alfworld-goal-interpreter skillA
alfworld-goal-interpreter is agent-read markdown (skill) from zjunlp/skillnet: 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)..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# 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 …
Read the whole file at its exact version.
How to install
mdr add zjunlp/skillnet/alfworld-goal-interpreter@git:20260307.388baa3mdr add zjunlp/skillnet/alfworld-goal-interpreter@sha256:5459842fff5b2a2bPin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_5kwsno74o4cmvjxd)
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Versions
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (2302 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
zjunlp/skillnet · 1,266 stars · license MIT · pushed 2026-09-25 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_5kwsno74o4cmvjxd GET https://markdownregistry.com/api/v1/resolve?ref=zjunlp/skillnet/alfworld-goal-interpreter GET https://markdownregistry.com/api/v1/blob/5459842fff5b2a2b76e4827456774e55d383abc615ea179d65526c2257b7621c
Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.