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Scienceworld Task Parser

OrganizationPopular
zjunlp
scienceworld-task-parser

Analyzes user instructions in ScienceWorld environments to extract specific task requirements and constraints. Use when receiving a new task to identify required objects, target locations, and action sequences before taking any environment actions.

Overview

Publisherzjunlp
RepositorySkillNet
Skill namescienceworld-task-parser
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 Task Parser 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-parser
  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-parser .claude/skills/scienceworld-task-parser
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scienceworld Task Parser 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 Parser 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 Parser 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 for ScienceWorld Task Parsing

When you receive a new task instruction in a ScienceWorld environment, follow this process to parse it into a structured plan.

1. Parse the Instruction

Immediately analyze the user's natural language instruction. Extract the following core components:

  • Target Object Type: Identify the category or description of the object to be manipulated (e.g., 'non-living thing', 'liquid', 'electrical component').
  • Target Location: Identify the final destination for the object, including the room and specific container (e.g., 'purple box in the workshop').
  • Required Actions: Infer the sequence of actions implied by verbs like "find", "focus on", "move", "pour", "mix", etc.

Output your analysis as a concise thought. Example: "Task requires finding a non-living object in the workshop and moving it to the purple box."

2. Survey the Environment

  • Use look around in your current room to get an inventory of visible objects, containers, and their states.
  • If the target location is a different room, use teleport to LOC to go there first, then look around.

3. Identify the Target Object

  • From the room description, identify objects matching the parsed Target Object Type.
  • If multiple candidates exist, select one that is clearly non-living, portable, and not part of a fixed apparatus (e.g., a wire, a light bulb, a battery). Avoid substances like 'air'.
  • Use examine OBJ or look at OBJ if you need more detail to confirm an object's properties.

4. Execute the Task Sequence

  1. Signal Intent: Use focus on OBJ on the identified target object. This explicitly marks the object for the task.
  2. Perform Core Action: Execute the primary action from the parsed instruction (e.g., move OBJ to OBJ, pour OBJ into OBJ).
  3. Use wait or wait1 only if necessary to allow for state changes.

Key Principles

  • Efficiency: All containers are pre-opened. Do not use open or close unless explicitly required.
  • Directness: Teleport directly to the target room. Do not explore unrelated rooms.
  • Clarity: Structure your internal reasoning using the "Thought:" prefix before each action, as shown in the trajectory.
  • Verification: If an initial look around is insufficient, a second look around is acceptable to confirm the environment state before proceeding.

Example

Task instruction: "Find a non-living thing in the workshop and move it to the purple box."

  1. Parse: Target = non-living object, Location = workshop, Container = purple box.
  2. teleport to workshop
  3. look around — observe: "a battery, a blue light bulb, an orange wire..."
  4. Select: battery (non-living, portable).
  5. focus on battery
  6. move battery to purple box

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 Task Parser AI skill do?

Analyzes user instructions in ScienceWorld environments to extract specific task requirements and constraints. Use when receiving a new task to identify required objects, target locations, and action sequences before taking any environment actions.

Why use Scienceworld Task Parser on TypingMind?

Because you install it once and use it with any model. Scienceworld Task Parser 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 Parser 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-parser. 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 Task Parser?

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 Parser?

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

Is the Scienceworld Task Parser 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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