat-article-search

WeChat Article Search is a small developer tool that lets you look up public articles on WeChat, a hugely popular Chinese social platform, without needing an account or an API key. You give it a keyword, and it searches through WeChat public accounts, which work like public pages or blogs on the platform, using Sogou WeChat Search, a separate search engine that indexes WeChat content, and returns a list of matching articles. Each result comes back as structured data rather than a messy web page: the title, the article's link, a short summary, when it was published, and the name of the account that posted it. There is an optional setting that tries to convert the search engine's redirect links into the real WeChat article links, though this does not always work because WeChat actively blocks automated requests. Another option lets you save the results directly to a file instead of just printing them on screen. Under the hood, the tool is a single Node.js script that fetches search result pages and reads the HTML with a library called cheerio, which is commonly used to pull information out of web pages. It needs Node.js version 18 or newer, plus one extra package installed before first use. There are no other accounts, keys, or paid services required. The project describes itself as a skill meant to be installed into AI coding assistants such as Codex, so an assistant can search WeChat articles on a user's behalf when asked. Because it relies on scraping a search engine that resists automated traffic, results can occasionally come back empty or fail to resolve to direct links, and the README warns against heavy or commercial scraping to avoid getting temporarily blocked. It is licensed under MIT, which allows free use, including commercial use, as long as the license is kept.