https://github.com/robertdebock/docker-scoring

An application to analyse knowledge spread on technologies in a team.
https://github.com/robertdebock/docker-scoring

analysis awk bash bc cut docker grep sed team wc

Last synced: 3 months ago
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An application to analyse knowledge spread on technologies in a team.

• Host: GitHub
• URL: https://github.com/robertdebock/docker-scoring
• Owner: robertdebock
• License: apache-2.0
• Created: 2017-02-07T20:15:04.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2017-03-30T14:24:49.000Z (over 8 years ago)
• Last Synced: 2025-02-16T10:28:52.361Z (8 months ago)
• Topics: analysis, awk, bash, bc, cut, docker, grep, sed, team, wc
• Language: Shell
• Size: 8.79 KB
• Stars: 1
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Scoring

An application to analyse knowledge spread on technologies in a team.

Imaging you want to understand strong and developmental areas within a group

of people. This tool can be used to analyse a teams knowlegde.

## INPUT

Fill details in the file "input.txt". Every line start with a technologie,

and appends a score per person. Higher is better. For example like this:

    Docker 1 3 5

    Vim    3 2 1

    HTML   5 4 5

This means:

- There are three people in the team, because there are 3 columns.

- There are three technologies being analysed, because there are 3 lines.

- Knowledge on Docker is varying from low (1) to higher (5).

- Knowlege on HTML is pretty high.

### LIMITATIONS ON INPUT

Please make sure:

- Every line starts with a "technology".

- There are at least two lines.

- Every technology is followed by two or more scores, separated by a

  (white)space.

- A technology is one word.

- A score is one number, without periods or commas.

More samples make a more interesting result, I would suggest listing seven or

more technologies, and four or more scores/people.

## ANALYSE

You can hook up a commit to CI, travis.yml has been provided.

You can also manually run the scripts. Using Docker is the most logical:

     

    docker tag $(docker build . -q) scoring

    docker run scoring

## OUTPUT

An example output can look like this:

    # Analysis of technology knowledge.

    Our team can clearly work with Git.

    We all feel insecure about Python.

    We are most similarly knowledgable on Puppet.

    Our expertise level differs most for Packer.

    The team may miss some knowledge about Packer.

    Not everybody in the team has knowledge on Packer.

## PRESENTING

The output is displayed in MarkDown format, you can use reveal-md to

present the output, or simply "cat" it. It's quite readable.