WebGrader is a self-evolving programmatic grader for training large language models (LLMs) in web development. Traditional evaluation methods require manually authored browser scripts, which are executable but costly to write for open-ended requirements. WebGrader automatically derives the required interaction flows from each website request, represents each flow as an executable Flow Contract, and uses its execution outcome as an RL reward. WebGrader materializes the generated project in a live browser, grounds target actions against the source code and live DOM, and collects visual, DOM, response, and persistent-state evidence along the same browser trajectory. A residual-driven offline loop then discovers reusable verifier skills, screens them on disjoint validation pages, and freezes the promoted skill graph before policy training. By separating test planning, action grounding, evidence collection, and semantic judgment, WebGrader issues a Pass verdict only after observing the requested transition. On WebGen-Bench, WebGrader trains an 8B policy to a 52.01% functional success rate, outperforming a matched appearance-plus-script reward by 7.88 points and surpassing o4-mini and DeepSeek-v4-flash. On WG-core-250, the policy reaches a Full Score of 44.953 and surpasses Qwen3-Coder-480B. Blogger's Review: WebGrader is an innovative solution that efficiently trains LLMs for web development. By using a self-evolving programmatic grader, WebGrader automates the evaluation of LLM-generated websites, reducing human costs and increasing evaluation efficiency. This approach achieves a higher success rate and full score than existing methods, demonstrating its huge potential in the field of web development.