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Agent skill

Scienceworld Task Parser

by zjunlp in zjunlp/SkillNet

Analyzes user instructions in ScienceWorld environments to extract specific task requirements and constraints.

MITAuto-check passed

Install Scienceworld Task Parser

skills CLI
$ npx skills add zjunlp/SkillNet --skill scienceworld-task-parser -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install zjunlp/SkillNet scienceworld-task-parser --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/zjunlp/SkillNet.git skills-src && mkdir -p .claude/skills && cp -r skills-src/experiments/src/skills/scienceworld/scienceworld-task-parser .claude/skills/scienceworld-task-parser && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
scienceworld-task-parser
GitHub stars
1.4k
Token cost
~777 tokens
SKILL.md length
414 words
Files
2 (incl. references)
Skills in repo
122
Repo updated
First seen
Licence
MIT

At a glance

Analyzes user instructions in ScienceWorld environments to extract specific task requirements and constraints.

  • Works in 4 steps: Parse the Instruction → Survey the Environment → Identify the Target Object → …
  • Receiving a new task to identify required objects
  • SKILL.md covers 1. Parse the Instruction, 2. Survey the Environment, 3. Identify the Target Object and 4. Execute the Task Sequence, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Scienceworld Task Parser is an agent skill from zjunlp/SkillNet. 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.

Its SKILL.md is about 780 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/task_patterns.md`).

The repository describes itself as: Create, Evaluate, and Connect AI Skills. The licence is MIT.

When your agent uses it

  • Receiving a new task to identify required objects
  • Target locations
  • Action sequences before taking any environment actions

Example prompts

  • “Use the scienceworld-task-parser skill to analyz user instructions in ScienceWorld environments to extract specific task requirements and constraints”
  • “/scienceworld-task-parser”

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Parse the Instruction
  2. Survey the Environment
  3. Identify the Target Object
  4. Execute the Task Sequence

What it can do on your machine

Read from SKILL.md and the folder at commit 3fcebf8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scienceworld Task Parser loads about 777 tokens when it runs, and up to ~1.3k if it reads all its reference files. Until then it costs about 68 tokens; SKILL.md has 414 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~68
When it runs · the whole SKILL.md, loaded when a task matches
~777
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from zjunlp/SkillNet at commit 3fcebf8, republished under its MIT licence (© zjunlp). 414 words, ~777 tokens.

Download SKILL.mdSave it as .claude/skills/scienceworld-task-parser/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
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.
Show full SKILL.md (184 more words)Show less

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

© zjunlp, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in experiments/src/skills/scienceworld/scienceworld-task-parser of zjunlp/SkillNet.

  • SKILL.md
  • references/task_patterns.md

Open the folder on GitHubat commit 3fcebf8

Compare with similar skills

Scienceworld Task Parser next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Scienceworld Task Parser compared with similar skills
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Scienceworld Task Parser this skillzjunlp/SkillNet1.4k—~777Automated safety check: PassMIT
Flox Environmentsaffaan-m/ECC275k2 repos~3.5kAutomated safety check: NotesMIT
Extractalirezarezvani/claude-skills28k—~1.4kAutomated safety check: PassMIT
Remote Environmentsasgeirtj/system_prompts_leaks69k—~1.4kAutomated safety check: PassCC0-1.0
Brand Extractnexu-io/open-design100k—~3.1kAutomated safety check: PassApache-2.0
Design Extractnexu-io/open-design100k—~549Automated safety check: PassApache-2.0

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Questions about Scienceworld Task Parser

What does Scienceworld Task Parser do?

Analyzes user instructions in ScienceWorld environments to extract specific task requirements and constraints. Scienceworld Task Parser is an agent skill from zjunlp/SkillNet. Analyzes user instructions in ScienceWorld environments to extract specific task requirements and constraints.

When should I use Scienceworld Task Parser?

Scienceworld Task Parser fits situations like: receiving a new task to identify required objects; target locations; action sequences before taking any environment actions.

How do I install Scienceworld Task Parser in Claude Code?

Run `npx skills add zjunlp/SkillNet --skill scienceworld-task-parser -a claude-code`. Or copy the skill folder (experiments/src/skills/scienceworld/scienceworld-task-parser in zjunlp/SkillNet) into .claude/skills/scienceworld-task-parser in your project. Claude Code loads it when a task matches its description.

How do I install Scienceworld Task Parser in Codex?

Run `npx skills add zjunlp/SkillNet --skill scienceworld-task-parser -a codex`. Or copy the skill folder (experiments/src/skills/scienceworld/scienceworld-task-parser in zjunlp/SkillNet) into .agents/skills/scienceworld-task-parser in your project. Codex loads it when a task matches its description.

Can I use Scienceworld Task Parser in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zjunlp/SkillNet --skill scienceworld-task-parser -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scienceworld-task-parser, .gemini/skills/scienceworld-task-parser, .github/skills/scienceworld-task-parser and .opencode/skills/scienceworld-task-parser in your project.

What does Scienceworld Task Parser need to run?

SKILL.md names no scripts, command-line tools or credentials: Scienceworld Task Parser is instructions for the agent only.

Does Scienceworld Task Parser access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Scienceworld Task Parser safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scienceworld Task Parser use?

Scienceworld Task Parser is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scienceworld Task Parser use?

About 777 tokens (SKILL.md is roughly 3.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 537 tokens, read only when the agent opens those files.

What are the alternatives to Scienceworld Task Parser?

Skills that share tags, products or a category with Scienceworld Task Parser: Flox Environments (affaan-m/ECC, 275k stars), Extract (alirezarezvani/claude-skills, 28k stars), Remote Environments (asgeirtj/system_prompts_leaks, 69k stars) and Brand Extract (nexu-io/open-design, 100k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scienceworld Task Parser?

zjunlp (a GitHub organization) maintains it in zjunlp/SkillNet, which has 1,393 GitHub stars. The repository holds 122 skills in this directory. The repository was last updated on October 7, 2026.

Source: zjunlp/SkillNet on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.