Recent work has substantially accelerated proof search in interactive theorem provers by integrating large language models, for both Lean and Coq.The natural language inference (NLI) counterpart lacks an analogous infrastructure: the behavior of dedicated DTT-based provers such as wani, inside the Japanese NLI system lightblue, is observable today only through verbose textual logs. This opacity blocks ML-acceleration efforts such as Neural Wani that need to know where the search spends its time and why it fails.We present a profiling and visualization tool for wani, implemented as a web-based component of the lightblue development environment, that exposes the proof search through a four-panel dashboard, Search Tree, Flame Graph, Rule Statistics, and Failure Analysis, each making one aspect of prover behavior directly inspectable.The tool provides the observability that ML-acceleration research in NLI currently needs but cannot easily obtain.It is released as open source software and provided as a Docker image.