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Shresth-sh
Shresth-shCOMMUNITY
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backend-dev

Implement FastAPI endpoints, Pydantic schemas, and TinyDB operations for a Resume Matcher backend.

Updated Feb 24, 2026
Search Tags:#pydantic#fastapi#backend#llm#api-design#tinydb#resume-matcher
One-click install
npx skills add https://github.com/Shresth-sh/resume-matcher-ai-project --skill backend-dev-shresth-sh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: backend-dev
Source: https://github.com/Shresth-sh/resume-matcher-ai-project/tree/main/.github/skills/backend-dev
Command: npx skills add https://github.com/Shresth-sh/resume-matcher-ai-project --skill backend-dev-shresth-sh

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Backend development often requires consistent structure, strict typing, and reliable data storage when building AI-powered features. This skill provides a blueprint for implementing FastAPI endpoints, Pydantic models, and a TinyDB-backed service layer to accelerate development and reduce errors.

Core Features & Use Cases

  • FastAPI endpoints with Pydantic models for strict typing and validation.
  • TinyDB-backed data storage and a simple service layer for business logic.
  • LiteLLM integration to connect to LLM providers for resume enhancement features.
  • Clear guidelines on architecture, error handling, and security practices for production-grade backends.

Quick Start

Set up a FastAPI project with predefined endpoints, data models, and a TinyDB store to power Resume Matcher backend.

Frequently Asked Questions about backend-dev

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

FAQPage Schema
How do I structure a FastAPI backend with Pydantic for strict data validation?▼

Structure a FastAPI backend by defining Pydantic schemas for strict typing, routing endpoints through a modular service layer, and enforcing typed functions with explicit error handling to ensure robust API validation.

Can I use TinyDB for data storage in a FastAPI application?▼

Yes, you can use TinyDB in a FastAPI application to provide a lightweight, file-backed data store. This skill implements a TinyDB-backed service layer to handle business logic and data operations safely.

How does LiteLLM integration work with FastAPI endpoints?▼

LiteLLM integration connects FastAPI endpoints to various LLM providers to support features like resume enhancement. It requires secure API key handling and async safety to manage LLM requests within the backend.

What is the best way to handle API keys securely in a FastAPI backend?▼

Handle API keys securely in a FastAPI backend by following explicit security guidelines for environment configuration. This skill enforces secure API key handling practices specifically for LiteLLM integration and external LLM provider access.

Does this FastAPI approach support building an AI resume matcher?▼

Yes, this FastAPI approach directly supports building an AI Resume Matcher by combining Pydantic data validation, TinyDB storage operations, and LiteLLM integration to process and enhance resume data.

Why enforce typed functions and async safety in a FastAPI backend?▼

Enforcing typed functions and async safety in a FastAPI backend prevents runtime errors during concurrent requests. Strict Pydantic validation and async operations ensure reliable data processing and LLM integration.

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