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ai-agent-daily-mentor

Generate an 8-week AI Agent learning plan covering Python, backend, LLM APIs, and RAG.

130|7|Updated Jun 3, 2026
Search Tags:#fastapi#python#rag#machine learning#ai agent
One-click install
npx skills add https://github.com/Marcos-wu/ai-agent-daily-mentor --skill ai-agent-daily-mentor
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-agent-daily-mentor
Source: https://github.com/Marcos-wu/ai-agent-daily-mentor/tree/main
Command: npx skills add https://github.com/Marcos-wu/ai-agent-daily-mentor --skill ai-agent-daily-mentor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires fastapi, pydantic, tortoise_orm, sqlalchemy, sqlite3, redis, langchain, llama_index, chroma, docker, git, markdown, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a structured 8-week learning plan for individuals seeking to develop AI Agent applications from scratch, offering a comprehensive approach to acquiring the necessary skills and knowledge.

Core Features & Use Cases

  • 8-Week Learning Plan: A step-by-step guide to Python basics, backend development, LLM API integration, and project completion.
  • Adaptive Feedback: Adjusts learning difficulty based on user feedback to ensure optimal progress.
  • Project-Driven: Focuses on building a final project, "AI-Interview", a simulation interview system based on RAG and Agents.

Quick Start

Generate a weekly learning plan for Week 1 using the ai-agent-daily-mentor skill.

Frequently Asked Questions about ai-agent-daily-mentor

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

FAQPage Schema
How do I build an AI Agent application from scratch using Python and FastAPI?▼

Build an AI Agent application using Python and FastAPI by following an 8-week learning plan covering backend setup, LLM API integration, and RAG implementation for a complete simulation interview project.

What do I need to set up before starting AI Agent development with LangChain and LlamaIndex?▼

Before starting AI Agent development, install dependencies including FastAPI, Tortoise ORM, Redis, Docker Compose, and obtain an OpenAI-compatible LLM API key to enable LlamaIndex and LangChain integrations.

How does RAG integration work for resume parsing and AI scoring in an interview simulation?▼

RAG integration for resume parsing and AI scoring works by combining Chroma vector databases with LLM APIs to parse candidate data, generate matching interview questions, and evaluate responses automatically.

Can I use SQLite instead of PostgreSQL for a Python backend with Tortoise ORM and SQLAlchemy?▼

Yes, you can use SQLite instead of PostgreSQL for your Python backend, as both database options are supported alongside Tortoise ORM and SQLAlchemy for managing relational data in the AI Agent application.

Is Docker Compose required to deploy a machine learning AI Agent using Chroma and Redis?▼

Docker Compose is required to orchestrate the deployment environment, ensuring seamless interaction between the Redis cache, Chroma vector database, and the FastAPI backend for the machine learning AI Agent.

What is the best way to structure an 8-week learning plan for mastering AI Agent development?▼

The best way to structure an 8-week AI Agent development learning plan is to progress sequentially from Python basics and backend development to LLM API integration, culminating in a project-driven RAG simulation interview system.

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