Alfworld Task Verifier logo

Alfworld Task Verifier

OrganizationPopular
zjunlp
alfworld-task-verifier

Use when the agent needs to check whether an ALFWorld task objective has been met after completing a sub-action (e.g., placing an object). This skill parses the task goal, evaluates the latest environment observation, and outputs a verification decision — task complete, task incomplete, or action ineffective — to guide the next step.

Overview

Publisherzjunlp
RepositorySkillNet
Skill namealfworld-task-verifier
Stars
1.4K
Forks
152
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Alfworld Task Verifier 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/alfworld/alfworld-task-verifier
  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/alfworld/alfworld-task-verifier .claude/skills/alfworld-task-verifier
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Alfworld Task Verifier 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 Alfworld Task Verifier 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 Alfworld Task Verifier 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.

Skill: Task Verifier for ALFWorld

When to Use

Trigger this skill when:

  1. The agent has just completed a key sub-action (e.g., put {obj} in/on {recep})
  2. The agent needs to determine whether the overall task goal is satisfied
  3. The agent must decide whether to continue searching or conclude the task

Core Workflow

1. Parse the Task Goal

Extract from the original task description:

  • Target object(s): What needs to be found/placed (including quantity)
  • Target receptacle: Where objects must end up
  • Required transformations: Any cleaning, heating, or cooling steps

2. Analyze the Observation

Evaluate the most recent Observation: text from the environment:

  • Look for evidence that required objects are present in the target receptacle
  • Count how many target objects have been successfully placed
  • Note: Other items in the receptacle do not invalidate success

3. Make a Verification Decision

ConditionDecisionOutput
All required objects confirmed in target receptacleCompleteVerification: Task complete. No further action needed.
Some but not all objects placedIncompleteVerification: Task incomplete. Continue searching for {missing object(s)}.
Observation says "Nothing happened"IneffectiveVerification: Last action was ineffective. Re-assess and try a different approach.
Object placed in wrong receptacleIncorrectVerification: Object placed in wrong location. Retrieve and redirect to {correct receptacle}.

4. Output Format

Output only the verification decision. Do not output the next action. This skill informs the planning for the next step, not the execution.

Example

Task: "Find two pen and put them in garbagecan."

Action: put pen 3 in/on garbagecan 1
Observation: You put the pen 3 in/on the garbagecan 1.

> Verification: Task incomplete. Continue searching for pen (1 of 2 placed).

Action: put pen 1 in/on garbagecan 1
Observation: You put the pen 1 in/on the garbagecan 1.

> Verification: Task complete. No further action needed.

Example — Failed action:

Action: put pen 3 in/on garbagecan 1
Observation: Nothing happened.

> Verification: Last action was ineffective. Re-assess and try a different approach.

Error Handling

  • Ambiguous observation: If the observation does not clearly confirm or deny placement, navigate to the target receptacle and re-examine it to get an updated state.
  • Quantity tracking: For multi-object tasks, maintain a running count. Re-examine the target receptacle if the count is uncertain.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Alfworld Task Verifier AI skill do?

Use when the agent needs to check whether an ALFWorld task objective has been met after completing a sub-action (e.g., placing an object). This skill parses the task goal, evaluates the latest environment observation, and outputs a verification decision — task complete, task incomplete, or action ineffective — to guide the next step.

Why use Alfworld Task Verifier on TypingMind?

Because you install it once and use it with any model. Alfworld Task Verifier 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 Alfworld Task Verifier in TypingMind?

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

Which AI models can use Alfworld Task Verifier?

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 Alfworld Task Verifier?

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

Is the Alfworld Task Verifier 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.

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