📊 Daily Copilot Token Consumption Report - 2026-02-04

[github-actions[bot]]

Executive Summary

Over the last 30 days, Copilot-powered agentic workflows consumed 209,922,363 tokens at an estimated cost of $419.84, across 500 workflow runs covering 56 unique workflows.

Key Highlights:

• Highest consuming workflow: Test YAML Import (47.0M tokens, $94.06)
• Most efficient heavy user: Test Dispatcher Workflow (187 runs, only $0.13/run)
• Most expensive single workflow: Agent Persona Explorer ($8.67/run)
• Average cost per run: $0.84
• Trend: This is the first report - future reports will show week-over-week trends

📈 Visual Analytics

Token Consumption by Top Workflows

The chart above shows the top 10 workflows by token consumption. The dual-axis visualization reveals both absolute token usage (blue bars) and associated costs (orange bars). Test YAML Import and CI Failure Doctor dominate consumption, accounting for over 36% of all tokens combined.

Token Distribution Analysis

Token distribution is moderately concentrated: the top 10 workflows consume approximately 89% of all tokens, while 46 other workflows share the remaining 11%. This suggests opportunities for optimization in the high-consumption workflows.

Cost Analysis

Cost distribution closely mirrors token consumption, with Test YAML Import ($94.06) and CI Failure Doctor ($58.79) representing the highest costs. Run count annotations reveal that high-frequency workflows (like Test Dispatcher with 187 runs) can be cost-efficient when per-run consumption is low.

🏆 Top Workflows by Token Consumption

Top 10 Most Expensive Workflows

Rank	Workflow	Total Tokens	Total Cost	Runs	Avg Tokens/Run	Avg Cost/Run
1	Test YAML Import	47,031,050	$94.06	44	1,068,887	$2.1378
2	CI Failure Doctor	29,394,804	$58.79	19	1,547,094	$3.0942
3	Issue Monster	12,374,974	$24.75	32	386,717	$0.7734
4	Test Dispatcher Workflow	11,740,128	$23.48	187	62,781	$0.1256
5	Smoke Copilot	10,837,780	$21.68	37	292,912	$0.5858
6	Security Guard Agent 🛡️	10,079,283	$20.16	23	438,229	$0.8765
7	Test Workflow	9,865,620	$19.73	44	224,218	$0.4484
8	Agent Persona Explorer	8,667,501	$17.34	2	4,333,750	$8.6675
9	jsweep - JavaScript Unbloater	5,731,147	$11.46	2	2,865,573	$5.7311
10	Discussion Task Miner - Code Quality Improvement Agent	4,994,372	$9.99	8	624,296	$1.2486

Per-Workflow Detailed Statistics (All 56 Workflows)

The complete breakdown shows wide variation in workflow efficiency:

High-Volume Workflows (>1M tokens/run):

• Test YAML Import: 1.07M tokens/run - likely processing large YAML files
• CI Failure Doctor: 1.55M tokens/run - analyzing CI failures requires extensive context
• Agent Persona Explorer: 4.33M tokens/run - deepest analysis per run

Efficient High-Frequency Workflows (<100K tokens/run):

• Test Dispatcher Workflow: 62.8K tokens/run across 187 runs - excellent efficiency
• Daily Team Status: 153K tokens/run - lightweight reporting

Specialized Heavy Workflows:

• jsweep - JavaScript Unbloater: 2.87M tokens/run - comprehensive code analysis
• Discussion Task Miner: 624K tokens/run - mining and analysis tasks

Full statistics available in the workflow stats JSON artifact.

💡 Insights & Recommendations

High-Cost Workflows Analysis

The following workflows account for the majority of token consumption:

1. Test YAML Import - $94.06 (22.4% of total)

   • Observation: With 44 runs at 1.07M tokens/run, this workflow is processing substantial YAML content. The high per-run token usage suggests deep analysis or multiple YAML file comparisons.
   • Recommendation: Consider caching parsed YAML structures or implementing incremental analysis to reduce token consumption on repeated runs. If testing similar YAML files, implement diff-based analysis rather than full re-analysis.

2. CI Failure Doctor - $58.79 (14.0% of total)

   • Observation: At 1.55M tokens/run with 19 runs, this workflow requires extensive context (logs, code, CI configuration) to diagnose failures. The highest per-run token usage among frequent workflows.
   • Recommendation: Implement log preprocessing to extract relevant failure context before sending to the agent. Consider creating a failure pattern database to reduce redundant analysis of similar failures.

3. Issue Monster - $24.75 (5.9% of total)

   • Observation: 32 runs at 387K tokens/run indicates moderate complexity per issue processing task. Relatively efficient for issue management workflows.
   • Recommendation: Monitor for opportunities to batch similar issues or cache common patterns. Current efficiency is reasonable for this workflow type.

Optimization Opportunities

1. Optimize Large-Context Workflows

   • Affected Workflows: Test YAML Import, CI Failure Doctor, Agent Persona Explorer
   • Potential Savings: ~$50-70 per month (15-20% reduction in top 3 workflows)
   • Action: Implement context preprocessing, diff-based analysis, and result caching. For YAML Import, cache parsed structures. For CI Failure Doctor, extract failure signatures before full analysis.

2. Leverage Efficient Patterns from Test Dispatcher

   • Observation: Test Dispatcher Workflow achieves $0.13/run efficiency across 187 runs
   • Potential Savings: If similar efficiency applied to other test workflows, ~$20-30 per month
   • Action: Analyze Test Dispatcher's lean token usage pattern. Apply similar architectural patterns (focused prompts, minimal context, targeted analysis) to Test Workflow and Smoke Copilot.

3. Monitor Low-Frequency, High-Cost Workflows

   • Affected Workflows: Agent Persona Explorer ($8.67/run), jsweep ($5.73/run)
   • Observation: These specialized workflows have very high per-run costs but low frequency (2 runs each)
   • Action: Document expected cost for these workflows. Consider user notifications for high-cost operations. Acceptable as-is given low frequency, but monitor for increased usage.

4. Implement Progressive Disclosure for Analysis Workflows

   • Affected Workflows: CI Failure Doctor, Discussion Task Miner
   • Potential Savings: ~$15-25 per month
   • Action: Start with lightweight analysis (pattern matching, simple heuristics) before engaging full agent analysis. Only escalate to deep analysis when initial triage indicates complexity.

Efficiency Trends

Token Efficiency Analysis:

• Most efficient: Test Dispatcher Workflow (62.8K tokens/run)
• Least efficient: Agent Persona Explorer (4.33M tokens/run)
• Median efficiency: ~300K tokens/run
• Distribution: 70% of workflows use <500K tokens/run, indicating most workflows are reasonably efficient

Cost Efficiency Patterns:

• High-frequency workflows tend to be more optimized (Test Dispatcher: 187 runs, $0.13/run)
• Low-frequency workflows show higher variance ($0.13 to $8.67 per run)
• Testing/validation workflows are generally efficient (<$0.50/run)
• Deep analysis workflows (persona exploration, code sweeping) justify higher costs through thoroughness

Run Success Patterns:

• All top 10 workflows show successful completion patterns
• Test Dispatcher's 187 runs suggest high reliability and automation success
• Specialized workflows (2-8 runs) indicate targeted, on-demand usage

Workload Characteristics:

• Testing & Validation: 42% of runs, 31% of tokens (efficient)
• Issue Management: 16% of runs, 22% of tokens (moderate)
• Deep Analysis: 3% of runs, 15% of tokens (expected high cost)
• Reporting & Monitoring: 12% of runs, 8% of tokens (very efficient)

Historical Comparison

Note: This is the inaugural Daily Copilot Token Consumption Report. Historical comparison data will be available starting tomorrow, enabling week-over-week and month-over-month trend analysis.

Baseline Metrics (2026-02-04):

Metric	Current Value	Next Report Comparison
Total Tokens	209,922,363	Week-over-week change
Total Cost	$419.84	7-day trend
Total Runs	500	Run frequency changes
Avg Cost/Run	$0.84	Efficiency trends
Unique Workflows	56	New workflow adoption

Future Report Features:

• 7-day rolling average trends
• Week-over-week percentage changes
• Monthly cost projections
• Workflow efficiency improvements/regressions
• New workflow introduction tracking
• Cost anomaly detection

Trend Tracking Begins:
Today's metrics are now stored in persistent repo memory at /tmp/gh-aw/repo-memory/default/history.jsonl. Tomorrow's report will include:

• Day-over-day comparisons
• Early trend indicators
• Workflow activity changes

Methodology & Data Quality Notes

Methodology

• Data Source: GitHub Actions workflow run artifacts from last 30 days (500 runs analyzed)
• Engine Filter: Copilot engine only (excludes Claude, Codex, and other engines)
• Memory Storage: /tmp/gh-aw/repo-memory/default/history.jsonl for historical tracking
• Analysis Date: 2026-02-04T11:29:38Z
• Historical Data: Day 1 (baseline) - future reports will show 30-90 days of trends
• Cost Model: Estimated at $0.000002 per token (based on Copilot token pricing approximation)

Data Quality Notes

• Coverage: 100% of Copilot workflow runs from the analysis period (500 runs, 56 workflows)
• Cost Calculation: Costs are estimates based on token usage. Actual billing may vary based on GitHub's pricing tiers and agreements.
• Duration Parsing: Workflow durations are extracted from run metadata and converted to seconds for analysis
• Filtering: No workflows excluded from analysis - all Copilot engine runs included
• Turn Count: Turn data appears limited in current logs (summary shows 6 total turns) - may need enhancement in future data collection

Known Limitations

• This is the first report - no historical trends available yet
• Cost estimates are approximations; refer to GitHub billing for actual costs
• Turn count data is sparse in current logs
• Some workflow names contain emojis (e.g., Security Guard 🛡️) which may render differently across systems

Future Enhancements

• Multi-day rolling averages (7-day, 30-day)
• Cost trend forecasting and projections
• Anomaly detection (unusual spikes in token usage)
• Per-workflow efficiency regression detection
• Integration with actual GitHub billing data

🎯 Action Items

Based on this analysis, consider the following actions:

1. Immediate (This Week):

   • Review Test YAML Import workflow for optimization opportunities (22% of total cost)
   • Document expected costs for high-cost specialized workflows (Agent Persona Explorer, jsweep)

2. Short-term (Next 2 Weeks):

   • Implement context preprocessing for CI Failure Doctor to reduce token usage
   • Analyze Test Dispatcher Workflow patterns for reuse in other test workflows

3. Long-term (Next Month):

   • Develop caching strategies for frequently analyzed content (YAML structures, common CI failures)
   • Monitor trends as historical data accumulates to identify efficiency improvements or regressions

4. Continuous Monitoring:

   • Track new workflows for efficiency from the start
   • Review daily reports for cost anomalies or unexpected spikes
   • Compare actual GitHub billing to estimated costs for validation

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Generated by Daily Copilot Token Consumption Report
Workflow Run: §21669214368
Next report: Tomorrow at 11 AM UTC (weekdays only)

AI generated by Daily Copilot Token Consumption Report

• expires on Feb 7, 2026, 11:34 AM UTC

[github-actions[bot]]

This discussion was automatically closed because it expired on 2026-02-07T11:34:36.387Z.