How do I install the Alfworld Goal Interpreter skill?

Run npx skillmds add zjunlp/alfworld-goal-interpreter in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Alfworld Goal Interpreter safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Alfworld Goal Interpreter?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Alfworld Goal Interpreter free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Alfworld Goal Interpreter?

zjunlp (@zjunlp) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Alfworld Goal Interpreter by zjunlp

Name: Alfworld Goal Interpreter
Rating: 4.5 (4 reviews)
Author: zjunlp

Alfworld Goal Interpreter

Parses the natural language task goal to extract actionable sub-objectives and required objects. Trigger this skill whenever a new task is assigned to break down complex instructions into clear, sequential targets. It interprets phrases like 'look at X under Y' to identify target objects (pillow), reference objects (desklamp), and spatial relationships (under).

zjunlp 41570dd 2 files · 4.3 KB Updated Sep 10, 2026

File contents

Goal Interpretation Protocol

1. Parse the Task Statement

When a new task is assigned, immediately analyze the natural language instruction to extract its core components. Use the parsing script (parse_goal.py) to perform this analysis.

Input: The raw task string (e.g., "look at pillow under the desklamp"). Output: A structured dictionary containing:

primary_target: The main object to interact with.
reference_object: The object that defines a location or condition.
spatial_relation: The preposition linking them (e.g., under, on, in).
action: The verb defining the required interaction.

2. Generate Sub-Objectives

Based on the parsed components, formulate a clear, sequential plan. The plan must account for the spatial relationship.

Example Logic:

IF relation is under → Sub-goal 1: Locate the reference_object. Sub-goal 2: Inspect the area beneath it for the primary_target.
IF relation is in or on → Sub-goal 1: Locate the reference_object. Sub-goal 2: Check its contents/surface.

3. Identify Required Objects & Actions

Map the parsed objects to the available action types in the environment. The primary action verbs (look, take, use, etc.) from the task must be translated into the agent's available action set (go to, take, use, etc.).

Critical Check: If the reference_object is a container (drawer, fridge), ensure the plan includes the open action before inspection.

4. Execute & Adapt

Initiate the plan. After each action, monitor the observation. If the expected object is not found, or the action fails ("Nothing happened"), consult the fallback logic in the reference guide (search_patterns.md).

Remember: The agent must maintain object permanence. If an object is moved (e.g., a pillow is picked up), it is now in the agent's inventory and the spatial relationship is void.

---name: alfworld-goal-interpreterdescription: Parses the natural language task goal to extract actionable sub-objectives and required objects. Trigger this skill whenever a new task is assigned to break down complex instructions into clear, sequential targets. It interprets phrases like 'look at X under Y' to identify target objects (pillow), reference objects (desklamp), and spatial relationships (under).---# Goal Interpretation Protocol## 1. Parse the Task StatementWhen a new task is assigned, immediately analyze the natural language instruction to extract its core components. Use the parsing script (`parse_goal.py`) to perform this analysis.**Input:** The raw task string (e.g., "look at pillow under the desklamp").**Output:** A structured dictionary containing:- `primary_target`: The main object to interact with.- `reference_object`: The object that defines a location or condition.- `spatial_relation`: The preposition linking them (e.g., under, on, in).- `action`: The verb defining the required interaction.## 2. Generate Sub-ObjectivesBased on the parsed components, formulate a clear, sequential plan. The plan must account for the spatial relationship.**Example Logic:**- **IF** relation is `under` → Sub-goal 1: Locate the `reference_object`. Sub-goal 2: Inspect the area beneath it for the `primary_target`.- **IF** relation is `in` or `on` → Sub-goal 1: Locate the `reference_object`. Sub-goal 2: Check its contents/surface.## 3. Identify Required Objects & ActionsMap the parsed objects to the available action types in the environment. The primary action verbs (look, take, use, etc.) from the task must be translated into the agent's available action set (go to, take, use, etc.).**Critical Check:** If the `reference_object` is a container (drawer, fridge), ensure the plan includes the `open` action before inspection.## 4. Execute & AdaptInitiate the plan. After each action, monitor the observation. If the expected object is not found, or the action fails ("Nothing happened"), consult the fallback logic in the reference guide (`search_patterns.md`).**Remember:** The agent must maintain object permanence. If an object is moved (e.g., a pillow is picked up), it is now in the agent's inventory and the spatial relationship is void.

Frequently asked questions