Scienceworld Room Explorer logo

Scienceworld Room Explorer

OrganizationPopular
zjunlp
scienceworld-room-explorer

Performs an initial survey of a room to identify all visible objects, containers, devices, and their states using the 'look around' action. Use this skill when you first enter a new location, need to refresh your understanding of the environment, or must identify available resources before planning a sequence of actions. Returns a detailed observation that serves as foundational context for subsequent tasks.

Overview

Publisherzjunlp
RepositorySkillNet
Skill namescienceworld-room-explorer
Stars
1.4K
Forks
152
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 zjunlp on GitHub. Read the source before you install it.

Installation

Install the Scienceworld Room Explorer 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-room-explorer
  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-room-explorer .claude/skills/scienceworld-room-explorer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scienceworld Room Explorer 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 Room Explorer 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 Room Explorer 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.

Instructions

Purpose

Use this skill to establish a foundational understanding of your immediate environment. It is the critical first step for any task requiring object interaction, inventory assessment, or spatial reasoning.

When to Use

  1. Upon entering a new room.
  2. When the task requires identifying available resources.
  3. Before planning a complex sequence of actions (e.g., building a circuit, mixing chemicals).
  4. If the state of the environment may have changed (e.g., after using an object, or if a long time has passed).

Core Action

Execute the look around action. This is the only action required for this skill.

Output Processing

  1. The observation returned by look around is your primary output.
  2. Parse this observation to create a mental inventory. Key elements to note:
    • Room Name: The identified location.
    • Objects: List all items, containers, and devices.
    • Object States: Note if devices are on/off, containers are open/closed, or if objects contain other items.
    • Connections: Note any pre-existing wire connections or object placements.
    • Exits: Identify doors to other rooms and their state (open/closed).

Example

Scenario: Agent teleports to the kitchen and needs to identify available resources.

  1. teleport to kitchen
  2. look around
  3. Observation: "This room is called the kitchen. In it, you see: a fridge (containing chocolate, milk), a metal pot, a thermometer, a counter, a stove (which is turned off). You also see: a door to the hallway (which is open)."
  4. Parsed inventory: fridge (open, contains chocolate and milk), metal pot, thermometer, counter, stove (off), exit to hallway.

Integration with Other Skills

The observation generated by this skill is essential context for downstream tasks. Before performing actions like pick up, connect, or use, always ensure your understanding of object locations and states is current by running this skill if needed.

Do not use this skill for examining specific objects in detail; use the examine OBJ or look at OBJ actions for that purpose.

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 Room Explorer AI skill do?

Performs an initial survey of a room to identify all visible objects, containers, devices, and their states using the 'look around' action. Use this skill when you first enter a new location, need to refresh your understanding of the environment, or must identify available resources before planning a sequence of actions. Returns a detailed observation that serves as foundational context for subsequent tasks.

Why use Scienceworld Room Explorer on TypingMind?

Because you install it once and use it with any model. Scienceworld Room Explorer 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 Room Explorer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zjunlp/SkillNet/tree/main/experiments/src/skills/scienceworld/scienceworld-room-explorer. 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 Room Explorer?

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 Room Explorer?

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

Is the Scienceworld Room Explorer 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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