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Labelu Data Annotation

CommunityPopular
Devin-AXIS
labelu-data-annotation

Open the independent Data Annotation Training Cloud workbench and read saved project status when explicitly requested.

Overview

PublisherDevin-AXIS
RepositoryiPolloWork
Skill namelabelu-data-annotation
Stars
6.9K
Forks
1.1K
Bundled files
Instructions only
Links
Skill sourceRepositoryHomepage
  • 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 Devin-AXIS on GitHub. Read the source before you install it.

Installation

Install the Labelu Data 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.
    https://github.com/Devin-AXIS/iPolloWork/tree/main/examples/plugin-packages/labelu-data-annotation/skills/labelu-data-annotation
  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/Devin-AXIS/iPolloWork.git /tmp/iPolloWork
mkdir -p .claude/skills
cp -r /tmp/iPolloWork/examples/plugin-packages/labelu-data-annotation/skills/labelu-data-annotation .claude/skills/labelu-data-annotation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Labelu Data 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 Labelu Data 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 Labelu Data 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.

Data Annotation Training Cloud

Use this plugin when the user wants to open 数据标注实训云, annotate an image, video, audio, or text, or inspect annotations already saved there.

Open the training workbench

  1. Do not search the workspace for media, ask for a file path, inspect an attachment, or create an annotation project from chat.
  2. Open the Data Annotation workspace app from the sidebar or right-side console. Its existing MCP AppBridge calls open-workbench and embeds the returned local URL in an iframe.
  3. Do not call browser tools; the workspace app owns opening the workbench.
  4. Do not paste, repeat, summarize, or ask the user to click the signed local URL in chat.
  5. When the workbench is visibly open, tell the user it is ready. The user either uploads their own image, video, or audio, enters text, imports a text-layer PDF, or selects a bundled training project with prepared media and labels, then annotates and saves it inside the visual workbench.

Help through conversation

  1. Only use these actions after the user explicitly asks to list, read, summarize, or count saved projects. Do not use them merely to open the workbench.
  2. If the user did not identify a project, call list-projects and use the returned modality, title, counts, and timestamps to resolve the intended saved project. Do not guess when multiple projects are plausible.
  3. Call get-project before reasoning about one project. Treat its revision as the saved version.
  4. Answer only from saved annotations. The conversational side is read-only in this version and must never alter, upload, replace, or automate annotations in the right-side page.

Boundaries

  • The chat side never selects or uploads media and never creates a project. It may only read saved project status when the user explicitly asks.
  • Do not use browser automation after opening the page unless the user explicitly asks for it.
  • The workbench supports user-driven image, video, audio, and text annotation, text-layer PDF import, bundled training projects, and saved-project continuation. PDF import extracts text only; scanned PDFs require OCR first. Do not claim review, assignment, automatic AI annotation, or multi-user workflow support.

Frequently asked questions

What does the Labelu Data Annotation AI skill do?

Open the independent Data Annotation Training Cloud workbench and read saved project status when explicitly requested.

Why use Labelu Data Annotation on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Devin-AXIS/iPolloWork/tree/main/examples/plugin-packages/labelu-data-annotation/skills/labelu-data-annotation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Labelu Data 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 Labelu Data Annotation?

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

Is the Labelu Data Annotation AI skill free?

It is published on GitHub by Devin-AXIS. 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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