CIT Course Tools

cit-course-tools is a small, cross-platform collection of command-line tools for Computer Information Technology courses. The initial release creates the recommended CIT 495 workspace and, when requested, generates a privacy-conscious hardware report for local-AI and llama.cpp troubleshooting.

The utility does not download or install llama.cpp, AI models, or any other external software. It has no third-party runtime dependencies.

Installation

For an isolated command-line installation, use pipx:

pipx install cit-course-tools

It can also be installed into an existing Python environment:

python -m pip install cit-course-tools

Python 3.10 or newer is required.

Usage

Navigate to the intended course workspace and use .:

cd /path/to/CIT495
cit-course .

Or supply the workspace directory directly:

cit-course --all /path/to/CIT495

On Windows, a path containing spaces should be quoted:

cit-course --all "C:\Users\Student\Courses\CIT495"

Available operations are:

cit-course . --scaffold
cit-course . --specs
cit-course . --all
cit-course --help

With no operation flag, the utility runs interactively. If the target is omitted, it defaults to the current directory.

The utility stays in its installed Python environment. It is never copied or moved into the course workspace.

Workspace structure

The initial scaffold is:

CIT495/
├── README.md
├── models/
│   └── README.md
├── llama.cpp/
│   └── README.md
├── labs/
│   ├── README.md
│   └── lab01/
│       ├── logs/
│       ├── prompts/
│       └── results/
├── project/
│   └── README.md
└── specs/
    └── README.md

Later lab directories are added as the semester progresses. The persistent semester system belongs in project/, while lab evidence belongs under labs/.

Scaffolding is idempotent: missing components are restored, populated README files are preserved, and student files are not deleted.

Hardware report and privacy

The optional report is written to:

<workspace>/specs/hardware_specs.txt

It may include the operating system, architecture, CPU, CPU counts, memory, storage, relevant instruction sets, Python version, and detected GPUs. Some details may be unavailable on a particular system.

The report intentionally excludes usernames, serial numbers, device IDs, MAC and IP addresses, license keys, credentials, precise location, and unrelated software inventories. Students should still review the report before sharing it.

Development

Run the standard-library test suite with:

python -m unittest discover -s tests -v

See PUBLISHING.md for the release checklist.