Been exploring graphify recently and I think the concept is pretty interesting. It’s an agent skill that reads a folder of source material, builds a knowledge graph from it, and then gives you a structured view of the concepts, relationships, communities, and key nodes across the whole set. It gives your agent a reference for a big codebase which stops it from grepping all over and getting lost. What's nice too is there are a lot of optional expansions for non-code use cases as well. What I like about it: - good for large, messy collections of material - good for mixed inputs like code, markdown, PDFs, screenshots, and diagrams - good for finding structure across a repo or research dump - good for persistent memory, since it saves the graph and lets you query it later - good for agent workflows, especially when you want something more navigable What I’d avoid using it for: - small codebases where you can already understand everything quickly - situations where you need perfect factual reliability on every relationship - simple “read one file and answer one question” jobs - cases where raw source reading is already cheap enough and the graph layer adds overhead That’s the part I find most interesting: it feels strongest when the problem is too much material + weak structure. If you want to check it out head over to: https://github.com/safishamsi/graphify Curious what other tools anyone else here has tried in this category yet?