KtKID

Feynman Learn — Development skill for Claude Code

Development community

Feynman technique learning coach for quick concept explanations, analogies, teach-back prompts, diagnostic questions, and knowledge-map learning plans.

How to install Feynman Learn

This entry records only its repository, not the path inside it, so there is no exact command to give. Open KtKID/feynman-learn and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What Feynman Learn does

Feynman technique learning coach for quick concept explanations, analogies, teach-back prompts, diagnostic questions, and knowledge-map learning plans.

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README

[中文说明](README_zh.md)

feynman-learn

feynman-learn is a Feynman explanation skill. It helps the assistant explain complex knowledge through a beginner-friendly story, then attach the technical points back to that story.

The skill is useful when the user wants to:

  • Understand a concept, mechanism, distinction, or process
  • Explain a technical topic to a non-expert
  • Learn a pipeline or training flow
  • Check whether their own explanation is clear
  • Build a knowledge map or learning plan when explicitly requested

Core Idea

The skill should make the user understand, not make the assistant look comprehensive.

A good explanation:

  • Starts with one familiar picture from everyday life
  • Uses that picture as the backbone of the explanation
  • Maps each technical term to a role, action, or result in the story
  • Explains processes by saying what existed before and what changed after
  • Leaves the user with one sentence they can repeat

How It Behaves

For a normal concept question, it explains the idea through a familiar story and a small example.

For a process question, it follows the main line of the story and shows how each step changes the situation.

For a knowledge-map or learning-plan question, it gives structure and sequence because the user explicitly asked for navigation.

The skill avoids fixed templates, forced sections, and course-like expansions for small questions.

Example

When explaining large model training, use one story such as training a new employee:

  • Data cleaning: preparing clean training material
  • Pretraining: reading a lot and doing fill-in-the-blank practice
  • Instruction tuning: learning how to follow workplace tasks
  • Alignment: learning which answers are more appropriate and safer
  • Evaluation: taking exams
  • Deployment: going on the job
  • User feedback: reviewing real work and improving next time

The explanation works when each technical point can return to the same story.