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Screen Annotation

OrganizationPopular
vellum-ai
screen-annotation

Point at things on the screen the user is showing you

Overview

Publishervellum-ai
Repositoryvellum-assistant
Skill namescreen-annotation
Stars
1.3K
Forks
186
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Screen Annotation 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/vellum-ai/vellum-assistant.git /tmp/vellum-assistant
mkdir -p .claude/skills
cp -r /tmp/vellum-assistant/assistant/src/config/bundled-skills/screen-annotation .claude/skills/screen-annotation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Screen Annotation 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 Screen Annotation 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 Screen Annotation 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.

Drawing on the screen the user is showing you, so they can go and do the thing themselves.

This is the opposite errand from computer use. Nothing here clicks, types or drives anything: the marks are a way of pointing while you talk, for someone who wants to learn where a control is rather than have it operated for them. A mark is drawn clear of what it indicates and never takes the mouse, so what you point at stays visible and clickable the whole time.

Requires a screen share

Marks are drawn on the frame around the surface the user is sharing with the call. With nothing shared there is nowhere to draw, and screen_point_at fails saying so. Ask them to share their screen from the call, then point.

Say what to point at

Name the thing. {"target": "color balance", "caption": "Click this"}. The name is looked up on the surface itself, which knows where its controls actually are, and an arrow is drawn at it.

The label, not a description of it. What is matched is the control's own name. Casing, spacing and punctuation are forgiven, so Color Balance finds color balance; nothing beyond that is, so "the stabilization button" finds nothing, because no control is called that. Give the label on its own: stabilization, Send, Search.

The arrow points at the middle of the control and stops just short, so what you are sending someone to stays visible the whole time.

You are answered with what was drawn and the name it resolved to, which is not always the name you asked for. Say the resolved one out loud: it is the word the user can see.

A name the surface does not carry draws nothing and comes back with the names it does carry. That is the answer, not a setback: the thing is nearly always one of those, so read the list and point again. Never fall back to coordinates for a control you could not find. A mark drawn at a guess is worse than no mark, because someone follows it; the words you say are the better tool for a thing you cannot point at. What the user calls something and what the surface calls it often differ, which is what the list is for: they may say "white balance" where the control reads color balance, or "the stabilization button" where it reads stabilization.

Coordinates, for an extent

For when the size of the thing is the message rather than where it is: a region of an image, an area of a canvas, a panel spoken of as a whole. These draw a ring around the bounds instead of an arrow at a place.

Fractions of the shared surface, 0 to 1, measured against the picture of that surface you were last shown. x and y are the top-left corner, width and height the size. Give the bounds of the thing itself: the ring is drawn around them, so a box tight on a button reads as a ring around that button, and a box drawn where you think the ring should go puts the ring outside that instead.

These are a guess measured off a picture that has been scaled on its way to you, and they are only as fresh as the last frame you were sent. If the user has scrolled or moved a window since, say what you are pointing at as well as drawing it, so a mark that has drifted is still recoverable in words.

Moving the share is the one kind of drift that is caught for you. A mark measured against the surface before the move is refused rather than drawn, because those fractions land somewhere arbitrary on the surface that replaced it. Wait for a frame of the new one and point again.

How to point

One thing at a time. A mark is where to look next. A screen with four marks on it is not four times as helpful; it is a diagram, and nobody knows which one to start with. Point at the current step, talk, then point at the next one.

Captions are imperatives, not explanations. "Click Share", "Type the name here", "This is the tempo". Whatever else needs saying, say out loud: the caption is drawn over the user's own work in a window they cannot scroll or dismiss, and it is capped at 80 characters for that reason.

Say it as well as draw it. The marks are a gesture that accompanies speech, the way a person points while explaining. A mark with no words is a riddle.

Take them down when they stop being true. Call screen_clear_marks when the step is done, when the user has moved on, or when the conversation has left the screen behind. Marks come down on their own if the share ends or moves, but a mark left standing over a finished step is one the user has to work out is stale.

Walking someone through several steps

Sometimes the answer is one pointer. Sometimes it is a route: four places to click, in order, before the thing they asked about happens. Decide which it is before you draw anything, because the two are paced differently.

Say the route before you start it. "There are three steps. First the Share menu, then the format, then Export." If they asked how and you inferred they want to be walked through it rather than told, this is where they wave it off and just want the answer. Keep it to the count and the landmarks; the detail belongs to each step as you reach it.

One step, then stop. Point at it, say what to do, and then wait. The temptation is to narrate the next step while the mark for this one is still up, and that leaves them doing step one with instructions for step two in their ear. Silence is the cue that it is their turn.

Advance on evidence, not on time. Move to the next step when a fresh picture shows this one done, or when they tell you it is. Do not move on because a plausible amount of time has passed.

The most direct telling is automatic. When the user clicks the control an arrow is pointing at, a message arrives as their turn saying they clicked it, by name, and that mark comes down on its own. Treat it as the step done: say what comes next and point at it. Nothing arrives for a click anywhere else, and nothing arrives for a ring, so for those the picture and their words are still the evidence. If the next picture shows the step not done, or done to the wrong thing, point at the same place again with a shorter caption and say what you saw. Pointing at the next step while the previous one is still open is how someone ends up two steps behind a mark.

Going back is just pointing again. There is no undo. If they went past something, or want to see step two again, point at step two. Say which step it is, so the words and the mark agree about where you both are.

Close it out. When the last step is done, clear the marks and say so, in a word. A mark left on the final button is one the user has to work out is stale, and a walkthrough that ends without an ending leaves them waiting for step five of four.

Shapes

A mark is either an arrow at a place or a ring around an extent, and naming a control gives you the arrow. Reach for the ring only when the extent is the thing being said: "this whole panel", "this part of the picture". A ring around one button says something about where that button ends, which is rarely what you mean and is the part most likely to be wrong.

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 Screen Annotation AI skill do?

Point at things on the screen the user is showing you

Why use Screen Annotation on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vellum-ai/vellum-assistant/tree/main/assistant/src/config/bundled-skills/screen-annotation. 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 Screen Annotation?

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 Screen Annotation?

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

Is the Screen Annotation AI skill free?

Yes. It is published on GitHub by vellum-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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