Portfolio Dashboard - 2025-12-22 #7231
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✅ Plan CreatedI've created a structured implementation plan to address the cost optimization opportunities identified in this portfolio analysis. 📋 Tracking IssueParent Issue: #aw_7231a8f9b3c1 - "Optimize workflow costs - Reduce monthly spend by 18-26%" 🎯 Sub-Issues Created
💰 Expected Impact
The sub-issues contain detailed implementation steps, acceptance criteria, and specific configuration changes needed.
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📊 Quick Overview
Analysis Period: December 19-22, 2025 (3 days)
Current Spend: $69.82 for 1,039 runs
Monthly Projection: ~$698/month
Key Finding: Top 3 meta-orchestrator workflows account for 35% of all runs and ~$247/month (35% of projected costs)
Confidence Level: Medium
💰 Cost Reduction Opportunities
Total Potential Savings: $125-180/month (18-26% reduction)
Strategy 1: Optimize Meta-Orchestrator Frequency - Save $80-120/month
The three meta-orchestrator workflows run excessively frequently:
Agent Performance Analyzer - Meta-Orchestrator
Current State:
Issue: Running constantly throughout the day to monitor agent performance. This is over-monitoring.
Recommendation: Reduce to 4x/day (morning, noon, afternoon, evening)
Implementation: Update
.github/workflows/agent-performance-analyzer-meta-orchestrator.md:Workflow Health Manager - Meta-Orchestrator
Current State:
Issue: Checking workflow health every ~30 minutes is excessive.
Recommendation: Reduce to 2x/day (morning and evening health checks)
Implementation: Update schedule in workflow file:
Campaign Manager - Meta-Orchestrator
Current State:
Recommendation: Daily is sufficient for campaign management
Implementation:
Combined Meta-Orchestrator Savings: $80-120/month
Strategy 2: Fix High-Error Workflows - Save $20-30/month
Three workflows have concerning error rates that indicate inefficiency:
Security Fix PR - 44 errors/run
Current State:
Issue: Something is fundamentally broken - nearly every operation generates errors.
Recommendation:
/tmp/portfolio-logs/run-*/agent-stdio.logPotential Savings: $8-11/month (wasted on failed operations)
Go Logger Enhancement - 45.6 errors/run
Current State:
Recommendation: Debug and fix before next run. This workflow is clearly broken.
Dev Hawk - 20.6 errors/run
Current State:
Recommendation:
Strategy 3: Reduce Smoke Test Frequency - Save $25-30/month
Currently running ~7 different smoke tests multiple times per day:
Combined: ~$81/month for smoke testing
Recommendation: Reduce smoke test frequency from ~7/day to 2/day
Strategy 4: Audit Hourly Workflows - Save $10-15/month
Hourly CI Cleaner: 19 runs/day seems excessive
Tidy: 20 runs/day is too frequent
🎯 Priority Actions
CRITICAL - Implement This Week
Reduce Meta-Orchestrator Frequencies → Save $80-120/month
Fix Broken Workflows → Save $15-20/month + improve reliability
Reduce Smoke Test Frequency → Save $58/month
HIGH PRIORITY - Next Week
Optimize Hourly Workflows → Save $70/month
Investigate Missing Tool Errors → Improve efficiency
📈 Portfolio Health Metrics
Overall Status ✅ Excellent
Quality Indicators⚠️ Needs Attention
Cost Efficiency 📊
🔍 Detailed Breakdown
Top Cost Drivers (Projected Monthly)
Workflows Not Run (Last 3 Days)
101 workflows out of 168 total (60%) had no runs in the 3-day period. This is expected for:
Note: Extended monitoring period needed to identify truly unused workflows.
🎬 Implementation Guide
Quick Wins (Can Implement Now)
1. Meta-Orchestrator Schedules - Copy/paste ready changes:
File:
.github/workflows/agent-performance-analyzer-meta-orchestrator.mdFile:
.github/workflows/workflow-health-manager-meta-orchestrator.mdFile:
.github/workflows/campaign-manager-meta-orchestrator.md2. Smoke Tests - Update all smoke test workflows:
Requires Investigation
High-Error Workflows: Review logs before changes
/tmp/portfolio-logs/run-*/agent-stdio.log📅 Next Steps
🎯 Success Metrics
After implementing recommendations:
Methodology: Analysis based on actual workflow execution data from December 19-22, 2025 (3 days, 1,039 runs). Costs calculated from total token usage (261M tokens = $69.82). Monthly projections based on linear extrapolation (3-day sample × 10). Recommendations focus on schedule optimization and error reduction without compromising functionality.
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