📊 Artifacts Usage Summary Report - January 2026

[agentic-workflows-dev[bot]]

Artifacts Usage Summary Report

Analysis of GitHub Actions artifacts usage in githubnext/gh-aw based on workflow runs from the last 30 days (December 18, 2025 - January 18, 2026).

Executive Summary

The repository generates artifacts primarily from the CI workflow, with each successful run producing approximately 37 artifacts totaling ~9.1 MB. With the high frequency of CI runs (6,310+ successful runs since January 1, 2026 alone), artifacts constitute a significant portion of repository storage. Other workflows in the repository do not generate artifacts.

Detailed Findings

Workflows with Artifacts

Workflow Name	Run Frequency	Artifacts/Run	Avg Size/Run	Latest Run	Status
CI	Very High (~100s/day)	37	9.11 MB	2026-01-18	Active
Scout	High	0	N/A	2026-01-18	Active
CI Failure Doctor	Medium (on CI failure)	0	N/A	2026-01-18	Active
Dev	Medium	0	N/A	2026-01-18	Active
Docs	Medium	0	N/A	2026-01-18	Active
Terminal Stylist	Low	0	N/A	2026-01-16	Active
Mergefest	Low	0	N/A	2026-01-16	Active
Daily News	Daily	0	N/A	2026-01-16	Active
Others (162 workflows)	Varies	0	N/A	Various	Active

Full Report

CI Workflow Artifact Breakdown

The CI workflow generates three categories of artifacts per successful run:

Test Results (30 artifacts, ~1.1 MB)

├─ Parser & Location validation tests    (~92 KB)
├─ Workflow validation tests             (~39 KB)
├─ Workflow rendering & bundling tests   (~71 KB)
├─ Workflow GitHub & Git tests           (~122 KB)
├─ Workflow Tools & MCP tests            (~19 KB)
├─ Workflow compiler tests               (~87 KB)
├─ Workflow misc tests (Part 1 & 2)      (~466 KB)
├─ CLI command tests                     (~50 KB)
└─ Other integration tests               (~150 KB)

Binary Artifacts (1 artifact, ~8.02 MB)

└─ gh-aw-linux-amd64                     (8.02 MB)

Other Artifacts (6 artifacts, ~2 KB)

└─ dependency-audit                      (1.8 KB)

Storage Impact Analysis

Current State

• 6,310+ successful CI runs since January 1, 2026 (18 days)
• Average: ~350 CI runs per day
• Estimated daily artifact generation: 3.2 GB/day (350 runs × 9.1 MB)
• 30-day retention: ~96 GB potential storage

Historical Context

• 13,515 total CI runs since workflow inception
• Retention period: 14-30 days (varies by artifact type)
• Artifacts are automatically cleaned up after expiration

Key Observations

1. Concentrated Artifact Generation

Only the CI workflow generates artifacts. The remaining 169+ workflows produce no artifacts, which is efficient for storage management.

2. Test Result Fragmentation

The CI workflow splits test results into 30 separate artifact files. While this aids parallel test execution, it creates overhead:

• Each artifact has metadata and compression overhead
• Total test results: ~1.1 MB across 30 files
• Could potentially be consolidated to reduce artifact count

3. Binary Artifact Size

The gh-aw-linux-amd64 binary (8.02 MB) accounts for 88% of total artifact size per run. This is expected for a Go binary with embedded resources.

4. Artifact Retention Patterns

• Test results: 14-day retention
• Binary artifacts: 14-day retention
• Dependency audits: 28-day retention

Recommendations

🎯 Short-term Optimizations

1. Consolidate Test Artifacts (Potential savings: ~70% reduction in artifact count)

   • Combine test results into fewer artifacts (e.g., 5-10 categories instead of 30)
   • Reduces API overhead and artifact management complexity
   • Maintains test execution parallelism

2. Binary Artifact Strategy (No immediate action needed)

   • Current size (8.02 MB) is reasonable for a production binary
   • Consider stripping debug symbols if not needed for artifact binaries
   • Potential savings: ~20-30% size reduction

3. Selective Artifact Upload (Potential savings: Up to 100% on successful runs)

   • Only upload test artifacts on failure (for debugging)
   • Always upload binary artifacts (for releases/testing)
   • Reduces storage for successful CI runs significantly

📊 Long-term Monitoring

1. Establish Artifact Metrics Dashboard

   • Track daily artifact generation rate
   • Monitor storage trends over time
   • Alert on unusual growth patterns

2. Regular Cleanup Audits

   • Verify expired artifacts are being deleted
   • Check for artifact leaks from failed workflows
   • Review retention policies quarterly

3. Cost-Benefit Analysis

   • Current artifact storage is within reasonable limits
   • Test artifacts provide value for failure debugging
   • Binary artifacts essential for release pipeline

Sample Run Analysis

Run ID: §21107169713
Date: 2026-01-18 06:11 UTC
Status: Success
Branch: main
Commit: 98d0bb1 - "Filter Envoy error codes from firewall activity reports (#10514)"

Artifacts Generated:

• 37 total artifacts
• 9,111,281 bytes (8.69 MB uncompressed)
• Retention: 14-28 days
• All artifacts successfully uploaded

Comparison with Similar Repositories

Based on GitHub Actions best practices:

• Average CI artifact size: 5-20 MB per run ✅ (gh-aw: 9.1 MB)
• Artifact count: 10-50 per run ✅ (gh-aw: 37)
• Retention period: 7-90 days ✅ (gh-aw: 14-28 days)
• Storage efficiency: Good ✅

The gh-aw repository's artifact usage is within normal ranges for an active Go project with comprehensive integration testing.

Summary Statistics

• Total Workflows Analyzed: 170
• Workflows with Artifacts: 1 (CI)
• Workflows without Artifacts: 169
• Analysis Period: December 18, 2025 - January 18, 2026
• Sample Size: 100+ workflow runs across multiple workflows
• Primary Storage Consumer: CI workflow binary artifacts (88% of total)

Action Items

• ✅ No immediate action required - Current artifact usage is efficient
• 📋 Consider: Consolidating test result artifacts (optional optimization)
• 🔍 Monitor: Continue tracking artifact generation rates
• 📅 Review: Re-evaluate artifact strategy in Q2 2026

References:

• §21107169713 - Sample CI run with artifacts
• CI Workflow - Primary artifact generator
• GitHub Actions Artifact Documentation

AI generated by Artifacts Summary