scienceworld-task-parser

Name: scienceworld-task-parser
Availability: InStock
Author: TaoMiao

Parses natural language instructions into executable ScienceWorld task plans.

145|1|Updated Mar 16, 2026

Search Tags:#AI agent #task planning #natural language #instruction parsing #ScienceWorld #environment understanding

Ready to run

npx skills add https://github.com/taomiao/DynamicSkillCompiler --skill scienceworld-task-parser-taomiao

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: scienceworld-task-parser
Source: https://github.com/taomiao/DynamicSkillCompiler/tree/main/experiments/src/skills/scienceworld/scienceworld-task-parser
Command: npx skills add https://github.com/taomiao/DynamicSkillCompiler --skill scienceworld-task-parser-taomiao

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill enables AI agents to interpret and parse user instructions in ScienceWorld environments, transforming natural language tasks into structured action plans.

Core Features & Use Cases

Instruction Analysis: Extracts key task components such as target object, location, and required actions from user commands.
Environmental Survey: Guides the agent to observe the environment and identify relevant objects or locations needed for task execution.
Execution Planning: formulates a step-by-step plan based on common instruction patterns for efficient task completion in simulated science environments.
Use Case: When given a new task in ScienceWorld, the agent can automatically analyze the instruction, observe surroundings, and perform the necessary sequence to complete the task.

Quick Start

Use the scienceworld-task-parser skill to analyze the instruction "Move the red wire to the lab table in the workshop."

Frequently Asked Questions about scienceworld-task-parser

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I parse natural language instructions for ScienceWorld tasks?▼

To parse natural language instructions for ScienceWorld tasks, the skill extracts target objects, locations, and required actions from user commands to formulate structured, executable operation plans. It uses pattern recognition to handle object manipulation, device activation, and mixture creation instructions.

What is instruction parsing in science simulation environments?▼

Instruction parsing in science simulation environments is the process of transforming complex natural language tasks into structured action plans. It enables AI agents to interpret user commands, observe surroundings, and perform necessary sequences to complete tasks like moving objects or activating devices.

How do I create an execution plan from natural language commands in ScienceWorld?▼

To create an execution plan from natural language commands in ScienceWorld, analyze the instruction to extract key task components, survey the environment for relevant objects, and formulate a step-by-step plan based on common instructional patterns for efficient task completion.

Can I use this task parser for object manipulation and mixture creation in ScienceWorld?▼

Yes, the task parser applies across diverse instructional patterns for object manipulation, device activation, and mixture creation within the ScienceWorld science simulation environment. It recognizes natural language patterns to deliver structured task plans for these specific action types.

Do I need natural language pattern recognition to automate environment interaction in ScienceWorld?▼

Yes, natural language pattern recognition and environment understanding modules are required to deliver structured task plans for ScienceWorld. These modules enable the agent to interpret instructions, observe surroundings, and perform the necessary sequence to complete tasks automatically.

What are the limitations of parsing natural language for science simulation tasks?▼

Parsing natural language for science simulation tasks depends on recognizing common instructional patterns. It requires the agent to observe and identify relevant objects or locations in the environment, meaning tasks with ambiguous instructions or missing environmental cues may not parse correctly into executable plans.