Scienceworld Result Archiver logo

Scienceworld Result Archiver

OrganizationPopular
zjunlp
scienceworld-result-archiver

Places an object into a designated container (like a colored box) based on a test outcome. It should be triggered when the agent needs to finalize a task by storing an object according to a rule (e.g., conductive → blue box, non-conductive → orange box). The input is the object and the rule, and the output is the object moved to the correct container.

Overview

Publisherzjunlp
RepositorySkillNet
Skill namescienceworld-result-archiver
Stars
1.4K
Forks
152
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by zjunlp on GitHub. Read the source before you install it.

Installation

Install the Scienceworld Result Archiver AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/zjunlp/SkillNet/tree/main/experiments/src/skills/scienceworld/scienceworld-result-archiver
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zjunlp/SkillNet.git /tmp/SkillNet
mkdir -p .claude/skills
cp -r /tmp/SkillNet/experiments/src/skills/scienceworld/scienceworld-result-archiver .claude/skills/scienceworld-result-archiver
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scienceworld Result Archiver in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Scienceworld Result Archiver on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Scienceworld Result Archiver is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Instructions

Use this skill to finalize a scientific test by archiving an object based on the observed result.

When to Use

  • You have just completed an experimental test (e.g., electrical conductivity, chemical reaction, physical property test).
  • You have a clear rule mapping a specific test outcome to a specific destination container (e.g., "If property X is true, place in Container A; if false, place in Container B").
  • The object to be archived and the destination containers are present in your current environment.

Core Procedure

  1. Verify Context: Ensure you are in the correct room (typically the workshop or lab area) where the test was conducted and where the destination containers are located.
  2. Confirm Test Result: Observe the final state of your experimental apparatus to definitively determine the test outcome (e.g., "light bulb is on").
  3. Apply Rule: Map the confirmed outcome to the corresponding destination container as specified by the task rule.
  4. Execute Archive: Move the test object from its current location (inventory or room) into the correct container using the move OBJ to CONTAINER action.

Key Principles

  • Direct Archiving: Do not re-run the test. The skill is for archiving the result based on an already observed outcome.
  • Rule Adherence: Strictly follow the provided mapping rule. Do not infer or create new rules.
  • Container Verification: Before moving the object, visually confirm the target container exists in the room (use look around if uncertain).

Example Rule Application

Task Rule: "If the metal pot is electrically conductive, place it in the blue box. If it is electrically nonconductive, place it in the orange box." Observation: The blue light bulb in the circuit is on. Interpretation: The metal pot is conductive. Action: move metal pot to blue box

Bundled Logic

For the specific, error-prone sequence of connecting a circuit to test electrical conductivity, use the bundled script scripts/conductivity_test.py as a reference. For all other test types, use the general instructions above.

Frequently asked questions

What does the Scienceworld Result Archiver AI skill do?

Places an object into a designated container (like a colored box) based on a test outcome. It should be triggered when the agent needs to finalize a task by storing an object according to a rule (e.g., conductive → blue box, non-conductive → orange box). The input is the object and the rule, and the output is the object moved to the correct container.

Why use Scienceworld Result Archiver on TypingMind?

Because you install it once and use it with any model. Scienceworld Result Archiver is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Scienceworld Result Archiver in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zjunlp/SkillNet/tree/main/experiments/src/skills/scienceworld/scienceworld-result-archiver. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Scienceworld Result Archiver?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Scienceworld Result Archiver?

As many as you like. As long as a model supports skills, you can use Scienceworld Result Archiver with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Scienceworld Result Archiver AI skill free?

Yes. It is published on GitHub by zjunlp under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇