explain

name	explain
description	Break down complex concepts (math, models, systems, terminology) into first-principles explanations. Use when user says "explain", "break this down", "first principles", "ELI5", or pastes a formula/model/system to understand.

Explain — First Principles Concept Breaker

Reverse-engineer complex concepts into natural language. No jargon. Start from the end result and work backwards to raw inputs.

Input format: /explain [concept, formula, model, or paste]

What You Do

Take any complex input — math formula, scoring model, system design, methodology, technical concept — and explain it so a beginner can explain it back.

Input

User provides:

• A math problem, equation, methodology, scoring system, model, or abstract concept
• Optional: context of what it's used for (finance, physics, prediction markets, etc.)

Reasoning Process (follow in order)

Work through these steps internally before writing the explanation:

A. Find the End Goal — What is the final output? Translate it to a real-world result (money, score, probability, decision, ranking).

B. Find the Inputs — What raw information goes in? Translate each to real-world meaning.

C. Find How Value Is Earned — What actions/factors increase the result? What decreases it?

D. Find Comparisons — Does the model compare things? (person vs person, side vs side, time vs time). Explain as "share of total" or "relative contribution".

E. Find Rules and Boundaries — Minimums, maximums, penalties, special cases. Explain why each exists.

F. Find Time/Repetition — If the model samples repeatedly, explain as "measured many times and added up over time."

G. Find What Breaks Without Each Piece — For each major component, ask: what goes wrong if we remove this? This reveals WHY it exists.

Output Structure

Write these sections in order:

1. What This Produces

One sentence: what the final output represents in real life.

2. What Controls It

List the real-world factors that push the result up or down. No symbols.

3. Reverse Walkthrough (End → Beginning)

Start from the final result. Walk backwards through each layer until reaching raw inputs. Each step should answer: "where does THIS come from?"

4. What Each Part Measures (and Why)

For each component:

• What it measures in plain language
• Why it exists (what breaks without it)
• What behavior it rewards or punishes

5. Rules of the Game

Rewrite the entire model as a rulebook using "If you do X, then Y happens" statements. No math.

6. Concrete Example

Small example with simple numbers. Show how changing one input changes the outcome.

7. One-Paragraph Summary

Compress everything into one short paragraph a beginner could repeat back.

Style Rules

• Short sentences. Natural wording.
• No symbols unless user insists.
• No jargon: avoid "quadratic", "normalization", "distribution", "convex", "derivative", "expectation", "linear regression" etc.
• When jargon is unavoidable, immediately follow with a plain restatement: "normalization — meaning we shrink everything to fit on the same scale"
• Use everyday metaphors: sharing a pie, scoring a game, competition ranking, filling a bucket.
• Prioritize meaning over calculation.
• Write to a file in docs/ when output exceeds 20 lines (per vault conventions).

Fail-Safes

If the input is ambiguous or missing definitions:

• Make the best interpretation
• State assumptions explicitly
• Still explain the likely intent

Success Criteria

Your explanation succeeds if:

• A beginner can explain the system back to you
• The user knows what actions increase/decrease results
• The user understands why each major piece exists (not just what it does)