SWEXP Module 01 — Linux & Command Line · Interactive Workspace

Software Engineering Experience Program — work-along starter workspace. This is the hands-on companion to Module 01. The lessons, deep dives, and the full capstone live in the Forge LMS; this repository is where you do the work: open an exercise, complete a solution.sh, run the tests, and push to be graded.

It is designed to run inside the LMS code-server (or any Linux box / Codespace). Every exercise is a small, sandbox-safe Bash task with a bats test spec — no sudo, no mutating your real system. You practise the gradable core of each lesson here; the system-state work (real useradd, hardening sshd, systemd timers) is done in your LMS box and written up in your engineering notebook.

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Quick start

npm install        # installs bats (the only dependency)
npm test           # run every exercise's tests (the spec)
npm run grade      # score yourself exactly like the autograder does
npm run check      # shell-syntax gate (bash -n) over every solution.sh

Work one exercise at a time:

npx bats labs/lab-00-environment/tests   # run just this lab's tests

Open the folder, read its README.md, complete the # TODOs in solution.sh, and re-run the tests until they are green. The tests/*.bats file is the spec — it defines exactly what "done" means.

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How it works

Each exercise folder contains:

File	Purpose
README.md	what the task is and how it maps to the lesson
solution.sh	you edit this — a starter with # TODO guidance (it fails until you complete it)
fixtures/	sample data (an os-release, an access.log, a df dump, …) where needed
tests/*.bats	the grading spec — runs your solution.sh and asserts its effects/output

The autograder (scripts/grade.mjs) auto-discovers every folder under labs/ and assignments/ that has a tests/ directory, runs its bats tests, applies a bash -n syntax gate, and prints a per-exercise score. You get the same report locally (npm run grade) that CI posts on your pull request.

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Exercises

Folder	Topic	What your solution.sh does
labs/lab-00-environment	Environment	Parse /etc/os-release and print PRETTY_NAME
labs/lab-01-filesystem-survey	Filesystem	Count files/dirs and name the largest file in a tree
labs/lab-02-file-recovery	File management	find *.log files and copy them into recovered/
labs/lab-03-permissions	Permissions	Tighten a secret to 640, list world-writable files
labs/lab-04-users-groups	Users & groups	Print a group's members from an /etc/group file
labs/lab-05-log-investigation	Text processing	Busiest client IP + total 4xx/5xx count from an access log
labs/lab-06-pipeline	Pipelines	Status-code tally + tee 5xx lines to errors.log
labs/lab-07-dotfiles	Shell environment	Idempotently symlink dotfiles into a home dir
labs/lab-08-onboarding-script	Bash automation	Prereq check + idempotent onboarding
labs/lab-09-process-management	Processes	Find the highest-%CPU PID from ps aux output
labs/lab-10-networking	Networking	List unique listening TCP ports from ss -ltn output
labs/lab-11-ssh	SSH	Emit a valid ~/.ssh/config Host block
labs/lab-12-packages	Packages	Report which wanted packages are missing
labs/lab-13-scheduling	Scheduling	Build the weekday-09:00 crontab line
labs/lab-14-resources	Storage	Flag filesystems over a use% threshold from df -P
assignments/capstone	Capstone core	Health check: world-writable + disk + missing packages

The numbering matches the lessons. Labs 00–14 are the per-lesson cores; the capstone integrates several of them into one report. The full CAP-9000 capstone (idempotent provision.sh, systemd service, hardened SSH, ops layer, docs, v1.0-capstone tag) is completed in your LMS box — see assignments/capstone-brief.md and the submission template.

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Grading & submission

1. Complete an exercise's solution.sh; run npm test (or the per-lab npx bats …).
2. When npm run grade shows the exercise green, commit and push:

   git add -A
   git commit -m "feat: complete lab-00 environment"
   git push

3. The Autograde GitHub Action (.github/workflows/autograde.yml) re-runs the grader on every push and on pull requests, and posts your score to the job summary (and as a PR comment). The check is green only when every exercise passes and every solution.sh parses cleanly.

For lesson write-ups and reflections, copy assignments/submission-template.md (and assignments/capstone-submission-template.md for the capstone) into your notebook.

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Repository layout

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├── README.md                  ← you are here
├── package.json               ← npm scripts: test / grade / check
├── scripts/grade.mjs          ← the autograder (don't edit)
├── .github/workflows/         ← Autograde CI
├── .devcontainer/             ← one-click Codespaces / code-server setup
├── labs/                      ← lab-00 … lab-14 (README + solution.sh + tests)
├── assignments/               ← capstone core + submission templates
└── resources/                 ← cheatsheet, glossary, AI-workflow guide, setup notes

The golden rule of this module: never run a command — or paste one from an AI — that you cannot explain. Understanding is the deliverable; the green check is just the evidence.