backend-dev

Name: backend-dev
Availability: InStock
Author: gaganpasupuleti

Standardize FastAPI endpoint creation with Pydantic schemas and LiteLLM integration.

Updated Mar 22, 2026

Search Tags:#pydantic #fastapi #python #backend #tinydb #litellm

Ready to run

npx skills add https://github.com/gaganpasupuleti/Resume-Matcher --skill backend-dev-gaganpasupuleti

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: backend-dev
Source: https://github.com/gaganpasupuleti/Resume-Matcher/tree/main/.github/skills/backend-dev
Command: npx skills add https://github.com/gaganpasupuleti/Resume-Matcher --skill backend-dev-gaganpasupuleti

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires fastapi, pydantic, tinydb, litellm.

What problem does it solve?

This skill provides a standardized framework for developing and maintaining the backend infrastructure of the Resume Matcher application, ensuring consistency across API endpoints, database operations, and LLM integrations.

Core Features & Use Cases

API Development: Provides patterns for creating robust FastAPI endpoints with strict Pydantic schema validation.
LLM Integration: Offers standardized wrappers for LiteLLM to handle multi-provider AI interactions securely.
Data Management: Implements reliable TinyDB operations with necessary concurrency controls and error handling.
Use Case: When adding a new feature that requires a database lookup and an AI-generated response, this skill provides the exact boilerplate and safety rules to implement it efficiently.

Quick Start

Use the backend-dev skill to generate a new FastAPI endpoint that follows the project's type-hinting and error-handling standards.

Frequently Asked Questions about backend-dev

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

FAQPage Schema

How do I create a FastAPI endpoint with Pydantic schema validation and LiteLLM integration?▼

Create FastAPI endpoints with Pydantic schema validation and LiteLLM integration by using standardized wrappers that enforce strict type-hinting, secure API key management, and consistent service logic for production-grade code generation.

What is the best way to manage TinyDB operations with concurrency controls in a Python backend?▼

Manage TinyDB operations with concurrency controls by implementing asynchronous resource locking and robust error handling across database operations, ensuring reliable data management within the FastAPI application infrastructure.

How does LiteLLM integration work for handling multi-provider AI interactions in Python?▼

LiteLLM integration works by providing standardized wrappers to handle multi-provider AI interactions securely, enforcing secure API key management while maintaining consistent service logic across the Python backend application.

Do I need Pydantic and FastAPI to standardize backend development workflows for Resume Matcher?▼

Yes, standardizing backend development workflows for Resume Matcher requires FastAPI and Pydantic for endpoint creation and schema definition, alongside TinyDB and LiteLLM for database operations and AI integrations.

Why does backend development require asynchronous resource locking and strict type-hinting?▼

Backend development requires asynchronous resource locking and strict type-hinting to ensure production-grade code quality, prevent concurrency issues during database operations, and maintain robust error handling across the application.