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Alfworld Search Verifier

OrganizationPopular
zjunlp
alfworld-search-verifier

Re-examines previously visited locations to confirm the absence of a target object or to check for overlooked items. Use when an initial search fails to find enough objects or when double-checking is required before concluding task failure. Systematically revisits receptacles, re-opens closed containers, and re-inspects contents to ensure no viable location was missed.

Overview

Publisherzjunlp
RepositorySkillNet
Skill namealfworld-search-verifier
Stars
1.4K
Forks
152
Bundled files
1
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.

  • 1 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 Search 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-search-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-search-verifier .claude/skills/alfworld-search-verifier
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Alfworld Search 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 Search 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 Search 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.

Instructions

Trigger Condition: Use this skill when an initial search for a target object (e.g., cellphone) has been performed but the required quantity has not been found, or when you need to verify that no viable location was missed before task failure.

Core Procedure

  1. Initialize Tracking: Maintain a mental or explicit list of all receptacles (recep) you have already visited during your initial search (e.g., sidetable 1, shelf 1-8, drawer 1, drawer 2, desk 1).

  2. Systematic Revisit: Do not search new, unvisited locations. Instead, systematically return to each previously visited receptacle in a logical order (e.g., by proximity or by the original search sequence).

    • Action: Use go to {recep} to navigate.
    • For Closed Receptacles: If a receptacle was closed during the initial search and you opened it, it may be closed again. Re-open it to verify its contents.
      • Action: Use open {recep}.
    • For Open/Empty Receptacles: Observe the contents again. The state may have changed, or an item may have been overlooked.
    • Action: No action needed; read the Observation.
  3. Verification Logic: For each revisited location:

    • If the target object is now present, retrieve it (take {obj} from {recep}) and proceed with the main task.
    • If the location is confirmed to lack the target object, note it as thoroughly checked and move to the next location on your list.
    • If you encounter a closed receptacle you previously assumed was empty, opening it is a critical verification step.
  4. Conclusion: After revisiting all locations on your list:

    • If the target object was found: Integrate it into your main task plan.
    • If the target object was not found: You can conclusively report that the object is not available in the searched area and adjust your task strategy accordingly (e.g., consider task failure or exploring a new, unsearched area).

Example

Scenario: You need two cellphones but only found one. Revisiting previously searched locations.

Thought: I only found 1 cellphone but need 2. Let me revisit sidetable 1 which I checked earlier.
Action: go to sidetable 1
Observation: On the sidetable 1, you see a cellphone 2, a keychain 1.
Thought: Found cellphone 2! I must have overlooked it during the initial search.
Action: take cellphone 2 from sidetable 1
Observation: You pick up the cellphone 2 from the sidetable 1.

Result: The second cellphone was found by re-examining a previously visited location.

Thought Process Template

When executing this skill, structure your reasoning as follows:

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 Search Verifier AI skill do?

Re-examines previously visited locations to confirm the absence of a target object or to check for overlooked items. Use when an initial search fails to find enough objects or when double-checking is required before concluding task failure. Systematically revisits receptacles, re-opens closed containers, and re-inspects contents to ensure no viable location was missed.

Why use Alfworld Search Verifier on TypingMind?

Because you install it once and use it with any model. Alfworld Search 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 Search 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-search-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 Search 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 Search Verifier?

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

Is the Alfworld Search 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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