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Understanding Ladder

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ChangWenC
understanding-ladder

Pick the output format that makes an explanation easiest to understand, following Karpathy's understanding ladder — (1) plain controlled text, (2) diagram, (3) interactive HTML page, (4) 3Blue1Brown-style explainer video. Classify the content (definition/steps, process/causality, parameter→outcome, unfolding derivation), recommend the lowest rung that fully answers, then produce it. Works in English and Chinese. Use when the user says "explain this clearly", "help me understand", "draw a diagram", "make it interactive", "make an explainer video", "what's the best way to explain X", "understanding ladder", "Karpathy ladder", or 「讲清楚」「帮我理解」「画个图」「做个交互页」「做个讲解视频」「用哪种方式讲最好懂」「理解阶梯」; also when explaining a non-trivial concept, mechanism, algorithm, or an AI agent's output.

Overview

PublisherChangWenC
Repositoryunderstanding-ladder
Skill nameunderstanding-ladder
Stars
113
Forks
2
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by ChangWenC on GitHub. Read the source before you install it.

Installation

Install the Understanding Ladder AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ChangWenC/understanding-ladder/tree/main/skills/understanding-ladder
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ChangWenC/understanding-ladder.git /tmp/understanding-ladder
mkdir -p .claude/skills
cp -r /tmp/understanding-ladder/skills/understanding-ladder .claude/skills/understanding-ladder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Understanding Ladder in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Understanding Ladder on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Understanding Ladder is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Understanding ladder

This skill decides which format to explain in. Writing quality inside every format is handled by level 1 rules (below).

Source: Andrej Karpathy, 2026-10-02 https://x.com/karpathy/status/2105819303471976479. LLM output is nearly free; human understanding is the bottleneck. So ask for formats that are easier to understand. He ranked four, each "even better" than the last — and each more expensive.

Language: reply in the user's language. These instructions are in English; the output is not.

The four rungs

RungFormatWhy it is easierCost
1Plain controlled textOne idea per sentence, one meaning per term; read once, no misreadinglowest
2DiagramText is linear; a diagram lays the structure out at oncelow
3Interactive HTML pageThe reader moves a parameter and sees the result changemedium
4Explainer video (3b1b style)Animation shows the process while narration explains ithigh

Step 1: classify the content, pick a rung

Content typeSignalsRung
Definition, fact, comparison, procedure"what is X", "how do I install", "A vs B"1
Process, causal chain, hierarchy, state changes, component relations"how does X work", "how does data flow", "who calls whom"2
A parameter changes an outcome; trade-offs; distributions"what happens if utilization rises", "how does learning rate affect convergence"3
A derivation or geometric intuition that unfolds over time"why do sine waves add up to a square wave", "how does gradient descent move on a surface"4

Rules:

  1. If the user names a format, use it. "Draw a diagram" means rung 2.
  2. Pick the lowest rung that fully answers. If a paragraph is enough, do not build a page.
  3. Mixed content: pick by the core difficulty. If the hard part is "how a parameter changes the result", pick rung 3 even if there is a definition inside.
  4. When unsure, go one rung lower and offer the next. End with one line, e.g. "If you want to drag the parameter yourself, I can make this a rung-3 interactive page."

Open with one line that states the choice, then produce it. Do not ask "which rung do you want?" unless the user asked for a recommendation:

Rung 3 (interactive page): the core question is how utilization changes latency — dragging a slider shows this faster than text.

Step 2: produce the rung

Every rung starts from a rung-1 draft. Write the plain text first, then turn it into a diagram, page, or video. The draft is what you check for correctness before spending effort on richer formats.

Rung 1: plain controlled text

Pick the rules by output language:

  • Chinese → use the plain-chinese skill (as a plugin it may be named understanding-ladder:plain-chinese). Chinese has no "ASD-STE100" to invoke, so that skill spells the rules out.
  • English → write "about 80% of the way to ASD-STE100" (Karpathy's phrasing), with these rules made explicit:
    • One idea per sentence. Aim for ≤20 words in instructions, ≤25 in descriptions.
    • One term per meaning. Define a term the first time; never rotate synonyms.
    • Active voice; name who does what. Simple tenses.
    • Steps as a numbered list, imperative verb first.
    • Conditions before actions ("If X, do Y").
    • No filler ("It's worth noting", "Great question", "In summary…").
    • Nothing cut. Keep every fact, number, condition, and hedge ("usually", "may"). Simple language, not less content — the rewrite may be longer.
    • Do not touch code, commands, paths, quoted errors, or proper names.
  • Other languages → apply the English rules in that language.

Rung 2: diagram

See references/level-2-diagram.md. In short:

  • Choose the diagram type first: flowchart, sequence, state, hierarchy, causal graph, comparison table.
  • Default to Mermaid (renders in most chat UIs and on GitHub). Use a single-file SVG/HTML when layout matters.
  • ≤8 words (or ≤8 Chinese characters) per box. Below the diagram, 2–4 sentences on how to read it.

Rung 3: interactive HTML page

See references/level-3-html.md. In short:

  • One self-contained HTML file. No build step; double-click to open.
  • One job: let the reader move the 1–3 parameters that matter and see the result update live.
  • Layout: one-line takeaway → interactive area → rung-1 text → "try this" prompts → limits and what to check.
  • Save it in the working directory, tell the user the path, open it if the environment allows.

Rung 4: explainer video

See references/level-4-video.md. This skill does not render video. It delivers:

  • A storyboard: per shot — visual, narration (rung-1 rules), duration.
  • Tool links, e.g. showtime (open source, early; v0.4.0+ reads the storyboard table directly), Manim, local TTS Kokoro.
  • A ready-to-paste prompt for those tools. With showtime ≥ 0.4.0, the storyboard table goes in as-is: showtime new <template> <dir> --from-storyboard storyboard.md.

Demo mode: same content, every rung

When the user says "show all rungs", "walk this from rung 1 to 4", 「逐级演示」:

  1. Rung 0: the usual default prose, as a baseline.
  2. Rung 1: plain text.
  3. Rung 2: diagram.
  4. Rung 3: interactive page (can hold rungs 0–2 as tabs in the same HTML file).
  5. Rung 4: storyboard.

This is the best way to feel how much the format itself changes understanding.

Always append "What to check"

End every output with 1–3 things the reader should verify: key numbers, formulas, assumptions, anything that disagrees with a source.

Why: the ladder lowers the cost of understanding, not the cost of verification. The clearer and prettier the artifact, the more it lowers the reader's guard. An error in an animation is more convincing than the same error in text.

Do not

  • Climb rungs to look thorough. If rung 1 is enough, stop at rung 1.
  • Add facts in the diagram, page, or video that are not in the rung-1 draft. New facts go into the draft first.
  • Invent data to make a page look real. Label sample data as sample data.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Understanding Ladder AI skill do?

Pick the output format that makes an explanation easiest to understand, following Karpathy's understanding ladder — (1) plain controlled text, (2) diagram, (3) interactive HTML page, (4) 3Blue1Brown-style explainer video. Classify the content (definition/steps, process/causality, parameter→outcome, unfolding derivation), recommend the lowest rung that fully answers, then produce it. Works in English and Chinese. Use when the user says "explain this clearly", "help me understand", "draw a diagram", "make it interactive", "make an explainer video", "what's the best way to explain X", "underst...

Why use Understanding Ladder on TypingMind?

Because you install it once and use it with any model. Understanding Ladder is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Understanding Ladder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ChangWenC/understanding-ladder/tree/main/skills/understanding-ladder. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Understanding Ladder?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Understanding Ladder?

As many as you like. As long as a model supports skills, you can use Understanding Ladder with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Understanding Ladder AI skill free?

Yes. It is published on GitHub by ChangWenC under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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