wtf is understand-anything?

Understand Anything is a plugin for Claude Code that helps a person make sense of a large, unfamiliar codebase by turning it into an interactive visual map instead of expecting them to read through the raw source files. The problem it addresses is a common one: joining a project with hundreds of thousands of lines of code and not knowing where to even begin reading. When a person runs its analysis command, a pipeline made up of multiple AI agents scans the project and identifies every file, function, class, and dependency, then assembles all of that into a knowledge graph, essentially a network diagram showing how the different pieces of the codebase connect to one another. That graph gets saved locally and is presented through an interactive dashboard where each piece of code becomes a clickable node. Clicking a node shows a plain English summary of what it does along with its relationships to other parts of the code. There is also a separate domain view that maps the code to real business processes rather than just technical structure. Beyond basic exploration, the tool offers guided tours that walk a new team member through the architecture in a sensible dependency order, search that works both by exact name and by meaning, so a person could search for something like which parts handle login, a diff impact feature that shows which parts of the system a pending code change would affect before it is committed, and an interface that adjusts how much technical detail it shows depending on whether the viewer is a junior developer, a product manager, or an experienced engineer. It also groups code automatically into architectural layers like API, service, data, and interface, and explains common programming concepts like closures or decorators wherever they appear in the code. The tool works with several AI coding assistants beyond Claude Code, including Codex, Cursor, GitHub Copilot, and Gemini CLI, and it can also analyze knowledge base wikis, not just source code. It supports generating its output in multiple languages including Chinese, Japanese, Korean, and Russian. Analyzing a whole codebase for the first time can use a significant number of AI tokens, though later runs only re-analyze files that changed. It is released under the MIT license.