📊 Agentic Workflow Lock File Statistics - February 1, 2026

[github-actions[bot]]

Comprehensive analysis of 148 agentic workflow lock files in the .github/workflows/ directory, revealing patterns in trigger usage, safe output configurations, structural characteristics, and automation preferences across the gh-aw repository.

Executive Summary

• Total Lock Files: 148 workflows (+9 from Jan 26)
• Total Size: 9.16 MB (9,601,501 bytes)
• Average File Size: 63 KB
• Size Range: 23 KB - 105 KB
• Analysis Date: February 1, 2026
• Growth: +6.5% workflows in 6 days

Key Findings

• 100% Concurrency Controls: All 148 workflows implement concurrency management (maintained)
• 88% Manual Triggers: 130 workflows support workflow_dispatch for on-demand execution
• 70% Scheduled: 104 workflows run automatically on cron schedules
• Dominant Pattern: 94 workflows (64%) use schedule + workflow_dispatch combination
• Safe Output Adoption: 82% of workflows use at least one safe output type

File Size Distribution

Size Distribution by Range

Size Range	Count	Percentage	Description
< 10 KB	0	0.0%	No minimal workflows
10-50 KB	10	6.8%	Lightweight testing workflows
50-100 KB	136	91.9%	Standard workflows (dominant)
> 100 KB	2	1.4%	Complex multi-tool workflows

Key Statistics:

• Smallest: codex-github-remote-mcp-test.lock.yml (23 KB)
• Largest: smoke-claude.lock.yml (105 KB)
• Median: ~63 KB
• Consistency: 92% of workflows fall within 50-100 KB range

View Size Analysis

The tight clustering around 50-100 KB indicates:

• Standardized workflow templates across the repository
• Consistent complexity levels for agentic workflows
• Well-defined patterns and best practices
• Efficient workflow design without bloat

Change from January 26: Average file size decreased from 75 KB to 63 KB, suggesting optimization efforts or addition of more lightweight workflows. The 50-100 KB range increased from 86% to 92%, showing even stronger standardization.

Trigger Analysis

Trigger Type Distribution

Trigger Type	Count	Percentage	Usage Pattern
workflow_dispatch	130	42.3%	Manual on-demand execution
schedule	104	33.9%	Automated cron-based runs
pull_request	14	4.6%	PR event handling
issue_comment	14	4.6%	Comment-triggered actions
issues	13	4.2%	Issue event responses
pull_request_review_comment	6	2.0%	PR review responses
discussion_comment	5	1.6%	Discussion interactions
discussion	4	1.3%	Discussion events
workflow_run	2	0.7%	Workflow chaining
push	1	0.3%	Push-triggered

Total Trigger Declarations: 307 across 148 workflows (average 2.07 triggers per workflow)

Common Trigger Combinations

Combination	Count	Percentage	Pattern
schedule + workflow_dispatch	94	63.5%	Dominant pattern
workflow_dispatch (only)	21	14.2%	Manual-only
pull_request + schedule + workflow_dispatch	7	4.7%	Multi-trigger
issues (only)	4	2.7%	Pure event-driven

Schedule Patterns

104 workflows run on schedules (70% of all workflows)

Peak Hours for Scheduled Workflows:

Hour (UTC)	Count	Description
14:00	13	Peak afternoon slot
09:00	10	Morning execution
11:00	8	Mid-morning
13:00	5	Early afternoon
15:00	5	Mid-afternoon

Schedule Characteristics:

• Weekday-focused: 23 workflows (22%) run only on weekdays (Mon-Fri)
• Time scattering: Workflows deliberately spread across different hours to avoid load spikes
• Daily automation: Majority of workflows run daily at various scattered times (e.g., 48, 53, 59 minutes)
• Load distribution: Non-zero minutes distribute GitHub Actions load

Safe Outputs Analysis

Safe Output Types Distribution

Type	Count	Percentage	Workflows Using
create-discussion	59	36.4%	39.9% of workflows
create-issue	37	22.8%	25.0% of workflows
add-comment	33	20.4%	22.3% of workflows
create-pull-request	27	16.7%	18.2% of workflows
update-issue	4	2.5%	2.7% of workflows
update-project	2	1.2%	1.4% of workflows

Total Safe Output Declarations: 162 across 148 workflows

Note: Safe output adoption is 82% (121/148 workflows), indicating widespread use of structured output mechanisms.

Discussion Categories

When workflows create discussions, they target these categories:

Category	Count	Usage Pattern
audits	21	Security and workflow audit reports
General	14	General discussions and findings
Audits	6	Alternative capitalization
reports	3	Various agent analysis reports
research	2	Research findings

Category Standardization Note: Multiple capitalizations of "audits" (audits, Audits, audit) suggest opportunity for standardization.

Structural Characteristics

Workflow Complexity

• Average File Size: 63 KB
• Concurrency Adoption: 148/148 (100%)
• Average Triggers: 2.07 per workflow
• Average Jobs: 4.7 per workflow
• Workflow Architecture: Multi-job design pattern

Timeout Patterns

Common Timeout Values:

Timeout (minutes)	Count	Usage Pattern
10	182	Standard short jobs
15	167	Default agent timeout
20	162	Extended agent processing
30	28	Long-running analysis
45	11	Complex multi-step workflows

Interesting Findings

1. Perfect Concurrency Adoption Maintained: 100% of workflows continue to implement concurrency controls, demonstrating excellent engineering discipline maintained through repository growth from 139 to 148 workflows (+6.5% in 6 days).

2. Safe Output Revolution: Safe output adoption jumped from 46% to 82% between Jan 26 and Feb 1, driven by improved detection methodology and growing standardization around discussion-based reporting.

3. Discussion-First Reporting Emerges: 59 workflows (40%) now use create-discussion, making it the dominant safe output type. This shows a clear preference for long-form, structured reports.

4. File Size Optimization: Average file size decreased from 75 KB (Jan 26) to 63 KB (Feb 1), a 16% reduction, while maintaining 92% in the 50-100 KB standard range.

5. Rapid Growth Rate: +9 workflows in 6 days (1.5 workflows per day) shows very active development. Annualized growth rate would be ~300% if sustained.

6. Time Scattering Mastery Continues: Schedule times remain deliberately scattered (e.g., 48 14, 59 10) to distribute GitHub Actions load—a sophisticated DevOps practice.

7. Category Standardization Opportunity: Discussion categories have inconsistent capitalization (audits/Audits/audit, General/general).

8. Automation-First Philosophy Maintained: 70% scheduled execution rate shows strong commitment to proactive monitoring.

Historical Trends

Comparing with previous analyses:

Metric	Jan 26, 2026	Feb 1, 2026	Change
Total Workflows	139	148	+9 (+6.5%)
Average Size	74.8 KB	63 KB	-11.8 KB (-16%)
Total Size	10.15 MB	9.16 MB	-0.99 MB (-10%)
Concurrency Adoption	100%	100%	Maintained ✅
Schedule Usage	76% (106)	70% (104)	-2 workflows
Workflow Dispatch	91% (126)	88% (130)	+4 workflows
50-100 KB Range	86% (120)	92% (136)	+16 workflows
Safe Output Adoption	46%*	82%	+36 pp

*Note: Jan 26 figure may have underestimated due to detection methodology

Recommendations

For Workflow Authors

1. Continue Concurrency Best Practice: Maintain 100% concurrency control adoption for all new workflows.

2. Standardize Discussion Categories: Choose lowercase singular form:

   • Use audits (not Audits, Audit, audit)
   • Use general (not General)
   • Use reports, research, etc. consistently

3. Time Scattering: Continue using scattered minutes (e.g., 41, 53, 57) to distribute GitHub Actions load. Avoid :00, :15, :30, :45.

4. Hybrid Triggers: Continue using schedule + workflow_dispatch pattern (64% adoption) for most workflows.

5. Safe Output Selection:

   • Use create-discussion for comprehensive analysis reports (now the standard)
   • Reserve create-issue for actionable problems requiring tracking
   • Use add-comment for routine updates on existing items
   • Use create-pull-request for automated code improvements

6. Size Consistency: Target 50-100 KB range for workflows.

For Repository Health

1. Monitor Rapid Growth: With 1.5 workflows/day growth rate, consider workflow consolidation opportunities and resource usage monitoring.

2. Category Standardization Initiative: Create a standard for discussion categories and migrate existing workflows to use consistent naming.

3. Documentation: With 148 workflows, create:

   • Workflow catalog (name, purpose, trigger, outputs)
   • Safe output categories documentation
   • Workflow authoring guide with templates

4. Performance Optimization: Monitor execution times and costs (~104 automated runs/day).

Methodology

• Analysis Tool: Bash scripts with text processing (grep, awk, sed)
• Lock Files Analyzed: 148 files in .github/workflows/*.lock.yml
• Cache Memory: Scripts stored in /tmp/gh-aw/cache-memory/ for reuse
• Improvements: Better safe outputs detection, category extraction, trigger parsing, timeout analysis

Analysis Scripts Created

1. analyze_lockfiles_2026_02_01.sh - Main analysis orchestrator
2. deep_analysis.sh - Deep pattern analysis
3. extract_safe_outputs.sh - Safe outputs extraction
4. compile_final_stats.sh - Final statistics

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Analysis Date: February 1, 2026
Analyst: Lockfile Statistics Analysis Agent
Workflow Run: §21564906030

AI generated by Lockfile Statistics Analysis Agent

• expires on Feb 8, 2026, 3:04 PM UTC

[github-actions[bot]]

This discussion was automatically closed because it expired on 2026-02-08T15:04:10.504Z.

Closed by Workflow