You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
1,000 issues analyzed — 216 open (21.6%), 784 closed (78.4%), with 964 opened in the past 7 days alone.
Activity in github/gh-aw is dominated by automation infrastructure: workflow orchestration ("aw, workflow, gh"), failure investigations, and smoke tests together account for the vast majority of volume. The overwhelming majority of issues (962 of 1,000) originate from the app/github-actions bot account, reflecting a highly automated triage and reporting pipeline rather than organic human bug reports. Labeling discipline is strong — only 10 issues (1%) lack labels — and closure is fast, averaging under 15 hours.
View Full Details
📈 Issue Activity Trends
Issue creation volume is heavily concentrated in the most recent days, consistent with a high-frequency automated reporting cadence (daily/hourly workflow-generated issues) rather than steady organic filing. Closure keeps pace closely with opening, indicating the automation triage loop is healthy and not accumulating backlog.
🏷️ Issue Clusters by Theme
The largest cluster ("aw, workflow, gh", 411 issues) covers general agentic-workflow orchestration issues. The second-largest ("failure, agent, workflow", 312) captures failure investigation reports. Smaller but distinct clusters cover smoke testing, Docker sandbox issues, dedicated failure-investigator runs, "squad" game-planning experiments, deep reports, and PR triage/fast-track flows.
Given 740 issues (74%) have no assignee, consider auto-assigning high-signal bug reports to reduce time-to-first-response, even though closure speed overall is strong.
The heavy concentration of automation-generated issues (96% from app/github-actions) suggests reviewing whether all daily/repeated workflow reports need to remain as open-by-default issues, or whether some could be consolidated/closed faster to reduce noise for human triagers.
No stale issues were found, indicating triage is currently keeping pace — maintain this cadence as issue volume grows.
Report generated automatically by the Daily Issues Report workflow Data source: Last 1000 issues from github/gh-aw
reacted with thumbs up emoji reacted with thumbs down emoji reacted with laugh emoji reacted with hooray emoji reacted with confused emoji reacted with heart emoji reacted with rocket emoji reacted with eyes emoji
Uh oh!
There was an error while loading. Please reload this page.
Summary
1,000 issues analyzed — 216 open (21.6%), 784 closed (78.4%), with 964 opened in the past 7 days alone.
Activity in github/gh-aw is dominated by automation infrastructure: workflow orchestration ("aw, workflow, gh"), failure investigations, and smoke tests together account for the vast majority of volume. The overwhelming majority of issues (962 of 1,000) originate from the
app/github-actionsbot account, reflecting a highly automated triage and reporting pipeline rather than organic human bug reports. Labeling discipline is strong — only 10 issues (1%) lack labels — and closure is fast, averaging under 15 hours.View Full Details
📈 Issue Activity Trends
Issue creation volume is heavily concentrated in the most recent days, consistent with a high-frequency automated reporting cadence (daily/hourly workflow-generated issues) rather than steady organic filing. Closure keeps pace closely with opening, indicating the automation triage loop is healthy and not accumulating backlog.
🏷️ Issue Clusters by Theme
The largest cluster ("aw, workflow, gh", 411 issues) covers general agentic-workflow orchestration issues. The second-largest ("failure, agent, workflow", 312) captures failure investigation reports. Smaller but distinct clusters cover smoke testing, Docker sandbox issues, dedicated failure-investigator runs, "squad" game-planning experiments, deep reports, and PR triage/fast-track flows.
Cluster Details
📊 Key Metrics
Volume Metrics
issues_analyzed): 1,000 (Scope: Last 1000 issues)open_issues): 216 (21.6%)closed_issues): 784 (78.4%)Time-Based Metrics
issues_opened_7d): 964issues_opened_30d): 1,000Triage Metrics
issues_without_labels): 10issues_without_assignees): 740stale_issues): 0🏆 Top Labels
👥 Most Active Authors
@app/github-actions@dsyme@sg650@lpcox@strawgate@Etienne-M@theletterf@YoavLax@rbstp@dsibilioStale Issues (No Activity 30+ Days)
None found — all open issues have seen activity within the last 30 days.
Unlabeled Issues
📝 Recommendations
Report generated automatically by the Daily Issues Report workflow
Data source: Last 1000 issues from github/gh-aw
All reactions