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alfworld-goal-interpreter

Agent Building
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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).

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How to use this skill

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  1. Open your project in Codex.
  2. Copy the prompt below and paste it into your agent.
  3. Review the proposed files and risks before you approve installation.
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I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/taomiao/DynamicSkillCompiler/blob/HEAD/experiments/src/skills/alfworld/alfworld-goal-interpreter/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/alfworld-goal-interpreter/. Do not write files or run scripts until I approve.

After I approve, install the complete skill folder, including required referenced files, into that project location. Verify it is discoverable, then tell me its actual invocation name and how to use it. Do not claim it is installed until you have verified it.

Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

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.