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Read your first pull request score

Open the evidence behind quality, effort, velocity, and AI attribution.

Audience
engineering leaders, developers
Required role
Any member with Measure access.
Product access
Measure
Navigation path
Measure > Pull requests > select a pull request

Outcome

Explain what a pull request score says, trace it to evidence, and identify incomplete analysis.

Before you begin

  • A repository with at least one processed pull request
Open a scored pull request to review its breakdown and the evidence behind each result.
The score summary and evidence remain available when reviewing a pull request on mobile.

Open a scored pull request

Open Measure and select a pull request that shows completed analysis. The detail page connects the summary metrics to commits, files, authorship evidence, and the original GitHub record.

Read the score summary

Quality reflects the rubric-based assessment of correctness and maintainability. Effort estimates how much of the change is meaningful work rather than churn. Velocity combines deterministic change size with the effort multiplier. Read these together, not as independent quotas.

Inspect evidence

Expand the score explanation and review the commits, changed paths, exclusions, and AI attribution confidence. Use the GitHub link to compare source evidence when a result is surprising.

Resolve an unexpected score

Confirm that the pull request is merged, all commits are synchronized, and analysis is complete. Administrators can review scoring settings or request reanalysis after correcting source data. Use feedback from the pull request page when the evidence is complete but the explanation still looks wrong.

Last reviewed 2026-07-18 by the FeatureFactory product team.

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