[n8n] Add a troubleshooting atlas for AI debugging

one thing i keep seeing in n8n workflows is that the hardest part is often not writing the flow. it is debugging the wrong part of the flow first. especially once a workflow gets bigger, the first debug move often goes to the symptom instead of the broken layer. people tweak prompts, retry nodes, add extra guards, patch around outputs, or rewrite the wrong step, and the workflow gets noisier instead of cleaner. that is the problem i have been trying to solve. i built **Problem Map 3.0**, a troubleshooting atlas for the first cut in AI debugging. in an n8n context, the idea is simple: **route first, repair second.** this is not a full repair engine, and i am not claiming full root-cause closure. it is a routing layer first. the goal is to reduce wrong-path debugging when a complex workflow fails. for n8n specifically, i think this is most useful when failures are ambiguous across multiple layers, for example: * the LLM output looks wrong, but the real issue is earlier context construction * the HTTP / API node looks broken, but the actual problem is upstream schema drift * retries keep firing, but the first wrong assumption was made before the failing node * people patch the final response formatting, while the real issue is in retrieval, branching, memory, or tool selection this work also grows out of my earlier **RAG 16 problem checklist** line. that earlier work turned out to be useful enough to get referenced in open-source and research contexts, so this is basically the next step for me: pushing the same failure-classification idea into broader AI debugging and workflow debugging. the nice part is that this version is lightweight. it is TXT-based, does not need installation, and can be tested quickly. the repo also has demos, so people can inspect the structure before deciding whether it is useful. i also ran a conservative Claude before / after directional check on the routing idea. it is not a formal benchmark, and i do not want to oversell it. but i still think it is worth looking at, because the pattern is pretty intuitive: if the first debug cut is more structured, the workflow tends to lose less time on wasted fix attempts and wrong-path trial and error. [not a real benchmark, reference only , you can re-produce the same, I put everything in comment](https://preview.redd.it/bfu32q6qy5pg1.png?width=1443&format=png&auto=webp&s=802e38c51e592437fb02067b3aa7b9055cf06543) i think this first version is strong enough to be useful, but still early enough that community stress testing can make it much better. that is honestly why i am posting it here. i want to know whether this actually helps in real n8n scenarios: does it reduce debug time, reduce patch stacking, or help identify the right failing layer earlier? and just as important: where does it overfit, where does it still miss the first cut, and what kinds of workflow failures should be added next? if anyone wants to try it and break it, i would really appreciate that. if it fails on your workflow, i want to know. repo: [https://github.com/onestardao/WFGY/blob/main/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/wfgy-ai-problem-map-troubleshooting-atlas.md)