Choose a fair comparison
Start with the release date in UTC and select a post-release range that matches the question: seven days for a focused launch response, or 30 days for a slower documentation and adoption cycle. Compare it with the immediately preceding range of the same length.
Check whether either range includes unusual holidays, outages, unrelated announcements, or missing collection days. A custom comparison is useful when the immediately previous period is not representative, but the reason for choosing it should be recorded.
Read several signals together
Views and unique views describe repository attention. Clones and unique clones describe full repository acquisition events. Stars, forks, and genuine subscribers add longer-lived adoption context. Referrers and popular paths can show where attention arrived and which content people opened.
Optional release asset downloads can add distribution context when the repository publishes assets and the metric has been refreshed. Do not imply that every clone or download came from the release announcement.
- Views: repository attention
- Clones: full clone events, excluding fetches
- Stars and subscribers: distinct forms of ongoing interest
- Referrers and paths: source and content context
- Release downloads: optional, cumulative, and separately refreshed
Annotate the event you actually know
Add a manual annotation with the release name and date to the selected repository report. Manual annotations avoid inventing a release event when tags, prereleases, rebuilt assets, or release timing need maintainer judgment.
Run the report with the same metric and granularity across both periods. Daily points help inspect the immediate shape; weekly aggregation can reduce noise over a longer window. Keep the accessible table with any shared interpretation so exact values remain available.
Write a defensible conclusion
A sound conclusion names the metric, ranges, observed change, and limitations: for example, repository views were 24 percent higher in the seven days after the release than in the preceding seven days, while clones were roughly flat. That statement reports evidence without claiming causation.
Follow up by checking referrers, popular paths, issue activity, package or release distribution, and whether the pattern persists. RepoLogbook's anomaly flags are deterministic historical comparisons, not forecasts or causal models.
Frequently asked questions
Can repository traffic prove a release caused growth?
No. A before-and-after change is correlation. Other announcements, search activity, automation, seasonality, or unrelated events may explain some or all of it.
Which date ranges should be compared?
Use equal-length UTC ranges and include enough ordinary days to avoid comparing a launch day with an unrepresentative baseline.
Does RepoLogbook add release annotations automatically?
No. The current workflow uses manual annotations so maintainers control which project events appear in a report.
Are release downloads always available?
No. Release asset downloads are an optional cumulative signal refreshed through the supported owner-requested workflow and should show their own freshness.