> For the complete documentation index, see [llms.txt](https://docs.oobeya.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.oobeya.io/use-cases/proactive-issue-detection.md).
# Proactive Issue Detection (Engineering Insights)
### Spot Problems Before They Become Bottlenecks
Instead of overwhelming dashboards and vague metrics, Oobeya highlights what truly matters: **the bottlenecks slowing down your teams**. From [unreviewed pull requests ](/team-insights-and-symptoms/symptoms-catalog/s9-unreviewed-pull-requests.md)to [high lead times](/team-insights-and-symptoms/symptoms-catalog/s12-high-lead-time-for-changes-dora.md), Symptoms proactively surface engineering issues, along with actionable recommendations to fix them.
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#### **Stop guesswork — get instant visibility into real problems**
Symptoms automatically surface issues like delayed reviews, excessive rework, or low-quality code, so you don’t need to dig through dashboards or reports.
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#### **Move from metrics to meaningful actions**
Each symptom includes contextual insights and tailored improvement suggestions — enabling your teams to act quickly and effectively.
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#### **Empower teams to take ownership of their delivery**
Teams see their signals in real-time, understand what needs attention, and develop a habit of continuous improvement without micromanagement.
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Symptoms are intelligent signals derived from your engineering data — highlighting code, process, or collaboration issues before they impact delivery.
Oobeya Symptoms
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:information\_source: **View the full Symptoms documentation:**
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[Symptoms Catalog](/team-insights-and-symptoms/symptoms-catalog.md)
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