Understanding RepoPulse's intelligence engine and how insights are generated
RepoPulse uses rule-based intelligence to analyze GitHub repositories. Each insight is generated by evaluating specific metrics against predefined thresholds. Insights are categorized by severity level and include confidence scores based on data reliability.
Critical issues requiring immediate attention
Potential issues that should be monitored
Positive indicators and healthy patterns
Repository has not been updated in 90+ days
Less than 0.1 commits per day over the last 90 days
Repository shows regular but not exceptional activity
Average time to respond to issues exceeds 24 hours
Pull requests take more than 7 days to merge on average
Issues and PRs are handled promptly
Limited stars and forks despite project age
Minimal increase in stars and forks over time
Consistent increase in community engagement
More than 50 open issues without recent resolution
Less than 70% of issues resolved in 90 days
Issues are regularly resolved and addressed
Project relies on a single maintainer
Small contributor base relative to project size
Multiple active contributors from different backgrounds
Strong evidence from reliable data sources with clear patterns
Moderate evidence with some uncertainty or limited data
Limited data or conflicting signals requiring further investigation
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