Agent Skillszjunlp/SkillNet › scienceworld-task-parser

scienceworld-task-parser

GitHub

用于解析ScienceWorld环境中的自然语言任务指令,提取目标对象、位置及动作序列,并指导智能体执行环境调查与任务操作。

experiments/src/skills/scienceworld/scienceworld-task-parser/SKILL.md zjunlp/SkillNet

Trigger Scenarios

接收ScienceWorld环境下的新任务指令 需要规划物体交互和移动路径

Install

npx skills add zjunlp/SkillNet --skill scienceworld-task-parser -g -y
More Options

Non-standard path

npx skills add https://github.com/zjunlp/SkillNet/tree/main/experiments/src/skills/scienceworld/scienceworld-task-parser -g -y

Use without installing

npx skills use zjunlp/SkillNet@scienceworld-task-parser

指定 Agent (Claude Code)

npx skills add zjunlp/SkillNet --skill scienceworld-task-parser -a claude-code -g -y

安装 repo 全部 skill

npx skills add zjunlp/SkillNet --all -g -y

预览 repo 内 skill

npx skills add zjunlp/SkillNet --list

SKILL.md

Frontmatter
{
    "name": "scienceworld-task-parser",
    "description": "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."
}

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

Version History

  • 7bb9a48 Current 2026-07-25 11:54

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