claude-code-crash-course: Hands-on crash course for Claude Code with branch-based projects on MCP, subagents, hooks, and automation

by emarco177 · MCP Server · ★ 191

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install claude-code-crash-course

git clone https://github.com/emarco177/claude-code-crash-course.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 69/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

agentic-aiagentic-codingclaude-codecontext-engineeringdeep-agentssubagents

Quick Facts

Stars191
Forks162
CategoryMCP Server
Quality Score69/100
Open Issues4
Last Updated2026-10-31
Created2025-07-28
Platformsclaude-code, mcp
Est. Tokens~16k

About claude-code-crash-course

Claude Code Crash Course 🚀 Welcome to the Claude Code Crash Course! This repository is designed to teach you the fundamentals and advanced concepts of Claude Code, Anthropic's official CLI for AI-powered software development, in a hands-on way. What is Claude Code? 💡 Claude Code is an interactive command-line interface that brings Claude's AI capabilities directly to your development workflow.

Compatible Skills

These tools work well together with claude-code-crash-course for enhanced workflows:

  • Mysti — semantic(0.28)+complementary+rare_topics+similar_pop+shared_platform (49%)
  • ai-coding-project-boilerplate — semantic(0.23)+complementary+similar_pop+shared_platform (48%)
  • Frame — semantic(0.23)+complementary+rare_topics+similar_pop+shared_platform (48%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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