How does it improve debugging?

Every tool response follows a pattern that includes a unique request ID and structured error codes, making it simple to trace specific tool calls through your application logs.

What frameworks does this skill support?

The patterns are designed to be framework-agnostic but include specific implementations for LangChain and Pydantic, making them ideal for any AI agent that utilizes structured function calling.

How does this help with AI reliability?

By enforcing strict input validation and structured JSON error responses, it prevents malformed inputs from crashing your system and ensures the AI can understand and recover from tool failures.

Can I use this for async operations?

Yes, the skill includes specific design patterns for high-performance, I/O-bound asynchronous tool implementations using httpx and Python's async/await syntax.

Does it handle sensitive data in logs?

Yes, the pattern includes best practices for logging tool invocations that emphasize redacting PII (Personally Identifiable Information) to ensure security and compliance.

AI Tool Design Pattern

Name: AI Tool Design Pattern
Author: ricardoroche

byricardoroche

API 开发

Enforces best practices for creating AI agent tools using Pydantic validation, robust error handling, and structured responses.

关于

The AI Tool Design Pattern skill provides a comprehensive framework for developing high-quality tools for AI agents and function-calling workflows. It standardizes the creation of tools by enforcing Pydantic-based input schemas, detailed docstring documentation, and structured JSON outputs. By implementing these patterns, developers ensure their AI tools are resilient to malformed inputs, provide clear feedback to the LLM during errors, and maintain high observability through consistent logging and request tracking. This skill is essential for teams building production-grade agentic systems where reliability and security are paramount.

主要功能

Standardized error handling with structured JSON and unique request IDs
Built-in patterns for both synchronous and high-performance async tools
Pre-configured unit testing patterns for success and edge cases
0 GitHub stars
Pydantic-based input validation with automatic schema generation
Comprehensive logging templates with PII redaction safety

使用场景

Standardizing tool implementation across large Python development teams
Developing custom LangChain tools for enterprise agentic workflows
Creating secure and validated function-calling interfaces for Claude

关于

主要功能

Standardized error handling with structured JSON and unique request IDs
Built-in patterns for both synchronous and high-performance async tools
Pre-configured unit testing patterns for success and edge cases
0 GitHub stars
Pydantic-based input validation with automatic schema generation
Comprehensive logging templates with PII redaction safety

使用场景

Standardizing tool implementation across large Python development teams
Developing custom LangChain tools for enterprise agentic workflows
Creating secure and validated function-calling interfaces for Claude