Awesome CopilotAdventures

Agentic engineering · Evidence first

Learn the craft.
Prove the result.

A complete learning library for GitHub Copilot: guided adventures, practical hands-on labs and the original source behind every exercise.

Ask → Plan → Agent → Review → Evidence

The complete library in three languages

New here? Follow one clear path.

Start with one local check. Choose your next lesson, then download only the materials it needs.

Generated visual stories

Adventures in motion

Three short montages introduce the adventure world. They are illustrations, not recordings of Copilot or evidence that a lab ran.

Videos load only when you choose to play. Reduced motion keeps the still image; you can still explicitly start an animation. Playback stops when the page is hidden.

A navigator studies instruments in the Nexus chamber.

10 seconds · no audio

Portals, context and iteration

A navigator explores the Nexus workstations, compares the same mechanism in two context mirrors, then works around the circular mechanism of Tempora.

A navigator examines a device between illuminated workstations.

10 seconds · no audio

From investigation to guardrails

Scenes move from a navigation desk to a clockwork loop, an archive city, a handoff between specialists and a controlled checkpoint with a stop mechanism.

Illuminated paths extend from a central charter into an archive city.

10 seconds · no audio

Rules, skills and connections

An archive charter gives way to a selected toolkit, specialist workstations, contrasting context mirrors and a cartographer observing a distant archive.

Interactive learning / local only

The practice studio

Make a decision. Observe the consequence. Reset and try again before taking the idea into a real lab.

Educational simulations, not a live Copilot session. No AI model, account, terminal, file edit or external service is used.

Guides

The complete learning library

Every current learning document is available in English, Spanish and Brazilian Portuguese. Switch language without losing your place.

Open the complete library

Choose your path

Two formats. One learning progression.

Build understanding

From first context to complex coordination.

  1. 01

    Foundations

    Context, harness choice and repository instructions.

  2. 02

    Engineering

    Bounded changes, skills, custom agents and guardrails.

  3. 03

    Coordination

    MCP, verification loops, graphs and parallel work.

  4. 04

    Surfaces

    CLI, cloud agent, SDK and an integrated capstone.

Learning paths

Build understanding

The complete learning library

Every current learning document is available in English, Spanish and Brazilian Portuguese. Switch language without losing your place.

Awesome Copilot Adventures

Learn agentic software engineering through guided, fantasy-themed laboratories built around observable evidence.

[!TIP] First visit: follow Start here for one working local check. Then use the learning order to choose your path. Download one learner kit, not every runtime and project.

[!IMPORTANT] The curriculum teaches agent roles, harnesses, environments, and customization primitives as separate concepts. It does not teach deprecated custom chat modes.

One progressive workflow

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---
flowchart LR
    accTitle: Evidence-first development workflow
    accDescr: Investigation leads to planning, implementation, review and evidence; unresolved gaps return to investigation.
    A["Ask<br/>Investigate"] --> P["Plan<br/>Design"]
    P --> G["Agent<br/>Implement"]
    G --> R["Review<br/>Challenge"]
    R --> E["Evidence<br/>Prove"]
    E -. "unresolved gap" .-> A

Legend. Rectangles are workflow stages. Solid arrows show the normal progression; the dashed arrow returns unresolved evidence gaps to investigation.

Explanation. A fluent response is not completion. The loop ends only when the reviewed result meets the acceptance criteria and the recorded checks support it.

Ask, Plan, and Agent are roles. Local and Copilot are VS Code agent harnesses. Cloud is a remote session target. A folder, worktree, Codespace, local machine, or remote ephemeral workspace is an environment.

Begin the journey

Destination Use it for
Prerequisites and accounts Install one environment, activate your own account and understand free-plan limits
Start here Prepare the environment and first session
Learning order Separate required steps, optional extensions and advanced work
Learner downloads Get an individual ZIP and follow extraction, baseline and optional GitHub setup
Curriculum map Follow the complete learning progression
Harness guide Choose where and how an agent executes
Customization primitives Select instructions, prompts, skills, agents, MCP, or hooks
Feature status Check current availability and preview labels
Adventure catalog Browse every guided mission
Hands-on Labs Follow non-fantasy exercises, including Spec Kit modernization
Diagram style Understand monochrome colors, legends and accessibility

The executable source for each mission lives in the GitHub adventure tree and lab tree.

The multilingual site also includes a repository explorer for original code, fixtures, solutions, customizations, media and historical material. Learning pages are available in English, Spanish and Brazilian Portuguese; executable examples and original licenses retain their source text.

[!NOTE] Product guidance was last verified against current official GitHub and Microsoft documentation on 2026-09-05. Recheck the linked source when availability or policy materially affects your task.

Search

Search in English. Source paths and executable examples retain their original text.