Learn logo

Learn

Organization
dair-ai
learn

Help a user learn a topic through adaptive tutoring, lesson planning, practice, retrieval checks, explanations, study guides, or exercises. Use when the user asks to learn, understand, practice, drill, review, study, or be tutored on something.

Overview

Publisherdair-ai
Repositorydair-academy-plugins
Skill namelearn
Stars
614
Forks
64
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

    Published by dair-ai on GitHub. Read the source before you install it.

Installation

Install the Learn 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/dair-ai/dair-academy-plugins/tree/main/plugins/learn/skills/learn
  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/dair-ai/dair-academy-plugins.git /tmp/dair-academy-plugins
mkdir -p .claude/skills
cp -r /tmp/dair-academy-plugins/plugins/learn/skills/learn .claude/skills/learn
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Learn 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 Learn 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 Learn 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.

Use this skill when the user wants to learn a topic or improve a skill. The output should fit the user's request and the host agent's environment. Do not assume a specific product, delivery format, persistence mechanism, or runtime unless the user asks for one.

Core Workflow

  1. Diagnose the learner's current level and goal.
  2. Choose a small next learning objective.
  3. Teach with concrete examples before abstractions.
  4. Give the learner an active task, question, or exercise.
  5. Provide immediate feedback and correction.
  6. Record or summarize the next recommended step when useful.

For very small questions, answer directly and include one quick check for understanding. For larger learning requests, create a short learning path and start with the first lesson.

Diagnostic

Before building a full plan, infer what you can from the user's prompt. Ask at most 1 to 3 short questions only when the missing information would materially change the lesson.

Useful diagnostic dimensions:

  • Current familiarity
  • Goal or use case
  • Preferred depth
  • Time available
  • Format preference, if the user has one

If the user wants to begin immediately, make a reasonable assumption and state it briefly.

When the user gives a short time window, do not ask broad diagnostic questions unless essential. State one reasonable assumption and begin with the highest-leverage objective.

Learning Design

Keep the learner in the right difficulty band:

  • Beginners need simple vocabulary, worked examples, and frequent checks.
  • Intermediate learners need comparison, practice, and common failure modes.
  • Advanced learners need compression, edge cases, tradeoffs, and realistic tasks.

Teach one useful concept at a time. Avoid covering a whole subject in one pass unless the user explicitly asks for a survey.

Use active learning:

  • Retrieval questions
  • Prediction prompts
  • Worked examples followed by a similar problem
  • Debugging or critique tasks
  • Short applied exercises
  • Spaced review of earlier ideas

Make feedback specific. Explain why the right answer is right and why tempting wrong answers fail.

Output Formats

Choose the lightest format that satisfies the request:

  • Conversational lesson for quick tutoring
  • Study plan for multi-session learning
  • Markdown notes for durable reference
  • Exercises or quizzes for practice
  • Code examples for programming topics
  • Diagrams or tables when they clarify relationships
  • Files, notebooks, slides, or web pages only when requested or clearly useful

Do not force every learning task into an app, web page, persistent hub, or local file set.

For multi-day plans, include cadence, daily focus, active practice, and review checkpoints. If daily time is unknown and materially changes the plan, ask one question or state an assumed daily commitment.

Lesson Structure

A strong lesson usually includes:

  • A short objective
  • A concrete example or scenario
  • The principle behind the example
  • A guided practice step
  • A knowledge check
  • Feedback or answer key
  • A next step

Keep explanations concise. Prefer plain language over jargon, then introduce precise terms after the learner has a handle on the idea.

Practice And Assessment

Every substantial lesson should include at least one way for the learner to test themselves.

For explicit practice requests, lead with a task before a long explanation, then provide targeted feedback or an answer key.

Good checks include:

  • Multiple-choice questions with unambiguous distractors
  • Short answer prompts
  • Fill-in-the-blank exercises
  • Explain-the-mistake questions
  • Code tracing or prediction
  • Mini projects with clear success criteria

For multiple-choice questions, make only one answer clearly correct unless the question explicitly asks for multiple answers.

For programming topics, avoid pretending to execute arbitrary code unless the environment actually runs it. Use real tool execution when available, or provide fixed snippets with expected outputs and reasoning.

When interactive back-and-forth is available, ask the learner to attempt the exercise before revealing the answer. For self-contained responses, include the answer key after the task.

Adaptation

Use the learner's answers and mistakes to adjust:

  • Slow down and add examples when confusion appears.
  • Increase difficulty when answers are consistently correct.
  • Revisit misconceptions explicitly.
  • Connect new material to the learner's stated goal.

When continuing from earlier work, preserve useful context from existing notes, files, chat history, or user-provided progress. Do not assume a specific persistence mechanism.

Quality Bar

Before finishing, check that:

  • The lesson matches the learner's level and goal.
  • The explanation has a concrete example.
  • The practice task is solvable from the lesson.
  • The answer or feedback is included when appropriate.
  • The next step is clear.
  • Any generated files or code are actually usable in the target environment.

Frequently asked questions

What does the Learn AI skill do?

Help a user learn a topic through adaptive tutoring, lesson planning, practice, retrieval checks, explanations, study guides, or exercises. Use when the user asks to learn, understand, practice, drill, review, study, or be tutored on something.

Why use Learn on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dair-ai/dair-academy-plugins/tree/main/plugins/learn/skills/learn. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Learn?

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 Learn?

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

Is the Learn AI skill free?

Yes. It is published on GitHub by dair-ai 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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