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Live Tutorial

OrganizationPopular
leon-ai
live-tutorial

Create an annotated MP4 tutorial from a workflow demonstrated in the owner's actual application. Use when asked for a tutorial video, not merely to watch an action live.

Overview

Publisherleon-ai
Repositoryleon
Skill namelive-tutorial
Stars
17.5K
Forks
1.5K
Bundled files
1
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.

  • 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 leon-ai on GitHub. Read the source before you install it.

Installation

Install the Live Tutorial 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/leon-ai/leon.git /tmp/leon
mkdir -p .claude/skills
cp -r /tmp/leon/skills/agent/live-tutorial .claude/skills/live-tutorial
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Live Tutorial 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 Live Tutorial 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 Live Tutorial 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.

Live Tutorial

Demonstrate the requested workflow, then return a verified MP4 made from its real screenshots.

Write captions and annotation text in the language of the user's tutorial request, keeping app control names as displayed.

The request already authorizes completing and rendering the tutorial. Do not ask whether to continue or offer screenshots/text instead of the requested video merely because execution paused. Resume with the same recording, captures, and manifest; ask only for genuinely missing information or authorization.

Use PNG paths returned in each observation's artifacts array. The recording output_dir is the destination for the video, not the location of those screenshots; do not search it for frames. The renderer is ready to run with the manifest below: no source inspection, image-processing code, or dependency setup is needed.

Use the live tool results for recording directories, screenshot paths, and current target tokens. After a pause, read a specific saved artifact only if the checkpoint omits a required value; do not scan directories or reread evidence already available. Each new window observation invalidates earlier tokens, so make one observation with any needed query and screenshot, then act immediately from that same result. Use the shell tool only for the final renderer command.

  1. Select the target application and one short route from its current view to the requested result. Demonstrate only the requested workflow, not every related option. Do not reset the application or visit unrelated settings merely to create a starting frame. If already at the result, show only the navigation needed to teach the requested step; do not claim a pre-existing state was caused by an action you did not perform.
  2. Start computer_use.cua.start_recording with record_video: false; retain output_dir.
  3. Use the shortest supported route: a known keyboard shortcut, an accessibility-backed action, or computer_use.cua.invoke_menu for native menus; never shell automation. Capture the starting state before acting. During recording, supported input actions automatically return a fresh screenshot and element queries include screenshots. Reuse the returned observation for verification and the next step instead of taking another identical screenshot. Observe again only if the result lacks needed evidence. Captions must describe what you actually did, including any shortcut; retain the meaningful intermediate states.
  4. Always stop recording. Select 2–6 distinct captures from the successful route, normally one per action plus the result; omit setup, failed attempts, retries, and polling. Keep valid captures across retries rather than re-recording the workflow. The final result must agree with the demonstrated actions. Use a short instruction for each action and a confirmation caption for the result.
  5. Write the manifest below with the file tool, then run the bundled renderer through the shell tool. Copy the returned artifact paths verbatim, including profile and session IDs; do not reconstruct them. Satellite results provide server-local copies for rendering. Do not replace this with a raw screen recording or a custom automation/rendering script. Do not inspect or rewrite the renderer unless it reports an error.
  6. Check the renderer's resolvedTargets against the intended controls and the source observations. Correct any mismatch before returning fileMarker verbatim. The returned previewPaths identify the rendered step frames. If unfinished, explain the obstacle.

Render

bash
"$NODE" "$LEON_CODEBASE_PATH/skills/agent/live-tutorial/scripts/render-tutorial.mjs" --manifest "/exact/path/to/tutorial.json"

Leon supplies NODE and LEON_CODEBASE_PATH. Do not set a working directory or construct executable paths. No system Node, Python, or FFmpeg installation is needed.

On PowerShell, use & $env:NODE "$env:LEON_CODEBASE_PATH/skills/agent/live-tutorial/scripts/render-tutorial.mjs" --manifest "/exact/path/to/tutorial.json".

Manifest:

json
{
  "outputDir": "<output_dir from start_recording>",
  "steps": [
    {"screenshotPath": "<pre-action PNG path>", "targetToken": "<element_token from this exact capture>", "instruction": "Open the relevant setting."},
    {"screenshotPath": "<result PNG path>", "instruction": "Read the displayed value."}
  ]
}

Use exact current-session paths and captions of at most 240 characters. Prefer targetToken: the renderer reads capture-bound geometry from the PNG's companion JSON and draws the control outline, arrow, and step number automatically.

Use point: {x, y, coordinateWidth, coordinateHeight} when the intended control has no accessible target geometry, even if other controls in the capture do. Copy the exact x/y and coordinate dimensions from the successful action performed on that capture; do not estimate a new annotation point. Never copy a point from another capture or mix window and desktop coordinates. The final result frame must have no point annotation; omit its marker or use a verified targetToken when highlighting a result is genuinely useful.

A tutorial request authorizes demonstrating navigation, not choosing an unspecified setting value or completing a destructive, financial, publishing, or security-sensitive action. Stop before committing such changes unless the owner explicitly authorized them.

Bundled files

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

Frequently asked questions

What does the Live Tutorial AI skill do?

Create an annotated MP4 tutorial from a workflow demonstrated in the owner's actual application. Use when asked for a tutorial video, not merely to watch an action live.

Why use Live Tutorial on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/leon-ai/leon/tree/develop/skills/agent/live-tutorial. 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 Live Tutorial?

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 Live Tutorial?

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

Is the Live Tutorial AI skill free?

Yes. It is published on GitHub by leon-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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