How to Use the Command 'onefetch' (with Examples)

Onefetch is a powerful command-line tool for displaying project information and code statistics for a local Git repository. It’s a popular choice among developers for its ability to provide a comprehensive overview of project metrics, such as the number of commits, the main programming languages used, contributors, and other insightful statistics. Easy to use and customizable, Onefetch is an excellent tool for anyone looking to quickly analyze the composition and contributors of a Git project.

Display Statistics for the Git Repository in the Current Working Directory

Code:

onefetch

Motivation:

Often, developers work within a single Git repository and need to quickly generate insightful statistics without navigating away from their working directory. Onefetch provides an easy way to grab crucial information on your current Git repository with just one command. This can help in understanding the current state of the project and can be used as a quick status check.

Explanation:

The onefetch command, without any additional arguments, defaults to scanning and analyzing the Git repository present in the current working directory. It will gather information such as the primary language, the number of files, top contributors, and various other metrics that provide a snapshot of the project.

Example Output:

Language:  Python
Total Files:  75
Authors: 5
Most Active Author: Alex
Top Languages:
  1. Python 80%
  2. JavaScript 10%
  3. CSS 5%

Display Statistics for the Git Repository in the Specified Directory

Code:

onefetch path/to/directory

Motivation:

When managing multiple repositories, it is sometimes necessary to analyze a repository that is not in the current working directory. This command allows a user to specify the path to a specific directory, making it easy to retrieve detailed statistics on any repository without the need to change the working directory.

Explanation:

Adding a path after the onefetch command specifies the directory of the Git repository to analyze. This flexibility allows users to target any local Git repository they have access to, providing insights for various projects with a single command.

Example Output:

Repository at 'path/to/directory':
Language:  JavaScript
Total Files:  50
Authors: 8
Most Active Author: Jamie
Top Languages:
  1. JavaScript 90%
  2. HTML 5%
  3. JSON 5%

Ignore Commits Made by Bots

Code:

onefetch --no-bots

Motivation:

In modern software development, automated bots often generate commits. While these can be valuable for specific processes, they may clutter statistics when analyzing human contributions. Ignoring bots can provide a cleaner view of the genuine human effort behind a project, which is useful for understanding real team dynamics and contributions.

Explanation:

The --no-bots flag instructs Onefetch to exclude any commits that are identified as being made by bots. This can be a crucial detail for analysis, especially in active repositories where bots handle frequent integrations and deployments.

Example Output:

Ignoring bots...
Language:  Ruby
Total Files:  120
Authors: 15
Most Active Author: Sam
Top Languages:
  1. Ruby 75%
  2. Java 20%
  3. YAML 5%

Ignore Merge Commits

Code:

onefetch --no-merges

Motivation:

Merge commits, while necessary for integrating changes, may not accurately represent individual contributions if numerous or complex. Ignoring these can offer a clearer picture of direct, purposeful coding contributions, enabling a better assessment of the coding activity in a project.

Explanation:

The --no-merges flag tells Onefetch to exclude merge commits from its statistics. This focuses the data on individual commit actions, which are typically more relevant when summarizing direct development efforts.

Example Output:

Ignoring merge commits...
Language:  Go
Total Files:  90
Authors: 20
Most Active Author: Lee
Top Languages:
  1. Go 85%
  2. Shell 10%
  3. Markdown 5%

Don’t Print the ASCII Art of the Language Logo

Code:

onefetch --no-art

Motivation:

Some users prefer a minimalist output, especially when running multiple scripts or viewing data programmatically. Omitting the ASCII art of the language logo can simplify the output, making it easier to read or parse.

Explanation:

The --no-art flag eliminates the decorative ASCII art that generally accompanies the output, reducing the visual clutter for users who prioritize the content over aesthetics.

Example Output:

Language:  PHP
Total Files:  100
Authors: 10
Most Active Author: Chris
Top Languages:
  1. PHP 95%
  2. SQL 5%

Show n Authors, Languages, or File Churns

Code:

onefetch --number-of-authors 5

Motivation:

Customizing the number of authors, languages, or file churns listed allows users to gain insights tailored to their specific needs, focusing on the most relevant contributors or languages used, especially in larger teams or multi-language projects.

Explanation:

The --number-of-authors, --number-of-languages, or --number-of-file-churns options allow the user to specify the number (n) of top contributors, languages, or files with high change rates to display, respectively. This customization is useful when the default numbers (3 for authors and file churns, 6 for languages) do not suffice.

Example Output:

Top 5 Authors:
  1. Taylor
  2. Morgan
  3. Casey
  4. Jordan
  5. Bailey
Top 3 Languages:
  1. Python 85%
  2. JavaScript 10%
  3. HTML 5%

Ignore Specified Files and Directories

Code:

onefetch --exclude path/to/exclude

Motivation:

Particularly in repositories with generated files or large data files that aren’t part of the core functionality, excluding certain files or directories can help focus the analysis on only the relevant source files, providing cleaner and more informative statistics.

Explanation:

The --exclude option enables users to specify files or directories to be ignored during the analysis. This is particularly useful for omitting data files, build artifacts or temporary files that do not need to be counted towards the statistics.

Example Output:

Excluding specified paths...
Language:  Java
Total Files:  70
Authors: 18
Most Active Author: Jamie
Top Languages:
  1. Java 80%
  2. XML 15%
  3. JSON 5%

Only Detect Languages from the Specified Categories

Code:

onefetch --type programming

Motivation:

In large projects encompassing multiple types of files such as prose (documentation), markup, and programming scripts, focusing only on a specific category (e.g., programming) can streamline the analysis to reflect only those files of interest, particularly for developers focused on code rather than documentation.

Explanation:

The --type option allows users to filter the detected languages by specific categories: programming, markup, prose, or data. This can be valuable when the project contains diverse file types and a more targeted analysis is needed.

Example Output:

Languages (programming only):
  1. C++ 50%
  2. C 20%
  3. Python 30%

Conclusion:

Onefetch serves a unique niche by providing quick and detailed insights into a Git repository’s statistics directly from the command line. By offering numerous customization flags, it tailors to a variety of needs, whether the user desires a comprehensive view or a more focused analysis. This flexibility makes it an essential tool for any developer looking to keep an informed overview of their software projects.