what is web-fetch fr?

AI coding assistants like Claude Code can read web pages for you, but they typically load the entire page contents directly into their working memory. That wastes context on irrelevant material and makes it harder for the assistant to find the specific data you actually need. web-fetch solves this by downloading a web page to a temporary file and handing the assistant a file path instead. The assistant can then run quick search commands to extract only the exact lines or data points it needs, keeping the conversation lean and focused. The tool runs as a single Python script with no external dependencies. You give it a URL, it fetches the page, optionally converts HTML into cleaner markdown or plain text, and saves the results to a temporary folder. It prints out the file paths so an agent can pipe them into standard search tools. It includes basic handling for sites protected by Cloudflare and can automatically retry with a more realistic browser identity, but it is deliberately limited: no JavaScript rendering, no authentication, no caching, and a 5 MB cap on response size. This is built for people working with terminal-based AI agents like Claude Code, OpenCode, or pi. A practical example: you ask your agent to look at a long API documentation page and find the specific parameters for one endpoint. Instead of pasting the entire page into context, the agent uses this skill to save the page to a file, searches that file for the relevant section, and reads only those lines into context. It also works as a standalone command-line script for anyone who just wants to fetch and convert web pages to markdown. The project's core philosophy is intentional simplicity. It handles one straightforward task, fetching public web content into a file, and explicitly avoids features like multi-provider fallback chains or JavaScript rendering. The author argues that if you need complex browser automation or provider routing, those belong in a different tool entirely. This keeps the script lightweight, easy to install, and reliable across macOS, Linux, and Windows.