CLAUDECODE100
Claude Code Training
Become an augmented developer by mastering Claude Code, Anthropic's CLI tool for AI-assisted development. This intensive one-day training teaches you to collaborate effectively with AI agents: context management with CLAUDE.md, planning with Plan Mode, test generation, sub-agent orchestration and creating reusable skills. Participants leave with concrete methods and workflows directly applicable to their projects.
✓ Official training SFEIR InstituteLevel Fundamentals⏱️ 1 day (7h)
What you will learn
- Explain the augmented developer concept and distinguish augmented development from vibe coding.
- Create effective CLAUDE.md context files to guide Claude Code behavior according to project conventions.
- Apply the Plan Mode workflow to review and approve AI-proposed refactorings before execution.
- Generate unit and integration tests with Claude Code achieving significant coverage (>=70%).
- Create autonomous sub-agents and reusable skills to automate repetitive workflows.
- Orchestrate multiple sub-agents in parallel and sequential patterns for complex tasks.
- Master essential commands and productivity tips for Claude Code.
Prerequisites
- Practical mastery of at least one programming language (Python, JavaScript, Java, TypeScript, Go...).
- Daily experience with Git and a modern IDE (VS Code, IntelliJ, WebStorm, etc.).
- Basic command-line and file editing skills.
- A standard laptop (16 GB recommended) with rights to install software.
- Recent operating system (Windows 10+, macOS 10.15+, Linux).
- Git installed and configured with access to GitHub/GitLab.
- Recent version of Node.js and npm installed and configured.
- A stable internet connection.
Target audience
- Software developers (backend, frontend, full-stack) looking to integrate Claude Code into their daily workflow., Tech Leads and architects seeking to evaluate and adopt AI-augmented development for their teams., DevOps and Platform Engineers interested in advanced automation with AI agents.
Training Program
6 modules to master the fundamentals
Objectives
- Explain the augmented developer concept and distinguish it from AI replacement narratives
- Identify tasks suited for AI delegation (refactoring, documentation, tests)
- Classify scenarios by AI suitability (autonomous, collaborative, human-directed)
- Articulate appropriate verification steps for AI-generated code
Topics covered
- →The augmented developer: a new paradigm for software development
- →Key principles: delegation, supervision, collaboration, trust with verification
- →Traditional vs augmented developer: comparing roles and workflows
- →Addressing common concerns about AI-generated code
- →AI capabilities matrix: autonomous, partnership, human-directed
- →Realistic expectations: what AI does well and struggles with in 2026
- →Vibe Coding vs Augmented Development: definitions, limits and when it's acceptable
- →Trust but always verify: automated and human verification
Activities
Quiz: augmented developer role, workflows, trust and AI delegation
Objectives
- Install and configure Claude Code on your workstation
- Choose the authentication method suited to your context
Topics covered
- →Installation methods: native (recommended), Homebrew, npm
- →Native installation on Windows (PowerShell, CMD), WSL, macOS and Linux
- →OS-specific considerations: WSL vs Git Bash on Windows
- →Authentication: OAuth (recommended) and API key
- →Environment integration: PATH, verification
- →IDE integration: JetBrains (IntelliJ, PyCharm)
Objectives
- Create effective CLAUDE.md files using the six-section template
- Apply the Plan Mode workflow for multi-file refactorings
- Generate unit and integration tests with >=70% coverage
- Evaluate when to use Plan Mode vs Direct Execution
Topics covered
- →CLAUDE.md: persistent context file for Claude Code
- →Six-section template: Project Overview, Architecture, Directory Structure, Coding Conventions, Development Workflow, Context for AI
- →Concrete examples per section and best practices
- →Without CLAUDE.md vs With: impact on generated code quality
- →Plan Mode: review-before-execution workflow in three phases (Request → Review → Execute)
- →Making effective requests and review checklist
- →Approve, modify or reject a plan
- →Test generation: three patterns (unit, integration, improvement)
- →Quality principles: AAA structure, descriptive names, mocking external dependencies
Activities
Lab 1-1: Create a CLAUDE.md and verify context usage
Lab 1-2: Refactoring with Plan Mode (analysis, plan, review, validation)
Lab 1-3: Unit and integration test generation
Quiz: CLAUDE.md impact, Plan Mode vs Direct Execution, coverage vs quality
Objectives
- Understand how Claude Code's agentic loop works
- Organize project documentation into structured .md files for efficient context management
- Implement modular rules in .claude/rules/ for large codebases
- Apply advanced context management techniques
Topics covered
- →The agentic loop: Perceive (Read) → Reason → Act
- →Claude Code models: Sonnet 4.6 (default), Opus 4.6, Haiku 4.5
- →Available tools: files, terminal, search, web
- →Access control and permissions: reads, modifications, dangerous commands
- →Sessions and context: ephemeral sessions, context management, parallelization
- →Structured documentation: from monolithic CLAUDE.md to docs/ by concern
- →CLAUDE.md as reference hub with selective context loading
- →Modular rules: organization in .claude/rules/ with subfolders
- →Loading hierarchy: enterprise, project, rules, user, local
- →Unconditional rules vs path-specific rules (YAML frontmatter paths)
Activities
Lab 2-1: Structured documentation organization with selective loading
Lab 2-2: Creating modular rules with YAML frontmatter paths
Quiz: agentic loop, monolithic CLAUDE.md, monorepo, docs vs rules
Objectives
- Create autonomous sub-agents with specialized contexts and tool permissions
- Orchestrate multiple sub-agents in parallel and sequential patterns
- Distinguish between sub-agents (isolation) and skills (automation)
- Build custom skills to automate repetitive workflows
Topics covered
- →Sub-agents: separate context, limited tools, specialized prompt, independent permissions
- →Built-in agents: Explore (Haiku), Plan (Sonnet), General-purpose (Sonnet), Bash (Sonnet)
- →Creating a custom sub-agent with YAML frontmatter in .claude/agents/
- →Project vs user storage and delegation modes (automatic vs explicit)
- →Skills: automating repetitive workflows with .claude/skills/*/SKILL.md
- →Skill anatomy: YAML frontmatter, $ARGUMENTS, manual vs automatic invocation
- →Skill types: Reference (knowledge) vs Task (action)
- →Sub-agents vs Skills: key differences (context, tools, model, performance)
- →Pattern 1: Parallel search (3 simultaneous agents)
- →Pattern 2: Agent chaining (exploration → review → fix → validation)
- →Pattern 3: Volume operation isolation
- →Advanced orchestration: foreground vs background, combining sub-agents + skills
Activities
Lab 3-1: Creating a custom sub-agent with tool restrictions
Lab 3-2: Creating custom skills for repetitive workflows
Lab 3-3: Multi-agent orchestration (parallel and chaining)
Quiz: sub-agent vs skill, file scanning, automation, parallelization
Objectives
- Master essential Claude Code commands for daily productivity
- Apply workflow optimization techniques
Topics covered
- →CLI commands: startup, sessions, print mode, updates
- →Interactive commands: /resume, /rename, /clear, /compact, /context, /memory, /cost, /stats
- →Checkpointing and rewind: restoring conversation, code, or both
- →Bash commands (! prefix) and background mode (Ctrl+B)
- →Workflow best practices and context optimization
Activities
Quiz: session resume, rewind, background mode, saturated context
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
Teaching Methods Used
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Guided Labs — Guided practical exercises on software, hardware, or technical environments.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
Evaluation and Monitoring System
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Upcoming sessions
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