Measuring CAPTCHA Throughput Before a Large Run Google reCAPTCHA v2 is among the most widespread challenges on the web, from the familiar checkbox to invisible and callback variants. CapSkip solves all of these on your own machine in seconds, so your automation does not grind to a halt whenever one appears. Because it mirrors common solver APIs, wiring it in is painless. GeeTest puzzles are famously tricky for bots, which is why running a solver that covers them helps a lot. CapSkip solves GeeTest locally, so workflows that rely on those sites keep running whenever the challenge appears. Python projects have a simple path with CapSkip, since it emulates the request format of major solving services. In practice, that means aiming existing code at CapSkip with little changes - no rewrite. Image CAPTCHAs remain everywhere, on login forms to checkout screens. CapSkip solves a huge range of image CAPTCHA types on your own hardware, typically in about a tenth of a second. That kind of speed matters when you handle large numbers of challenges. Uptime improves when the solver lives on your own hardware. You have zero dependence on a remote service that could slow down or go down at the worst time. CapSkip hands you that control out of the box. Turnstile runs lightweight checks which are meant to tell apart humans from automation without the usual puzzles. Getting past them dependably calls for a purpose-built solver, and CapSkip covers Turnstile locally. Turnstile is now a frequent barrier on sites that aim to deter bots and skip traditional image puzzles. CapSkip clears Turnstile locally within seconds, handling the challenge and managed modes. For automation that run into Turnstile, this removes a major roadblock. Proxy support are often necessary for real scraping, and CapSkip plays nicely with proxies without fuss.