Effective AI risk assessment relies on the quality of evaluations. Currently, there are large quality differences, such as in construct validity and annotation, between existing benchmarks. In this work, we propose a quality scorecard for benchmarks designed to make this diversity easier to navigate. The scorecard employs two main components: dimensions, which provide granular scores of an evaluation under that dimension, and classifications, which correspond to concrete use-cases ranging from research to post-deployment. By establishing a common language and objective methods, this framework aims to aid in transparency and raise the baseline quality of benchmarks used across the ecosystem.