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Choose a useful medium for understanding a topic, then get a prompt to create it.

Output Ladder is an agent skill for turning a learning goal into clear writing, a diagram, an interactive web page, or an explainer video. It considers the audience and practical constraints, explains its choice, and produces a self-contained prompt you can reuse.

It generates prompts by default. Creating the actual page, diagram, or video is a separate request and depends on the tools available in your agent environment.

The ladder expands the available media; a higher level does not guarantee better understanding. The skill selects the simplest medium that meets your learning goal and constraints. L3 supports learner-controlled exploration, while L4 supports guided exposition without requiring interaction.

If you already know which medium you want, specify it and the skill will honor that choice.

The skill instructions are in SKILL.md, at the root of this repository. No packages or API keys are required to install the skill itself.

Run the appropriate command from the root of your downloaded or cloned repository.

Install for your user account:

mkdir -p "$HOME/.agents/skills/output-ladder"
cp SKILL.md "$HOME/.agents/skills/output-ladder/SKILL.md"

Invoke it with $output-ladder in your request.

Install for your user account:

mkdir -p "$HOME/.claude/skills/output-ladder"
cp SKILL.md "$HOME/.claude/skills/output-ladder/SKILL.md"

For a project-specific installation, place the file at .claude/skills/output-ladder/SKILL.md inside that project instead.

Invoke it with /output-ladder followed by your request.

For environments supporting the Agent Skills format, install the output-ladder folder using that environment's instructions. You can also provide the contents of SKILL.md directly as instructions to an LLM; automatic discovery and invocation depend on the host.

Natural language is enough. Describe the topic, who it is for, and any constraints. The response follows your language unless you request another.

For Codex:

$output-ladder Help me understand Bayes' theorem. I'm a beginner; recommend a useful format and give me a prompt.

For Claude Code:

/output-ladder Help me understand TCP congestion control. Use an interactive web page for beginners, offline, with no external dependencies.

You can also use field-style shorthand:

/output-ladder topic: Raft consensus audience: beginner preference: video constraints: offline, 3 minutes, captions without narration

These fields guide the agent; they are not a strict command-line syntax.

A response contains:

1. Topic analysis: The learning bottleneck and relevant audience assumptions.
2. Recommended or selected level: The medium and a brief rationale.
3. Ready-to-use prompt: A copyable prompt with content, deliverable, constraints, accuracy checks, and observable learning outcomes.
4. Alternative options: Up to two useful alternatives, omitted when you request only one format.
5. Practical tips: Suggestions for verification and iteration.

For example, an offline TCP congestion-control request can produce an L3 prompt for a simplified TCP Reno simulator with a congestion-window chart, loss-event controls, explicit model assumptions, and worked traces. See the complete example in SKILL.md.

Copy the generated prompt into an agent with the appropriate tools, or ask your current agent to use it to create the artifact.

The skill applies your constraints to the main prompt, alternatives, and tool recommendations.

• Offline: No runtime network requests, CDNs, remote fonts, or cloud narration. One-time setup requirements are identified separately; specify “offline setup too” if dependencies must already be available locally.
• No API keys: Choose tools that do not require credentials. This is distinct from offline operation.
• Video: A storyboard, source project, and rendered video are different deliverables. State which you need; rendering requires an appropriate toolchain.
• Accuracy: Check key facts, equations, algorithm variants, and simulated behavior. Custom artifacts can be disposable while still requiring correct teaching content.
• Writing: ASD-STE100-inspired technical English is optional. Short sentences alone do not establish formal compliance with the standard.

Mermaid, native HTML/CSS/JS, Manim, and Remotion are possible artifact tools, not dependencies of this skill. Tool availability, costs, and generated output quality depend on your environment.

Inspired by Andrej Karpathy's original post on understanding language-model outputs through writing, diagrams/images, web pages, and bespoke explainer videos.

The post presents these media progressively, discusses ASD-STE100 and 3b1b-style explainers, and advocates custom, discardable software artifacts. This project adds the Understanding-First Output Ladder name, L1–L4 labels, selection rules, parameters, and prompt templates. Those additions are project interpretations, not a named methodology from the original post. This project does not imply endorsement by Karpathy or affiliation with 3Blue1Brown.

Improvements to format selection, prompt quality, examples, and accessibility are welcome. Keep changes consistent with the skill's scope: choosing media and generating prompts for understanding.

When proposing a behavioral change, include a realistic request and the resulting output. Check explicit preferences, L1/L4 boundaries, offline constraints, and topic accuracy where relevant. Distinguish structural validation from actual model behavior testing.

Released under the MIT License.

The SKILL.md instructions and the README files are covered by the same MIT terms. The inspiration and attribution notes above describe provenance, not endorsement: the MIT grant applies to this project's own text, not to Andrej Karpathy's original post or to any third-party trademarks.