Claude Code's ELI5 Skill Explained: How It Works, Setup, and When to Use It

This article reflects Claude Code's spec as of August 2026. Since Claude Code updates frequently, check the official docs for the latest behavior.

Type /eli5 <topic> into Claude Code, and the ELI5 skill generates a jargon-free, diagram-heavy HTML explainer on the spot.

It went viral in August 2026 after Anthropic's own dev team mentioned they use it internally.

This post covers how ELI5 works, how to install it, what happened when we pointed it at one of our own technical topics, and what to watch out for before using it at work.

What is the ELI5 skill?

"ELI5" stands for "Explain Like I'm 5." Type /eli5 <topic> in Claude Code, and it produces a single HTML artifact (Claude's panel-style output format) aimed at someone with zero background on the topic, built from big visuals and minimal text.

Unlike a normal chat reply, which tends to come back as a wall of text, the output is locked to three constraints:

• Output format: not a chat reply, but a single standalone HTML artifact
• Information density: "few words." It's built to avoid long explanations.
• Mode of expression: "big pictures." Structure gets shown visually instead of described in prose.

The skill was published by Thariq Shihipar of the Claude Code team, who introduced it on X on August 21, 2026 as "a skill people at Anthropic have been using a lot lately." It's distributed through the community marketplace anthropics/claude-plugins-community.

Why does the actual behavior differ from "explain to a 5-year-old"?

Looking at ELI5's SKILL.md, there's a mismatch between the description field, which controls when the skill triggers, and the body text, which actually shapes the output:

---
name: eli5
description: Explain a topic like I'm a 5 year old. Use when the user types /eli5 <topic> or asks for a dead-simple picture explainer of how something works.
---

# eli5

Explain like I'm someone who knows nothing about this topic, using a HTML artifact with big pictures and few words.

Topic: $ARGUMENTS

The description still says "like a 5-year-old," but the body, the part that actually drives the model, has been changed to "someone who knows nothing about this topic."

That's a deliberate choice: literally targeting a 5-year-old's vocabulary tends to tank explanation accuracy, so the body is tuned instead toward "strip out assumed background knowledge" rather than "talk like you're speaking to a child."

Installing and using ELI5

You can install ELI5 through Claude Code's plugin system in two commands. The plugin itself is distributed free under the MIT license (Claude Code usage still follows your existing plan's pricing).

# 1. Add the community marketplace
claude plugin marketplace add anthropics/claude-plugins-community

# 2. Install eli5 (specify the marketplace after @)
claude plugin install eli5@claude-community

Check that it installed correctly:

claude plugin list

eli5@claude-community
Version: 1.0.0
Scope: user
Status: enabled

Basic usage

Just follow /eli5 with whatever you want explained:

/eli5 how does RAG actually work
/eli5 how does auth work in this project
/eli5 why is the sky blue

Left to its own devices it sometimes generates fewer diagrams than you'd like. If you want it to lean harder into visuals, adding an explicit condition like "include at least 3 SVG diagrams" makes the output more consistent.

Trying it on RAG

We asked ELI5 to explain RAG (retrieval-augmented generation) to see what the output actually looks like.

Prompt used:

/eli5 Explain how RAG works in a simple way. Don't just use text —
include at least 3 SVG diagrams. Show the flow of gather documents →
split them → search → generate an answer as a flowchart with arrows.

The recreated output boiled the process down to four steps: gather documents, chop them into small pieces, find similar chunks, read and answer. It used emoji and short phrases to fit everything on one screen:

• 📚 Gather documents: collect all the internal docs and PDFs
• ✂️ Split into chunks: break long documents into readable pieces
• 🔍 Find similar chunks: pick only the chunks close to the question's meaning
• ✍️ Read and answer: the AI reads the selected chunks and writes an answer

Bottom line: question → find documents → read and answer. That's it.

This is meant as an entry point for grasping what the mechanism even is, not a technically precise spec.

Using ELI5 at work: what to keep in mind

ELI5 is designed to optimize for "gets the idea across" over "is precisely correct." Keep these three things in mind when using it for actual work.

Where ELI5 shines

• Explaining to non-engineers: turn how an AI system works into a single shareable slide for internal proposals or client materials
• Onboarding: give new team members a visual first pass at how auth flows or module structure work before they dig into code
• Incident retrospectives: lay out where a problem started, what it touched, and what ultimately happened, in chronological order
• Checking your own understanding: if you can't turn something into a diagram, that's usually the part you don't actually understand yet

Where ELI5 falls short

Because it optimizes for ease of understanding, it's not a good fit for:

• Verifying exact specs, numbers, or conditional branches
• Checking edge cases and boundary conditions
• Auditing security issues
• Actually fixing code

For that kind of work, use ELI5 to get the big picture first, then switch to normal Claude Code instructions or official documentation to nail down the details.

Try it yourself

Start with something simple like /eli5 why is the sky blue and see how different the output feels from a normal chat response.

It's also a quick way to turn trending technical terms into a shareable visual explainer. Worth keeping in your toolkit.

This article was edited with AI assistance.
*Originally published in Japanese on EdgeHUB.