Explain logo

Explain

Organization
TheBushidoCollective
explain

Explain code, concepts, or technical decisions in clear, understandable terms

Overview

PublisherTheBushidoCollective
Repositoryhan
Skill nameexplain
Stars
195
Forks
20
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by TheBushidoCollective 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.

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.
  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/TheBushidoCollective/han.git /tmp/han
mkdir -p .claude/skills
cp -r /tmp/han/plugins/core/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.

Explainer Skill

Create clear, insightful explanations of code, concepts, and technical decisions.

Core Principle

Clarity over completeness. An explanation is only useful if it's understood.

Name

han-core:explain - Explain code, concepts, or technical decisions in clear, understandable terms

Synopsis

/explain [arguments]

Explanation Process

1. Understand the Question

What are they really asking?

  • Literal question: "What does this do?"
  • Deeper question: "Why does this exist?" or "How does this work?"
  • Context matters: Junior dev vs senior dev needs different explanations

2. Gather Full Context

Read before explaining:

  • The code in question (obvious)
  • Surrounding code (function, class, module)
  • Related code (what calls it, what it calls)
  • Tests (reveal intended behavior)
  • Comments (capture original reasoning)
  • Git history (understand evolution)

Anti-pattern: Explaining code you only partially understand

3. Structure the Explanation

Start broad, then narrow:

  1. One-sentence summary: What it does in plain English
  2. Purpose: Why it exists, what problem it solves
  3. High-level approach: How it solves the problem (conceptually)
  4. Implementation details: Specific code patterns, algorithms
  5. Edge cases: Unusual scenarios handled
  6. Related concepts: Connections to other parts of the system

4. Choose the Right Level

Match explanation to context:

For "What does this do?"

BAD: "It's a reducer function that takes state and action..."
GOOD: "This manages the shopping cart state. When you add/remove
       items, it updates the cart and recalculates the total."

For "How does this work?"

BAD: "It just loops through items and sums prices."
GOOD: "It iterates through cart items, applies discounts to each,
       then sums the discounted prices. Tax is calculated on the
       subtotal, not individual items, to avoid rounding errors."

For "Why this approach?"

BAD: "Because it's faster."
GOOD: "We chose this over X because: (1) handles async updates
       correctly, (2) prevents race conditions when multiple users
       modify the cart, (3) makes testing easier. Trade-off is
       slightly more complex code."

Explanation Levels

Tailor explanations based on context:

  • High-level: What it does and why it exists
  • Implementation: How it works internally
  • Technical details: Specific algorithms, patterns, edge cases
  • Historical: Why this approach was chosen over alternatives

Explanation Patterns

Code Explanation Format

markdown
## What it does

[One-sentence plain English summary]

## Purpose

[Why this code exists, what problem it solves]

## How it works

1. [High-level step 1]
2. [High-level step 2]
3. [High-level step 3]

### Key details

- [Important implementation detail 1]
- [Important implementation detail 2]

### Example

[Concrete example showing usage]

## Related

- [Link to related code/docs]

Concept Explanation Format

markdown
## [Concept Name]

[One-sentence definition]

### Why it matters

[Practical importance, when you'd use it]

### How it works

[High-level explanation without jargon]

### Example

[Concrete, relatable example]

### In our codebase

[Where we use this concept, with links]

### Common pitfalls

[What to watch out for]

Decision Explanation Format

markdown
## Decision: [What was decided]

### Context

[Situation that required a decision]

### Options considered

1. **[Option 1]**: [Brief description]
   - Pros: [Key benefits]
   - Cons: [Key drawbacks]

2. **[Option 2]**: [Brief description]
   - Pros: [Key benefits]
   - Cons: [Key drawbacks]

### Choice: [Selected option]

**Rationale:**
[Why this option was chosen over others]

### Trade-offs accepted

[What we gave up by choosing this approach]

### References

[Links to discussions, docs, related decisions]

Writing Guidelines

Be Concrete

Bad: "This uses a common design pattern for state management" Good: "This uses the Redux pattern: all state changes go through a central store via actions and reducers"

Use Analogies (When Helpful)

Example: "Think of this like a restaurant kitchen:

  • Actions are customer orders
  • Reducers are chefs preparing food
  • Store is the completed order waiting area"

Warning: Don't force analogies. If it's clearer without, skip it.

Show, Don't Just Tell

typescript
// BAD explanation:
// "This function filters and maps the array"

// GOOD explanation:
// "This function finds all active users and formats them for display:
const displayUsers = users
  .filter(u => u.status === 'active')  // Only show active users
  .map(u => ({                         // Convert to display format
    id: u.id,
    name: `${u.firstName} ${u.lastName}`,
    joined: formatDate(u.createdAt)
  }))

Avoid Jargon (Unless Explaining Jargon)

Bad: "This leverages a memoized selector with referential equality optimization" Good: "This caches the filtered list so we don't recalculate it every render, improving performance"

Exception: When explaining what jargon means: "Memoization means caching function results so we don't recompute the same thing twice."

Common Explanation Scenarios

Explaining Error Messages

markdown
## Error: "Cannot read property 'map' of undefined"

**What happened:**
You're trying to use `.map()` on something that's `undefined`.

**Where:** [Link to line]

**Why:**
The `users` array hasn't loaded yet (async), so it's `undefined`
when the component first renders.

**Fix:**
```typescript
// Before
users.map(u => ...)

// After
{users?.map(u => ...) || <Loading />}

Why this works: ?. is optional chaining - it only calls .map() if users exists. If not, it shows a loading state instead.


### Explaining "Why Not X?"

```markdown
## Why not use X?

**Context:** [What X is]

**Reasons we chose Y instead:**

1. **[Primary reason]**: [Explanation]
2. **[Secondary reason]**: [Explanation]
3. **[Tertiary reason]**: [Explanation]

**Trade-offs:**
Y is slower than X, but the reliability benefit outweighs the
50ms performance difference.

**When X would be better:**
If we needed real-time updates (< 100ms), X would be the right choice.

Explaining Legacy Code

markdown
## Legacy: [Module Name]

**What it does:** [Current function]

**History:**
This was written in 2019 when [context]. At the time, [justification].

**Why it looks odd today:**
Modern approaches would use [better pattern], but this works and
changing it isn't worth the risk.

**If you must modify it:**
1. [Key constraint to preserve]
2. [Another constraint]
3. Add tests first (none exist currently)

**Related:** See [modern equivalent] for new code.

Output Format

  • Start with a brief summary
  • Break complex explanations into sections
  • Use code examples to illustrate points
  • Link to relevant documentation
  • Avoid jargon unless explaining jargon

Anti-Patterns

Assuming Knowledge

BAD: "This uses HOCs to inject props via connect()"
GOOD: "This connects the component to Redux, giving it access
       to the store data it needs. See: [Redux docs link]"

Over-explaining Obvious Code

typescript
// Don't explain this:
const total = price * quantity  // Multiplies price by quantity

// Do explain this:
const total = price * quantity * (1 - discount) * TAX_RATE
// Discount applied before tax, per accounting requirements

Vague References

BAD: "Similar to what we do in other places"
GOOD: "Similar to UserService.findActive() in services/user.ts:45"

Explaining How When Asked Why

Question: "Why do we use a Set here instead of an Array?"

Bad: "A Set is initialized with new Set() and supports .add() and .has() methods..."

Good: "A Set automatically removes duplicates. We need unique user IDs, and Set handles that for us. An Array would require manual duplicate checking."

Verification

After explaining, check:

  • Does this answer the actual question asked?
  • Is it understandable without reading it 3 times?
  • Are technical terms explained or linked?
  • Are there concrete examples?
  • Does it avoid unnecessary complexity?
  • Would someone new to the codebase understand this?

Examples

When the user says:

  • "Explain what this function does"
  • "Why are we using this pattern here?"
  • "What's the difference between these two approaches?"
  • "How does the authentication flow work?"
  • "Explain this error message"

Integration with Other Skills

  • Use code-reviewer skill when explaining code quality issues
  • Use proof-of-work skill when explaining behavior (show actual output)
  • Use simplicity-principles skill when explaining why simpler is better
  • Reference other skills when explaining patterns they cover

Notes

  • Focus on clarity over completeness
  • Use analogies when helpful
  • Provide context for technical decisions
  • Include links to related code or docs

Remember

The best explanation is the one that's understood, not the one that's most complete.

If in doubt, start simple and add detail only when needed.

Frequently asked questions

What does the Explain AI skill do?

Explain code, concepts, or technical decisions in clear, understandable terms

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/TheBushidoCollective/han/tree/main/plugins/core/skills/explain. TypingMind reads its SKILL.md 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?

It is published on GitHub by TheBushidoCollective. Check the repository for licensing terms. 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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