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Scienceworld Living Entity Identifier

OrganizationPopular
zjunlp
scienceworld-living-entity-identifier

Analyzes room observations to identify potential living things (e.g., eggs, plants, animals) among listed objects. Use this skill when a task involves finding, focusing on, or interacting with a living thing, biological entity, or organism. Processes observation text, flags candidate living items based on domain knowledge, and outputs a focused target for subsequent actions like focus on or pick up.

Overview

Publisherzjunlp
RepositorySkillNet
Skill namescienceworld-living-entity-identifier
Stars
1.4K
Forks
152
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

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

  • Open source

    Published by zjunlp on GitHub. Read the source before you install it.

Installation

Install the Scienceworld Living Entity Identifier 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/zjunlp/SkillNet/tree/main/experiments/src/skills/scienceworld/scienceworld-living-entity-identifier
  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/zjunlp/SkillNet.git /tmp/SkillNet
mkdir -p .claude/skills
cp -r /tmp/SkillNet/experiments/src/skills/scienceworld/scienceworld-living-entity-identifier .claude/skills/scienceworld-living-entity-identifier
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scienceworld Living Entity Identifier 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 Scienceworld Living Entity Identifier 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 Scienceworld Living Entity Identifier 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.

Skill: Living Entity Identifier

Purpose

Identify living things (eggs, plants, animals) from room observations and focus on them for task progression.

Core Workflow

  1. Survey Room: look around to get the observation text listing all objects.
  2. Identify Candidates: Scan the object list for living entity indicators:
    • Animals: dove, giant tortoise, bee, frog, fish
    • Eggs: dove egg, chicken egg, turtle egg
    • Plants: flower, tree, moss, fern, algae
    • Other biological: mushroom, seed, pollen
  3. Focus on Target: focus on [IDENTIFIED_OBJECT] to signal task progress.
  4. Transport (if needed): pick up [OBJECT] then move [OBJECT] to [CONTAINER].

Key Actions

ActionPurpose
look aroundSurvey room for objects
teleport to LOCMove to rooms with biological likelihood
focus on OBJSignal identification to task system
pick up OBJAcquire entity for transport

Example

Task: "Find a living thing in the environment."

  1. teleport to outside
  2. look around — observation lists: "a dove egg", "a rock", "soil"
  3. Identify "dove egg" as the living entity candidate
  4. focus on dove egg
  5. If transport required: pick up dove egg then move dove egg to blue box

Important Notes

  • Prioritize explicit living things (e.g., "dove egg," "giant tortoise") over ambiguous substances (e.g., "air," "water").
  • If the current room lacks candidates, teleport to rooms with higher biological likelihood: outside, greenhouse, bedroom.
  • Always use focus on immediately after identification — it signals task progress.

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 Scienceworld Living Entity Identifier AI skill do?

Analyzes room observations to identify potential living things (e.g., eggs, plants, animals) among listed objects. Use this skill when a task involves finding, focusing on, or interacting with a living thing, biological entity, or organism. Processes observation text, flags candidate living items based on domain knowledge, and outputs a focused target for subsequent actions like focus on or pick up.

Why use Scienceworld Living Entity Identifier on TypingMind?

Because you install it once and use it with any model. Scienceworld Living Entity Identifier 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 Scienceworld Living Entity Identifier in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zjunlp/SkillNet/tree/main/experiments/src/skills/scienceworld/scienceworld-living-entity-identifier. 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 Scienceworld Living Entity Identifier?

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 Scienceworld Living Entity Identifier?

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

Is the Scienceworld Living Entity Identifier AI skill free?

Yes. It is published on GitHub by zjunlp 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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