effect-ai-tool

Define and implement type-safe AI tools using @effect/ai Tool and Toolkit APIs.

68|13|Updated Aug 9, 2025
One-click install
npx skills add https://github.com/kriegcloud/beep-effect --skill effect-ai-tool-kriegcloud
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: effect-ai-tool
Source: https://github.com/kriegcloud/beep-effect/tree/main/.repos/beep-effect/.claude/skills/effect-ai-tool
Command: npx skills add https://github.com/kriegcloud/beep-effect --skill effect-ai-tool-kriegcloud

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill streamlines the process of building AI integrations by providing a robust framework for defining, managing, and executing tools for Large Language Models (LLMs).

Core Features & Use Cases

  • Type-Safe Tool Definitions: Create tools with precise parameter and response schemas using Effect-TS.
  • Toolkit Composition: Combine multiple tools into a single, manageable toolkit.
  • Dependency Management: Easily provide service dependencies to your tool handlers.
  • Provider-Defined Tools: Integrate with provider-native tools like bash or web search.
  • Use Case: When building an AI assistant that needs to interact with external services like a database or an API, use this skill to define the available actions, their inputs, and expected outputs in a type-safe manner.

Quick Start

Use the effect-ai-tool skill to define a new tool named 'GetCurrentTime' that returns the current timestamp.

Frequently Asked Questions about effect-ai-tool

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I define type-safe tools for LLM integrations in Effect-TS?

Type-safe tool definitions for LLM integrations are created using @effect/ai's Tool and Toolkit APIs to establish precise parameter and response schemas. This enables strict parameter validation and handler implementations for your AI tools.

What is toolkit composition for AI tools and when do I need it?

Toolkit composition for AI tools combines multiple defined tools into a single, manageable toolkit. You need this when building an AI assistant that must interact with multiple external services like databases or APIs simultaneously.

How do I provide service dependencies to AI tool handlers?

Service dependencies are provided to AI tool handlers through the dependency injection framework in @effect/ai. This allows you to easily supply required services to your tool implementations alongside toolkit composition.

Can I integrate provider-defined tools like bash or web search with my custom AI tools?

Provider-defined tools like bash or web search can be integrated alongside your user-defined tools using @effect/ai's Toolkit APIs. This allows your LLM integrations to access native provider capabilities within the same type-safe framework.

Does this approach support parameter validation for user-defined AI tools?

Parameter validation for user-defined AI tools is natively supported through Effect-TS schemas. Defining tools with precise parameter schemas ensures that inputs to your LLM tool handlers are validated before execution.

What is the best way to manage multiple AI tools for a single LLM assistant?

The best way to manage multiple AI tools is through toolkit composition, combining individual type-safe tool definitions into a unified toolkit. This allows streamlined handler implementation and dependency management across all tools.