Scienceworld Task Focuser logo

Scienceworld Task Focuser

OrganizationPopular
zjunlp
scienceworld-task-focuser

Use when the agent needs to direct attention to a specific object in the environment or inventory before performing a critical action such as measuring, using, or connecting. This preparatory "focus on" step signals intent to the ScienceWorld system, often unlocking required state changes for subsequent interactive steps in experimental procedures.

Overview

Publisherzjunlp
RepositorySkillNet
Skill namescienceworld-task-focuser
Stars
1.4K
Forks
152
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Scienceworld Task Focuser 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-task-focuser
  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-task-focuser .claude/skills/scienceworld-task-focuser
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scienceworld Task Focuser 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 Task Focuser 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 Task Focuser 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: scienceworld-task-focuser

Purpose

Formally declare intent to interact with a specific object in the ScienceWorld environment. The focus on action is a required preparatory step that signals to the system which object subsequent actions (use, measure, connect) will target, often unlocking necessary state changes or measurement capabilities.

When to Use

  • Before using a measurement tool (e.g., thermometer) on a target object
  • Before complex operations such as connecting, mixing, or combining objects
  • When a task description explicitly instructs "focus on" an object
  • To confirm the correct item is in inventory before proceeding with an action

Workflow

  1. Identify the target object -- Determine the exact name as it appears in the environment (e.g., thermometer, metal fork).
  2. Locate the object -- Use look around or inventory to confirm the object's location (environment or inventory).
  3. Execute the focus action -- Issue the appropriate command:
    • Object in the environment: focus on <OBJECT_NAME>
    • Object in inventory: focus on <OBJECT_NAME> in inventory
  4. Verify confirmation -- Wait for the system response: "You focus on the <OBJECT_NAME>." before proceeding to the next step.

Examples

Example 1: Preparing to measure temperature of a metal fork

> focus on thermometer in inventory
You focus on the thermometer.

> focus on metal fork in inventory
You focus on the metal fork.

> use thermometer on metal fork
The thermometer measures a temperature of 23 degrees celsius.

Example 2: Focusing on an environmental object before moving it

> look around
You see: a table, a blue box, a red box, a metal fork (on table).

> focus on metal fork
You focus on the metal fork.

> move metal fork to blue box
You move the metal fork to the blue box.

Important Notes

  • This skill does not perform the main action (e.g., measuring). It only prepares for it.
  • The focus action has no physical effect on the object; it is a declarative meta-action.
  • Always wait for the confirmation observation before attempting the next step.
  • If the object is not found, use look around, examine, or teleport to to locate it before attempting to focus.

Frequently asked questions

What does the Scienceworld Task Focuser AI skill do?

Use when the agent needs to direct attention to a specific object in the environment or inventory before performing a critical action such as measuring, using, or connecting. This preparatory "focus on" step signals intent to the ScienceWorld system, often unlocking required state changes for subsequent interactive steps in experimental procedures.

Why use Scienceworld Task Focuser on TypingMind?

Because you install it once and use it with any model. Scienceworld Task Focuser 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 Task Focuser in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zjunlp/SkillNet/tree/main/experiments/src/skills/scienceworld/scienceworld-task-focuser. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Scienceworld Task Focuser?

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 Task Focuser?

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

Is the Scienceworld Task Focuser 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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