What problem does it solve? Learners often believe they understand a topic until they try to explain it. This Skill runs a Feynman-style teach-back session where the learner explains a recently studied topic in plain language, and the AI challenges jargon, missing causal steps, and misleading simplifications to expose real gaps in understanding. ## Core Features & Use Cases - Teach-back setup: Invites the learner to explain a topic as if to a curious ten-year-old, without giving a model answer first. - Claim-by-claim challenge: Breaks each explanation into claims, quotes the weakest phrase, and asks the learner to define terms, supply missing steps, or predict counterexamples. - Foundation report: Summarizes actual mistakes (missing definition, missing mechanism, boundary confusion, unsupported assumption), separates repaired from unresolved gaps, and asks one transfer question. - Use Case: After reading about how database indexes work, a learner explains the concept in their own words; the Skill catches that they skipped why a B-tree lookup is logarithmic and probes until they can articulate the mechanism. ## Quick Start Ask the AI to run a Feynman teach-back on a topic you just studied and challenge every gap in your explanation.
