- Define a language for the description
- dates, date ranges, metada, headers, list: see https://github.com/hotoo/markline
- Implement the notion of timeline
- Write full project description example
- Implement schema on the client side
- Test if md files can be retrieved from other CVS
- Add logging
- Write unittests
Gitchore collects project management descriptions from git repos via HTTP endpoints. Projects are written using PM Flavored Markdown
Key features/ideas
- Markdown and extensions for project management
- Git-driven
- Decentralized
- Schema verification
Tech. Stack
- Markdown, Javascript, Google Charts, Bootstrap, Python, Flask, Flask-RestX, Flask-SqlAlchemy, SQLite
- for an example, see https://docs.gauge.org/writing-specifications.html?os=linux&language=javascript&ide=vscode
A project description consists of different sections; some of which are mandatory and few are optional.
The sections of a PM Flavored Markdown are as follows:
- Overview
- Description
- Tasks
- Sprints
- Results
Section headings are written in the
In the following example, # Overview is the section heading, followed by tags and steps (statements preceded by *).
Tasks are the executable components of a project that are written by using the Markdown unordered list syntax.
Parameters are used as placeholders instead of actual values. These parameters are used when referring to a table column value of a data table. Data tables are defined at the beginning of a spec. Parameters are also used as values in a Concept. Parameters have the following syntax: .
Login as user and "password"
Verify email text is tag:research
Clone Repo
$ git clone https://github.com/jorge-cardoso/gitchore
$ cd gitchore
Install Modules using a Virtual Environment
$ virtualenv env
$ source env/bin/activate
$ pip3 install -r pip-requirements.txt
Set up the environment
FLASK_DEBUG enables hot reloading.
$ export FLASK_APP=run.py
$ export FLASK_ENV=development
$ export FLASK_DEBUG=1
Directories
- log to keep the logs
- instance to keep the database
Sample Data
samples/ultra_scale_aiops.md
Start the application
$ flask run --host=0.0.0.0 --port=8011