ArtJack

AI Agent Bootcamp — AI skill for Claude Code

AI community

My journey building AI agents with Claude — progressively more advanced agentic systems.

How to install AI Agent Bootcamp

This entry records only its repository, not the path inside it, so there is no exact command to give. Open ArtJack/ai-agent-bootcamp and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What AI Agent Bootcamp does

My journey building AI agents with Claude — progressively more advanced agentic systems.

Alternatives in AI

  • Cl4r1t4s — LEAKED SYSTEM PROMPTS FOR CHATGPT, CLAUDE, GEMINI, GROK, PERPLEXITY, CURSOR, LOVABLE, REPLIT, AND MORE 47.2k ★
  • Dive Into Claude Code — A Systematic Analysis and Discussion of Claude Code for Designing Today's and Future AI Agent Systems 2.1k ★
  • Qiaomu Design — 偏执型设计顾问:反 AI 味设计 + 风格试衣间 + 58 站设计系统库的 Claude Code Skill Opinionated design advisor for Claude Code: anti-gener 513 ★

README

AI Agent Bootcamp

A collection of progressively advanced AI agents built with the Anthropic Claude API. Part of my journey into AI engineering.

What's Inside

`hello_claude.py`

Basic API integration — single message request/response with token accounting.

`first_agent.py`

First multi-turn agent implementing the **ReAct pattern** (Reason + Act). Demonstrates:

  • Tool definition with JSON Schema
  • Agent loop with stop-reason handling
  • Parallel tool execution
  • Multi-turn reasoning

Tools: `get_weather`, `calculate`

`agent_v2.py`

Enhanced agent with production-grade features:

  • System prompt for behavior/personality control
  • Three tools including currency conversion
  • Graceful error handling for unknown inputs
  • Pre-emptive reasoning (agent skips tools when it knows they'll fail)
  • Structured logging of each turn and tool call

Tech Stack

  • Python 3.12
  • Anthropic Python SDK
  • python-dotenv for secret management

Setup

python3.12 -m venv venv
source venv/bin/activate
pip install anthropic python-dotenv

# Add your API key to .env
echo "ANTHROPIC_API_KEY=sk-ant-..." > .env

python agent_v2.py

Concepts Demonstrated

  • ReAct (Reasoning + Acting) loop
  • Tool use / function calling
  • Parallel tool invocation
  • System prompt engineering
  • Error handling in agentic systems

What's Next

  • Streaming responses
  • Conversational memory
  • Model Context Protocol (MCP) integration
  • RAG (Retrieval-Augmented Generation)