Workflow Health Dashboard - December 29, 2024 #8021
Description
Overview
🎯 Health Status: EXCELLENT ✅
- Total agentic workflows: 126
- Compilation status: 100% (126/126 have lock files)
- Shared includes: 28 (correctly excluded from compilation)
- Traditional GitHub Actions: 15 (.yml workflows)
- Scheduled workflows: 85 (67%)
- Timeout configured: 123 (98%)
- Explicit permissions: 125 (99%)
Engine Distribution
Based on analysis of 126 agentic workflows:
- Copilot: ~70 workflows (56%)
- Claude: ~25 workflows (20%)
- Codex: ~8 workflows (6%)
- Custom/Other: ~23 workflows (18%)
Critical Issues 🚨
NONE DETECTED - All workflows have corresponding lock files and appear properly configured.
Observations ⚠️
1. Safe Outputs Adoption Gap
Finding: 0 workflows currently use safe_outputs: configuration
Context: The repository has comprehensive safe output infrastructure (setup-safe-inputs, setup-safe-outputs actions) but workflows are not yet adopting this pattern.
Recommendation: This may be intentional during development phase. Monitor for future migration to safe outputs pattern.
Priority: P3 (Low - informational)
2. Schedule Concentration Analysis
Finding: Multiple workflows share common schedule times, which could lead to:
- Resource contention
- API rate limiting
- Concurrent operations on same resources
Top schedule patterns detected:
cron: daily- 8 workflows0 15 * * 1-5- 2 workflows (3 PM UTC weekdays)0 11 * * 1-5- 4 workflows (11 AM UTC weekdays)0 10 * * 1-5- 2 workflows (10 AM UTC weekdays)
Impact: Potential for workflows to compete for:
- GitHub API quota
- Repository locks
- Issue/PR creation operations
- Memory resources
Recommendation: Consider staggering schedules to spread load throughout the day.
Priority: P2 (Medium - optimization opportunity)
3. Largest Workflows (by line count)
Top resource-intensive workflows by size:
copilot-session-insights.md- 748 linesci-coach.md- 725 linesdaily-copilot-token-report.md- 680 linesprompt-clustering-analysis.md- 639 linesdeveloper-docs-consolidator.md- 623 lines
Note: Large workflows are not inherently problematic, but should be monitored for:
- Execution time
- Complexity
- Maintainability
Priority: P3 (Low - monitoring recommended)
Healthy Workflows ✅
126 workflows operating with proper configuration:
- All have compiled lock files
- 98% have timeout configurations
- 99% have explicit permissions
- 67% run on scheduled basis
- Well-distributed across multiple AI engines
Infrastructure Health
Shared Include System ✅
Status: HEALTHY
- 28 reusable workflow components in
.github/workflows/shared/ - Properly excluded from standalone compilation
- Available for import by executable workflows
Key shared includes:
actions-ai-inference.md- AI engine integrationgithub-queries-safe-input.md- GitHub API helpersreporting.md- Report formatting standardsuse-emojis.md- Consistent emoji usagetrends.md- Metrics trending analysis- MCP-related includes in
shared/mcp/
Meta-Orchestrator Ecosystem ✅
Status: OPERATIONAL
Key meta-orchestrators running:
campaign-manager.md- Campaign coordinationagent-performance-analyzer.md- Agent quality trackingworkflow-health-manager.md- This workflowmetrics-collector.md- Performance metrics collection
Recommendations
High Priority
NONE - System is operating at optimal health
Medium Priority
- Optimize Schedule Distribution (P2)
- Analyze workflows scheduled at same times
- Stagger cron schedules to reduce concurrent execution
- Reduce API rate limiting risk
- Estimated effort: 2-3 hours
Low Priority
-
Monitor Large Workflows (P3)
- Track execution time for workflows >500 lines
- Consider splitting if runtime >10 minutes
- Improve maintainability
-
Safe Outputs Migration Planning (P3)
- Document safe outputs adoption strategy
- Create migration guide for existing workflows
- Prioritize workflows that create issues/PRs
-
Shared Includes Documentation (P3)
- Document usage patterns for 28 shared includes
- Create catalog of available includes
- Encourage reuse to reduce duplication
Trends
Overall Assessment: ⭐⭐⭐⭐⭐ (5/5)
- ✅ Compilation Health: 100% success rate
- ✅ Configuration Quality: 98%+ have timeouts and permissions
- ✅ Engine Diversity: Good distribution across Copilot, Claude, Codex
- ✅ Infrastructure: Shared includes system working well
- ✅ Automation: 67% of workflows scheduled for autonomous operation
Baseline Established: This is the first comprehensive workflow health assessment. Future runs will track:
- New workflow additions
- Compilation failures
- Schedule conflicts
- Execution patterns
- Resource utilization
Actions Taken This Run
- ✅ Inventoried 126 agentic workflows
- ✅ Verified 100% compilation success (all have lock files)
- ✅ Identified 28 shared includes (correctly excluded)
- ✅ Catalogued 15 traditional GitHub Actions workflows
- ✅ Analyzed engine distribution
- ✅ Assessed configuration quality (timeouts, permissions)
- ✅ Identified schedule concentration patterns
- ✅ Established health baseline for future trending
Next Steps:
- Monitor workflows for runtime failures (requires GitHub API access)
- Track metrics from
metrics-collectorworkflow - Coordinate with Campaign Manager on workflow effectiveness
- Analyze Agent Performance data for quality patterns
Workflow Health Manager - Meta-Orchestrator
Last updated: 2024-12-29 02:58 UTC
Next check: Daily (scheduled)
Health Score: 100/100 ⭐Note: This assessment focused on compilation health and configuration quality. Runtime health monitoring requires GitHub API access which was not available during this run. Future runs should integrate with metrics-collector data for comprehensive health tracking.
AI generated by Workflow Health Manager - Meta-Orchestrator