scienceworld-task-parser

scienceworld-task-parser is a skill for Claude Code, Codex from zjunlp/SkillNet. It costs 44 tokens per session (698 once invoked), scanned A, original, MIT.

A task-planning step for ScienceWorld, a simulated environment for science experiments. It reads a natural-language instruction and identifies the objects, destination, and actions needed.

In plain words
What is it for?
Use it when a new ScienceWorld task asks you to find, move, pour, mix, or otherwise manipulate objects.
Why use it?
It removes the need to work out an experiment's requirements from scratch while acting in the environment. This helps turn a broad instruction into an actionable sequence.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

About the project

SkillNet is infrastructure for finding, creating, evaluating, combining, and coordinating reusable capabilities for AI agents. Agent developers use it as a searchable and installable library of skills and as a system for selecting skills for particular tasks. The catalogue contains skills that can be discovered, installed, or used with SkillNet.

zjunlp/SkillNet · 1,253 stars · on GitHub · skillnet.openkg.cn

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/zjunlp/skillnet/scienceworld-task-parser
Any agent
npx skills add zjunlp/SkillNet --skill scienceworld-task-parser
Clone the repo
git clone --depth 1 https://github.com/zjunlp/SkillNet

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for scienceworld-task-parser

README.md
[![agentmods](https://agentmods.dev/badge/skills/zjunlp/skillnet/scienceworld-task-parser.svg)](https://agentmods.dev/skills/zjunlp/skillnet/scienceworld-task-parser)
Your own site
<a href="https://agentmods.dev/skills/zjunlp/skillnet/scienceworld-task-parser"><img src="https://agentmods.dev/badge/skills/zjunlp/skillnet/scienceworld-task-parser.svg" alt="Measured on agentmods" height="20"></a>
Per session 44 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 698 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00044 $0.00698
Opus 5 $0.00022 $0.00349
Sonnet 5 $0.00009 $0.00140
Haiku 4.5 $0.00004 $0.00070

Measured 2d ago against content hash 8577631125aa, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

scienceworld-task-parser scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

experiments/src/skills/scienceworld/scienceworld-task-parser/SKILL.md · 45 lines

How it starts

The opening of the file, as written. The whole thing — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 45 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 45 lines · 44 tokens per session scan A 8577631125aa

Subscribe to this mod's changes

scienceworld-task-parser is a skill published in the GitHub repository zjunlp/SkillNet (1,253 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 698 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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