Open the "Skills for Jira" administration console (“Apps / Manage your apps” menu)
Open the "Skills" tab
Modify/delete the default skills, add your own, and organize them into categories as you see fit
You can export your skills configuration for backup or migration purposes
You can also always reset your skills configuration to the out-of-the-box defaults
Skill Tree size limitation
Due to the platform limitation, your skill tree can not exceed 32kb in JSON form. If the current limitation is insufficient for your needs, please contact Support.
You can override your skills on the Project or Custom Field Context levels. It can be done in the respective configuration sections:
Open the "Skills for Jira" app configuration section
Open the "Users" tab
Search for users you want to update
Use the "Modify Skills" button to configure user's skills
Note: Changes to user's skills do not immediately propagate to all issues. Propagation might take 1 hour or more, depending on the size of your Jira instance.
User skills information is foundational for all Skills for Jira features. It is used for showing you the Organization Knowledge Graph, qualified experts on each issue, self-service assignments, expert-only transitions or analytics.
Open the Settings / Apps / Skills for Jira - Administration Console
Open the "Assignments" tab
Configure the Default Queue with Self-service assignment method or create specialized work queues, prioritize and assign them to user groups
Configure your work queues:
Scope
JQL filter that represent the scope of issues the relevant user groups will work on
The JQL filter must include issues waiting to be pulled as well as issues in progress
The JQL filter must be shared with your users
Ready for work (statuses)
Issues in these statuses will be pulled by your users when clicking “Get next task” in their dashboards
In progress (statuses)
When you users pull assignments, they will be transitioned into one of these statuses, depending on your workflow configuration
Qualification (field)
This field defines the skill requirements for workers. Only issues for which the user has all the necessary skills show up in user’s work queue.
Once Self-service assignments are enabled, your users will gain access to their personal Apps / Skills for Jira - User Console / Assignments dashboard as well as the Self-service assignments gadget that you can add to your existing dashboards
The dashboard will allow them to get their next most important task in one click
Skills for Jira will inspect all work queues relevant for the user and assign the highest priority task to the user
Use the "Skillset field name-Experts" field as a target
“All of the following conditions” should be true (condition group)
“Skillset field name” is empty
“Skillset field name-Experts” is empty
Workflow conditions in the demo are configured to restrict transition to “In Progress” to Experts only if the expertise requirements are specified. Otherwise anyone can work on the task.
The extra conditions are there to allow the transition when no skills are selected (in which case the Experts field will be empty as well).
By attaching this Workflow Condition to transitions to "In Progress", you could prevent any user who lacks the required expertise from getting the issue
You can get much more creative with "Skillset" fields and Jira workflows if you are using Jira addons that offer custom workflow extensions like Script Runner for Jira Cloud.
For example, you could prevent transitions if there's another user with the required skillset, who has better availability. You could do that by reading the users property of the "Skillset" field and analyzing backlog for all qualified users.
6. Start specifying skill requirements for your issues
Make sure that your new Skillset field is added to your issue screens (i.e. Create, View screens)
Open any issue and click on your Skillset field. You will see your customized skill tree to choose skills from
Use search or tree navigation to find and select skills
Submit and wait while Skills for Jira finds the matching experts
Matching experts will take from 3 seconds to 1.5 minutes. The delay will be reduced or removed as the Atlassian Forge platform evolves.
These skill requirements are then used to show you qualified experts, give you your next most important task on demand, help you spot knowledge gaps or bottlenecks and more.