Master CLAUDEmd, Skills, Planning Mode, and Automation to Turn Claude Code into Your Project Co-Pilot
β±οΈ Length: 6.9 total hours
π₯ 125 students
π February 2026 update
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- Course Overview
- Transition from a casual AI user to a professional AI-Native Architect by mastering the command-line interface (CLI) capabilities of Claude Code, specifically designed for high-velocity software engineering.
- Deep dive into the philosophy of agentic workflows, where the AI is treated as a junior-to-mid-level developer capable of autonomous execution, rather than just a simple text-based autocomplete tool.
- Explore the mechanics of the Claude Code CLI, moving beyond browser-based chat interfaces to a terminal-centric environment that integrates directly with your local filesystem and version control.
- Understand the internal logic of Planning Mode vs. Act Mode, learning how to force the AI to reason through complex architectural decisions before it touches a single line of production code.
- Learn to manage Agentic Context within large-scale repositories, ensuring that Claude remains aware of project-specific patterns, legacy debt, and cross-module dependencies without hitting token limits.
- Master the art of automated scaffolding and project bootstrapping, where you use specialized prompts to generate entire boilerplate architectures in seconds.
- Analyze real-world case studies from 2026 development cycles where AI-Native workflows reduced the time-to-market for complex microservices by over 60 percent.
- Requirements / Prerequisites
- Proficiency with the Command Line Interface (CLI), including a solid understanding of shell navigation, file manipulation, and terminal-based environments like Zsh or Bash.
- An active Anthropic API account with sufficient tier access to utilize the latest Claude models via the terminal-based agent.
- Local installation of Node.js (LTS version) and npm/pnpm, as these are fundamental to running the Claude Code environment and its associated plugins.
- Foundational knowledge of Git version control, including branching, committing, and resolving merge conflicts, which are essential for managing Claude’s autonomous PR suggestions.
- A mid-level understanding of at least one major programming language (e.g., TypeScript, Python, or Rust) to effectively review and audit the code generated by the AI agent.
- Familiarity with Environment Variables and secure API key management to ensure a safe integration between your local dev machine and the cloud-based LLM.
- Skills Covered / Tools Used
- CLAUDE.md Configuration: Designing and maintaining the “brain” of your project by creating specialized instruction files that dictate coding standards and project goals.
- Model Context Protocol (MCP): Integrating custom MCP Servers to allow Claude Code to interact with external tools like Google Search, GitHub, Slack, and local databases.
- Context Indexing: Utilizing built-in indexing tools to help the AI map out large codebases, allowing it to “understand” files it hasn’t even opened yet.
- Shell Integration: Running shell commands directly through the AI interface to execute tests, install dependencies, and perform system-level diagnostics.
- Agentic Debugging: Using Claude to perform automated stack trace analysis and recursive bug fixing where the AI iterates until unit tests pass.
- Skills & Shortcuts: Creating custom “Skills” (reusable macros) that automate repetitive tasks like writing documentation, generating unit tests, or refactoring exports.
- Vision-to-Code: Leveraging multi-modal capabilities to feed UI/UX screenshots into the workflow for direct frontend implementation via the CLI.
- Benefits / Outcomes
- Achieve a 10x developer output by offloading the cognitive load of syntax, boilerplate, and routine debugging to a highly tuned AI co-pilot.
- Eliminate context-switching fatigue by keeping your entire development lifecycleβcoding, testing, and deploymentβwithin a single terminal-based agentic interface.
- Develop a Standardized Project Intelligence by utilizing CLAUDE.md files, ensuring that any developer (human or AI) entering the project immediately follows the same architectural rules.
- Master the ability to refactor massive codebases safely, using the AI to identify deprecated patterns and replace them across hundreds of files simultaneously.
- Improve Code Quality and Documentation by automating the generation of JSDoc, README files, and inline comments that stay synchronized with actual logic changes.
- Gain a competitive edge in the 2026 job market by demonstrating mastery over agent-led development, a skill set increasingly demanded by high-growth tech firms.
- Reduce Mean Time to Resolution (MTTR) for production bugs by deploying the AI to investigate logs and suggest patches in a fraction of the time required for manual searching.
- PROS
- Provides a cutting-edge look at terminal-first AI integration, which is significantly more powerful than standard IDE extensions or web chats.
- Focuses heavily on automation and autonomy, teaching you how to let the AI work for you while you focus on high-level system design.
- Includes advanced techniques for Model Context Protocol (MCP), which is the current gold standard for expanding AI capabilities.
- Designed for professional scale, moving away from “toy” examples toward managing production-grade, multi-file repositories.
- CONS
- The course has a steep technical learning curve that may be overwhelming for developers who are not comfortable with advanced CLI operations or shell scripting.
Learning Tracks: English,Development,Data Science
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