Context Course documentation
Welcome to The Context Course
Welcome to The Context Course
This course is about context engineering for AI code agents: structuring knowledge so an agent can efficiently find what it needs, when it needs it to improve its generated outputs.
The Key Skill for Code Agents
Claude Code, Codex, and OpenCode all share the same constraint: an agent is only as good as the context it has. Good context means fewer wrong turns, cleaner diffs, and less rework.
Across six units you’ll build portable skills, wire up tools through the Model Context Protocol (MCP), package those pieces into plugins, coordinate sub-agents for larger tasks, and study how a minimal agent loop actually works under the hood.
What You’ll Learn
This course is structured into 6 core units:
| Unit | Topic | What You’ll Learn |
|---|---|---|
| Unit 0 | Onboarding | Course overview, tool setup, prerequisites |
| Unit 1 | Agent Skills | What skills are, how to build and share them, how agents load them |
| Unit 2 | Model Context Protocol | MCPs explained, connecting tools and APIs to agents |
| Unit 3 | Plugins | Building plugins, designing agent workflows |
| Unit 4 | Sub-agents | Spawning specialized agents, multi-agent patterns |
| Unit 5 | Hooks | Observing, blocking, and automating the agent lifecycle |
| Unit 6 | Bonus: Nano Harness | Building a minimal agent loop from scratch |
Prerequisites
Before starting, you should be comfortable with Python basics (variables, functions, loops, and file I/O), able to navigate directories and run scripts from the command line, and have a Hugging Face account (huggingface.co). You’ll also need at least one code agent installed and configured — see the setup section below.
Tool Setup
This course works with multiple code agents. Choose at least one to follow along:
This edition of the course uses Claude Code, Codex, and OpenCode as the reference agents throughout. If you’re using Cursor or GitHub Copilot, the same ideas still apply, but their MCP and extension UX differs enough that they are not covered step-by-step here.
Claude Code is Anthropic’s official code agent, accessible via the web, desktop app, or CLI.
curl -fsSL https://claude.ai/install.sh | bash
Getting started: Visit claude.ai/code to use Claude Code on the web, or install the CLI above and run claude in any project directory. You’ll be prompted to sign in on first use.
How to Navigate This Course
Recommended Pace
Plan on one unit per week, roughly 2–3 hours each. Context engineering is a practice-heavy skill, so build the examples rather than skimming them.
Learning Format
Each unit mixes conceptual material with runnable code, a hands-on project, and a short quiz.
Customizing Your Path
While we recommend following units in order, you can customize based on your needs:
- Just want skills? Start with Unit 1, revisit MCPs when needed
- Building a plugin for your team? Start with Unit 3
- Multi-agent systems? Begin with Unit 4, return to Unit 1-2 as reference
- Following along with open source? All lessons include OpenCode examples alongside Claude Code and Codex
Certifications
This course offers two levels of certification:
Context Fundamentals Certificate
Demonstrates you understand core context engineering concepts. Pass the Unit 1–2 quizzes with 70% or higher to earn this certificate in 2–3 weeks. It’s shareable and displayed on your Hugging Face profile.
Context Engineering Certificate
Demonstrates mastery of context engineering across all domains. Pass all Unit 1–5 quizzes (70% or higher) and complete the capstone project to earn this certificate in 5–8 weeks. It’s displayed on your Hugging Face profile with a project showcase.
The capstone project will be announced in the course live stream. Follow the Context Course org on Hugging Face to get updates.
Both certificates verify your ability to build and maintain agent skills, connect external tools through MCPs, design multi-agent systems, and optimize context for maximum agent performance.
Course Structure
Every unit follows the same shape: an introduction, conceptual material, practical walkthroughs, a hands-on project, and a quiz. Starter templates and copy-pasteable code are provided throughout so you spend time on the ideas rather than on boilerplate.
Meet Your Instructors
Ben Burtenshaw — ML Engineer, Hugging Face
Ben focuses on LLM applications with emphasis on post-training and agentic approaches. He leads initiatives around agent best practices and context engineering at Hugging Face.
Atin Kumar Singh — ML Researcher & Founding Engineer @ Data Pigeon
Atin’s research focuses on world models and robotics at the DARE Lab, UC Davis. He is a founding engineer at Data Pigeon, an AI-driven EV charging platform. His broader work spans applied ML and infrastructure for AI agents.
Maya Nielan
Maya contributed to the Claude Code sections of this course.
Ryan Whitehead
Ryan contributed to the Claude Code sections of this course.
Connect with the Community
Learning is better together. Join the conversation:
- Discord: discord.gg/huggingface
- Share your work: Tag #ContextCourse on social media
- Report issues: GitHub Issues for course content bugs
Next Steps
Install at least one of the agents above, check the prerequisites, then head to Unit 1 to start with agent skills.
Update on GitHub