Gives Claude Code ML engineering skills for debugging failing training runs, keeping a persistent experiment journal, planning ML pipelines, iterating after failed experiments, and researching ML topics and frameworks.
@leeroo-aiUse when something is failing in ML/AI work — OOM, NaN, divergence, crashes, bad throughput, wrong outputs, dependency conflicts
@leeroo-aiUse when starting, logging, or reviewing ML experiments — maintains a persistent experiment journal with hypotheses, results, and…
@leeroo-aiUse when the user is stuck, needs ranked next steps, or wants alternatives after initial experiments — "I tried X and got Y, what…
@leeroo-aiUse when the user wants an implementation plan, architecture design, or multi-step ML pipeline — "build X", "implement X",…
@leeroo-aiUse when the user wants to understand an ML/AI topic, compare approaches, or survey framework capabilities — "how does X work?",…
@leeroo-aiUse when the user wants to verify code, config, or math before running — or proactively before any expensive training job or…
@leeroo-aiUse when starting any conversation involving ML/AI — establishes how to use Leeroopedia KB tools and workflow skills
@leeroo-aiShips with superml: Give your AI coding agent ML engineering superpowers. Watch how SuperML works in 90 seconds: If SuperML helps…
@leeroo-aiIs this you? Claim your profilePut a plugin at the top of Plugins, from $9
