Quickstart

The goal of autograder-setup is to take a plain Rust project template and make it GitHub Classroom–ready in under a minute.
This quickstart shows the minimal workflow instructors need to get up and running.

Step 0: Explore the CLI

autograder-setup --help

This prints top-level usage and shows all available subcommands. Use --help after any command to see its options.

Step 1: Initialize an autograder configuration

autograder-setup init

• Scans your project recursively for all #[test] functions.
• Builds a JSON config at .autograder/autograder.json.
• Automatically adds optional checks (Clippy linting, commit count) unless disabled.

Why it’s useful: this JSON file acts as a single source of truth for grading. You can tweak points, timeouts, or thresholds before generating CI.

Step 2: Review and adjust points

Open .autograder/autograder.json in your editor:

[
  {
    "meta": { "name": "add_two", "description": "check add_two works", "points": 2, "timeout": 10 },
    "type": "cargo_test",
    "manifest_path": "Cargo.toml"
  }
]

You can increase/decrease point values, set timeouts, or change descriptions here. This makes grading customizable.

Step 3: Build the GitHub Actions workflow

autograder-setup build

• Reads .autograder/autograder.json.
• Emits .github/workflows/classroom.yaml with one job per test/check.
• Uses the official classroom-resources/autograding-* actions.

Why it’s useful: this workflow is what Classroom actually runs when grading. It ensures consistency between local tests and CI.

(Optional) Step 4: Generate a grading table

autograder-setup table --to-readme

• Produces a Markdown table of test names, descriptions, and points.
• Appends to your README.md, or copy to clipboard by default.

Why it’s useful: students can see exactly how they’ll be graded up front.

Step 5: Reset if needed

autograder-setup reset

Deletes .autograder/ and the generated workflow, so you can start fresh.

Summary

• init = discover tests and set up config
• build = turn config into a ready-to-run workflow
• table = generate a transparent grading table for students
• reset = undo everything

Together, these steps let you go from zero → reproducible autograder in under a minute, while keeping grading criteria explicit for both instructors and students.