Leeroo-AI/superml

ML Debugging systematically diagnoses ML and AI training failures such as OOMs, NaNs, divergence, crashes, slow throughput, wrong outputs, and dependency conflicts. Use it when an experiment or model run breaks and you need root-cause analysis before applying fixes.

ML Experiment Journal keeps a persistent log of experiment hypotheses, results, and lessons across sessions. Use it before, during, and after ML runs to track what changed and what was learned.

ML Iteration generates grounded, ranked next steps when an ML experiment is stuck or underperforming. Use it after initial runs to compare alternatives, review what was tried, and decide what to change next.

ML Planning turns an ML goal into a grounded, step-by-step implementation plan. Use it when you need an architecture, pipeline, or build plan for a machine learning project.

ML Research helps compare ML and AI approaches using verified framework documentation and model references. Use it when you need to understand how a method works or survey framework capabilities.

ML Verification checks code, configs, and math before training or deployment to catch mistakes early. It is used to validate framework behavior and flag issues before expensive runs.

Using Leeroopedia establishes the lookup-first workflow for ML and AI conversations. It tells the assistant to use Leeroopedia or web sources, cite claims, and check for config or API mistakes before answering.