Inspiration

Codegeist was my first real exposure to the Atlassian ecosystem. Before this hackathon, I had never used Jira, Confluence, or Atlassian Forge. When I was invited to participate, I saw it as an opportunity to learn something completely new and challenge myself.

As I started learning Jira, I noticed that even when work was completed, the journey of an issue often felt frustrating. Tickets moved back and forth, ownership changed, and work stayed “in progress” without clear reasons. These struggles were familiar from real-world team experiences, yet they were invisible in standard Jira metrics.

That gap between what Jira reports show and how work actually feels became the foundation for Backspot.


What it does

Backspot helps teams see the hidden friction in their Jira workflows.

It analyzes the historical behavior of issues to detect:

  • Rework loops caused by repeated status changes
  • Assignee churn indicating unclear ownership
  • Silent delays where work sits idle in active states

These signals are combined into a Friction Score, giving teams a simple, human-readable way to understand why certain issues were harder than others.

At the project level, Backspot highlights recurring patterns so teams can fix workflow problems early, before they turn into stress or burnout.


How we built it

Backspot is built entirely on Atlassian Forge and runs inside Jira Cloud. All data remains within the Atlassian platform, with no external services involved.

The app processes Jira issue changelogs and applies a heuristic scoring model:

[ \text{Friction Score} = 2.0 \times \text{Reopens} + 1.5 \times \text{Loops} + 1.0 \times \text{Backtracks} + 0.5 \times \text{Assignee Changes} + 0.1 \times \text{Idle Days} ]

Insights are displayed directly inside Jira through an issue-level panel and a project-level overview, keeping everything simple and close to where teams already work.


Challenges we ran into

This hackathon was personally challenging. While building Backspot, my mother was unwell, which placed a lot of emotional stress on me and significantly reduced the time I could consistently dedicate to the project. There were moments where it was difficult to focus, but I made a conscious decision to continue learning and building whenever I could.

At the same time, I was learning the Atlassian ecosystem from scratch. Understanding Jira concepts, Forge limitations, and deployment workflows required patience and repeated trial and error. I was also working without access to paid or premium Atlassian features, which forced me to design within strict constraints.

Despite these challenges, I never stopped learning. I adjusted my pace, narrowed the scope, and focused on building a meaningful core feature set rather than chasing complexity.


Accomplishments that we're proud of

  • Learned Jira, Confluence, and Atlassian Forge entirely during the hackathon
  • Built a working Jira Forge app under personal and technical constraints
  • Shipped a product that reflects real workflow pain points
  • Stayed committed to learning and building during a stressful period
  • Created something I am genuinely proud of despite limited time and resources

What we learned

This experience taught me that progress doesn’t always come from ideal conditions. Even under stress and uncertainty, consistent learning and small steps can lead to meaningful results.

I also learned that many workflow problems are rooted in hidden process friction, not individual effort. By making this friction visible, teams can improve how they work without placing blame.

Technically, I gained hands-on experience with Atlassian Forge, Jira’s data model, and building within platform constraints.


What's next for Backspot

Next, I plan to:

  • Improve friction detection accuracy
  • Add trend analysis across sprints
  • Allow configurable thresholds per workflow
  • Expand insights to long-term projects and releases

Backspot represents both a technical learning journey and a personal one — proving that meaningful work can still be done during difficult times.

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