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Audit of 115 workflow runs from 2026-04-13 (today's batch). Overall health is 80% success rate (32 success / 4 failure, excluding 75 skipped and 4 in-progress). The dominant root cause of all 4 failures was GitHub API rate limiting caused by concurrent workflow execution. No missing tools or missing data were reported. Token consumption was concentrated in one run: Daily Workflow Updater (459K tokens, $4.56 estimated cost).
Note on trend charts:matplotlib is not available in this runner environment. Charts will be added in a future audit once the dependency is resolved. Audit data has been seeded to repo memory for trend tracking going forward.
Health Summary
Metric
Value
Total Runs
115
✅ Success
32 (80%)
❌ Failure
4 (20%)
⏭️ Skipped
75
🔄 In Progress
4
Total Tokens
12,478,749
Estimated Cost
$4.56
Missing Tools
0
Missing Data
0
Total Errors
0 (reported)
Failures (4 runs)
All 4 failures share the same root cause: GitHub API rate limit exceeded for installation — triggered by many workflows firing in overlapping windows around 20:30–20:35 UTC.
Rate limit exceeded during issue lock for issue #26074
Recommendations:
Stagger daily/scheduled workflow cron times to avoid concurrent API bursts (e.g., offset by 5–10 minutes)
Add retry logic with exponential backoff in activation steps that call GitHub API
For Daily DIFC Integrity-Filtered Events Analyzer: add a file-existence guard before calling upload_artifact to prevent failures when chart generation is skipped
Rate Limit Error Detail (AI Moderator)
##[error]Failed to lock issue: API rate limit exceeded for installation.
##[error]ERR_NOT_FOUND: Failed to lock issue #26074: API rate limit exceeded for installation.
##[error]❌ Rate limit check failed: API rate limit exceeded for installation.
Timestamp: 2026-04-13T20:35:00–16 UTC — multiple workflows overlapping in a ~16-second window.
23 skipped runs received overkill_for_agentic LOW assessments. These are primarily PR/issue comment triggers where the event didn't match the workflow's filter conditions — resulting in zero turns, zero tool calls, read-only actuation. This is expected behavior; the assessment is informational only and does not indicate a problem.
Patterns (First Baseline)
This is the first audit run — repo memory has been seeded with today's data. Future runs will surface trends and regressions. Key patterns to watch:
API rate limiting — present today across 4 runs; will track frequency
Daily Workflow Updater efficiency — 12 turns for repo maintenance is a regression target
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Overview
Audit of 115 workflow runs from 2026-04-13 (today's batch). Overall health is 80% success rate (32 success / 4 failure, excluding 75 skipped and 4 in-progress). The dominant root cause of all 4 failures was GitHub API rate limiting caused by concurrent workflow execution. No missing tools or missing data were reported. Token consumption was concentrated in one run: Daily Workflow Updater (459K tokens, $4.56 estimated cost).
Health Summary
Failures (4 runs)
All 4 failures share the same root cause: GitHub API rate limit exceeded for installation — triggered by many workflows firing in overlapping windows around 20:30–20:35 UTC.
Recommendations:
Daily DIFC Integrity-Filtered Events Analyzer: add a file-existence guard before callingupload_artifactto prevent failures when chart generation is skippedRate Limit Error Detail (AI Moderator)
Timestamp: 2026-04-13T20:35:00–16 UTC — multiple workflows overlapping in a ~16-second window.
Medium-Severity Agentic Issues
Daily Workflow Updater — §24366711283
Agent: Copilot | Status: ✅ Success | Duration: 5m43s | Turns: 12 | Tokens: 459K
resource_heavy_for_domainpoor_agentic_controlpartially_reduciblemodel_downgrade_availableRecommendations:
/tmp/gh-aw/agent/— this would cut turns by ~50%engine.model: gpt-4.1-miniorclaude-haiku-4-5to reduce inference costWorkflow Coverage
All Active Workflows (21 distinct)
Overkill Assessments (Skipped Runs)
23 skipped runs received
overkill_for_agenticLOW assessments. These are primarily PR/issue comment triggers where the event didn't match the workflow's filter conditions — resulting in zero turns, zero tool calls, read-only actuation. This is expected behavior; the assessment is informational only and does not indicate a problem.Patterns (First Baseline)
This is the first audit run — repo memory has been seeded with today's data. Future runs will surface trends and regressions. Key patterns to watch:
References:
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