The .github folder, end to end
How custom instructions activate via glob patterns, how prompt files create reusable slash commands, how agents define specialized personas — the complete directory anatomy, explained through a real-world project.
Seven surfaces, one context architecture
Build each customization layer individually — instructions, prompts, agents, skills, MCP servers, hooks, and plugins — then combine them into a composable three-axis context system that activates automatically.
Full SDLC integration
Apply context engineering across the entire software development lifecycle: plan with prompts, code with instructions, review with agents, and automate with hooks — from first commit to production CI or CD.
VS Code and CLI throughout
Every demo runs in VS Code with GitHub Copilot Chat and the Copilot CLI. No cloud accounts or paid APIs required — you build a real .github setup that works on your local machine from lesson one.
GitHub Copilot is a powerful code generation tool. But out of the box, it does not know your project's naming conventions, preferred libraries, architecture decisions, or testing patterns. Every new chat session starts from zero — an amnesiac genius that writes excellent code in the wrong framework. Context engineering fixes that by building structured, version-controlled configuration files that teach the AI how your project works.
The customization system has seven distinct surfaces. Custom instructions activate automatically based on file paths. Prompt files create reusable slash commands for recurring tasks. Custom agents define specialized personas with restricted tool access. Skills package domain knowledge for cross-tool portability. MCP servers connect external tools. Hooks automate pre- and post-chat actions. Plugins extend agent capabilities from the marketplace. Each surface has a defined file format, trigger mechanism, and scope — and they compose together through a three-axis model.
In this course you build the complete picture. Starting with a blank .github folder, each lesson adds one layer of context capability — from always-on instructions through lifecycle hooks. By the end, you have a production-grade .github and /docs tree that makes Copilot behave like a project-aware expert. Every demo uses VS Code and the Copilot CLI.
1Understand why context engineering matters
2Master the .github directory structure
3Build all seven customization surfaces
4Apply the three-axis context model
5Integrate context into your SDLC
6Avoid common anti-patterns
Prerequisites
What you need before starting
Individual Developers
You use Copilot daily and want it to generate code that matches your project conventions without constant manual correction.
Tech Leads and Architects
You want to encode architecture decisions, coding standards, and security policies into AI configuration that applies automatically for the whole team.
DevOps and Platform Engineers
You manage CI or CD pipelines and developer tooling, and want to integrate AI context into existing automation and governance workflows.
Teams Adopting Agentic AI
Your team is moving from autocomplete to agents, and needs a structured approach to agent configuration, skills, and multi-agent orchestration.
9 lessons + 1 interview questions · ~95 mins
Each lesson builds on the previous one — follow them in order for the best experience.
Why Context Engineering Matters
Curate Project Context with .github and /docs/
Design Instruction Architecture
Planning Workflows with Prompts and Plan Agents
Implementation Workflows with Agents and Skills
MCP, Hooks, and Guardrails
Surface Strategy: Copilot, Claude, and Codex
Context Engineering Governance & Operating Model
End-to-End AI-Assisted SDLC Workflow
Course Quiz
