feynman
Explain any concept in the simplest possible terms, then surface exactly where understanding is still fuzzy.
Skill by atalariq
Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/atalariq-feynman
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: testing
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
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Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/feynman
What's inside
- Skill name
feynman- Description
- Explain any concept in the simplest possible terms, then surface exactly where understanding is still fuzzy. Use when the user wants to deeply understand something — not just encounter it. Trigger on "feynman [topic]", "explain like I'm 5", "help me really understand", "why does X work", "I keep reading about X but don't get it", or any time the user seems to have surface familiarity without real understanding. Also use after explaining something complex when the user should test their grasp before moving on.
- License
- MIT
- Instructions
- 61 lines in SKILL.md · view on GitHub
Files (1)
- SKILL.md
SKILL.md (first lines)
# Feynman
Named after Richard Feynman's learning technique: if you can't explain it simply, you don't understand it yet.
Your job is to explain a concept as simply as possible, find where the user's understanding is still fuzzy, and help them fix it.
## Core principle
Jargon is a hiding place for incomplete understanding. Strip it away. If a concept can't be explained without specialized vocabulary, explain the vocabulary first — in plain terms.
## Process
### Phase 1 — Simple explanation
Explain the concept as if to someone smart but with no background in the field:
- Use concrete analogies and real examples, not abstract definitions
- Build from what people already know
- When you must use a term of art, define it immediately in plain language
- Aim for the explanation to be surprising in its simplicity — "oh, that's all it is?"
Identify the 2-3 hardest things to truly understand about this concept (the parts people fake-understand most often). Flag them explicitly.
### Phase 2 — Check understanding
After the explanation, ask the user to explain the concept back to you in their own words. Don't accept "I think I get it" — ask them to actually articulate it.
Use this prompt: _"Now explain it back to me as if I've never heard of it. Don't look at what I wrote — use your own words."_
### Phase 3 — Diagnose and fix
When the user explains it back:
- Identify specifically what's correct
- Identify specifically what's fuzzy, missing, or wrong
- Don't just say "almost right" — name the exact gap
- Patch the gap with a targeted explanation, not a full re-explanation
Watch for fake understanding: repeating your exact phrasing (memorized, not understood), using the jargon term to define itself ("X is when you X"), explaining the what but not the why, or explaining the normal case but not what breaks the rule.
Repeat Phase 2-3 until the user's explanation is solid.
## Output format — Phase 1
**The simple explanation:**
[Plain language explanation with analogy]
**The hardest parts to actually understand:**
- [Hard part 1] — here's why people get this wrong: [common misconception]
- [Hard part 2] — here's why people get this wrong: [common misconception]
**Now your turn:**
Explain [concept] back to me in your own words. Don't reread my explanation — just go from memory.
## Example trigger
User: "feynman transformers"
→ Explain self-attention in terms of weighted relevance between words, using a concrete sentence example. Flag that "attention heads" and "positional encoding" are the parts people fake-understand most. Ask them to explain it back.From atalariq/skills, MIT.
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Signals
- Stars
- 1
- Forks
- 0
- Open issues
- 0
- Last commit (this folder)
- Jul 2, 2026 (3 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- MIT
- Repository
- github.com/atalariq/skills
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Find deepening opportunities in a codebase — surface architectural friction and propose refactors that make it more testable and AI-navigable.
SkillCFreshness: C★ 1Claude Code +9 - 0.502score
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Cite this page
feynman skill. Agent Mart. https://agentmart.dev/skills/atalariq-feynman (last updated 2026-10-24)