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Duration 14 hours
Course Outline
Introduction
- Overview: The agentic coding loop, context management, and tool utilization
- Identifying common challenges at the team level
Project Memory and Configuration
- Understanding the CLAUDE.md hierarchy and appropriate content inclusion
- Managing settings files, permission modes, and allowlists
- Practical Exercise: Configuring a sample repository
Subagents
- Determining when to delegate tasks to subagents
- Defining specific roles, prompts, and tool access permissions
- Practical Exercise: Building subagents for code review and test writing
Hooks
- Exploring hook events and common application scenarios
- Preventing unsafe actions and enforcing team standards
- Practical Exercise: Implementing automatic formatting and testing on changes
Skills, Commands, and Plugins
- Developing reusable Skills and custom slash commands
- Organizing and sharing plugins within a team environment
- Practical Exercise: Packaging a complete team workflow
MCP Integration
- Understanding the Model Context Protocol (MCP) and its use cases
- Connecting to issue trackers, databases, and documentation systems
- Addressing trust and security considerations
Automation and CI
- Utilizing headless mode and scripting capabilities
- Integrating Claude Code into pull request workflows
- Managing costs, usage limits, and monitoring
Best Practices
- Strategies for effectively reviewing AI-generated code
- Identifying common failure modes and prevention tactics
- Creating a team rollout checklist
Requirements
- Consistent daily programming experience in at least one language
- Proficiency with terminal commands and Git workflows
- Prior practical experience with Claude Code or comparable AI coding assistants
Target Audience
- Software developers and engineers who utilize Claude Code in their regular workflow
- Tech leads and platform engineers responsible for establishing shared standards across teams
- DevOps and QA engineers seeking to automate repetitive engineering tasks
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
