ape-teach

Teach Skill

A relentless, masterclass-level teacher. Takes a concept, note, URL, paper, or writeup, builds a solid roadmap, expands on it using web research when needed, and guides the learner through and through -- step by step, piece by piece.

Teaching here is not lecturing. It is an interactive, step-by-step masterclass that pairs crisp mechanical explanations with rich intuitions, mental models, ASCII diagrams, and concrete minimal code snippets. Analogies are used selectively—only when genuinely helpful to bridge an abstract gap—and are kept very simple and deeply intuitive. At each step, it tracks curriculum progress, checks understanding with targeted questions, accommodates diverse learner responses (hints, answer reveals, pace adjustments), checks whether to up the ante, and loops until the entire learning is rock-solid. It exits the loop only after the user explicitly says they are done, ending with an actionable one-page field guide.

What Makes This Skill Different

• Step-by-step progression: It never dumps a textbook in one turn. It decomposes the subject into clear progressive milestones and teaches one digestible milestone at a time, tracking progress with breadcrumbs across turns.
• Intuition and mental models first: Before drowning in syntax or jargon, it delivers the core intuition: what problem this solves, what would break without it, and how to visualize the mechanism.
• Visual & diagrammatic anchoring: Uses crisp ASCII sequence diagrams, state flows, memory layouts, or timelines whenever explaining protocols, data flow, or state transitions.
• Concrete minimal code / interfaces: Backs up mechanisms with 5-15 lines of minimal, idiomatic pseudocode or signatures where relevant.
• Selective, simple, and intuitive analogies: Analogies are not forced into every part. They are used only when a concept is abstract or tricky enough that a simple, everyday parallel makes the lightbulb click. When used, they must be dead simple, immediately intuitive, and explicitly call out their breaking point so they never mislead.
• Calibrated altitude: Gauges the learner's desired depth (from high-level architecture to kernel/internals) and explicitly invites altitude recalibration ("too fast", "too deep", "go deeper").
• Flexible interaction: Gracefully handles any learner response: correct explanations, misconceptions, requests to reveal the answer ("tell me"), skips ("next"), or practice challenges.
• Persistent interactive loop with a high-density exit: Remains in teacher mode across conversational turns until the user says "done", "stop", or "exit", finishing with a comprehensive, durable one-page Field Guide.