Explain

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hugobowne

explain

Guided tour of changes or codebase - explains how things work conceptually rather than listing changes mechanically. Use anytime you're asked to explain, review, walk through, or summarize work, branch changes, or codebase architecture.

Overview

Publisher	hugobowne
Repository	`show-us-your-agent-skills`
Skill name	`explain`
Stars	70
Forks	10
Bundled files	1
License	MIT
Links	Skill source Repository Homepage

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by hugobowne on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/hugobowne/show-us-your-agent-skills/tree/main/skills/explain
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.

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/hugobowne/show-us-your-agent-skills.git /tmp/show-us-your-agent-skills
mkdir -p .claude/skills
cp -r /tmp/show-us-your-agent-skills/skills/explain .claude/skills/explain

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Explain 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 Explain 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 Explain 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.

Explain: Conceptual Clarity Over Mechanical Completeness

When the user asks you to explain changes, review what's on a branch, walk through the codebase, or understand how something works, use this approach.

The Style

Conceptual clarity over mechanical completeness. Lead with the mental model, then layer in specifics that illuminate it. This isn't about dumbing things down - the user may be a core maintainer who knows this code intimately. It's about explanations that build understanding rather than enumerate changes.

Talk like you're explaining to a colleague who knows the project but wants to understand this particular work - what you did, how it fits together, why it's shaped this way. Conversational, not documentary. Express enthusiasm when something is elegant. Invite follow-up. Don't quote ADR language or doc-speak - say it how you'd actually say it.

If the user indicates they lack context on something specific, adjust accordingly. But default to assuming expertise.

What NOT To Do

No itemized lists of changes
No file-by-file summaries
No mechanical descriptions of what was modified
No changelog-style output

These are useless for understanding.

What To Do

Provide a guided tour that builds understanding.

Structure Your Explanation

Lead with the conceptual model: What is this system trying to do? What are the key abstractions? How do they relate to each other?
The formal behavior: How does the system work now? What are the invariants, the contracts, the mental model someone needs to have?
What's new or different: If explaining changes, what shifted conceptually? Not "added function X" but "we now support Y, which means Z."
What this enables: Developer-facing implications. What can someone do now that they couldn't before? What patterns does this unlock?
What we're teeing up: If this is setting up for future work, say so. Help the user see the trajectory.

Depth

Err on the side of thoroughness. A rich explanation that builds complete understanding is better than a crisp summary that leaves gaps. Walk through how things actually work - show the flow, explain the mechanics, make the runtime behavior clear. The user can always ask you to trim; they can't fill in what you left out.

Default Behavior

If no specific topic is given, examine the current branch and explain what's different from main. Tell it as a coherent story of what's being built, not as a changelog.

Examples of Good Framing

Instead of:

"Added ResourceRef class in resources.py with methods resolve() and bind()"

Say:

"Resources can now be referenced before they're fully resolved. A ResourceRef is a promise of a resource - you can pass it around, compose with it, and the actual resolution happens lazily when needed. This means you can wire up complex dependency graphs without worrying about initialization order."

Instead of:

"Modified Provider.get() to accept optional context parameter"

Say:

"Providers now have access to execution context, which opens up dynamic configuration. A provider can behave differently based on the environment it's running in - same code, different behavior in dev vs prod."

Bundled files

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

images/hero.png947 KB

Frequently asked questions

What does the Explain AI skill do?

Why use Explain on TypingMind?

Because you install it once and use it with any model. Explain 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 Explain in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hugobowne/show-us-your-agent-skills/tree/main/skills/explain. 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 Explain?

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 Explain?

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

Is the Explain AI skill free?

Yes. It is published on GitHub by hugobowne 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Explain: Conceptual Clarity Over Mechanical Completeness

The Style

What NOT To Do

What To Do

Structure Your Explanation

Depth

Default Behavior

Examples of Good Framing

Bundled files

Frequently asked questions

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