📊 Agentic Workflow Lock File Statistics - February 2026

[github-actions[bot]]

Executive Summary

Comprehensive statistical analysis of 145 agentic workflow lock files in the github/gh-aw repository, revealing usage patterns, popular triggers, structural characteristics, and configuration trends.

Key Metrics:

• Total Lock Files: 145
• Total Size: 8.89 MB (9,105,394 bytes)
• Average File Size: 61 KB (62,795 bytes)
• Analysis Date: 2026-02-06

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File Size Distribution

Size Range	Count	Percentage
< 30 KB	6	4.1%
30-50 KB	7	4.8%
50-70 KB	100	69.0%
70-90 KB	23	15.9%
> 90 KB	9	6.2%

Size Extremes:

• Smallest: codex-github-remote-mcp-test.lock.yml (22 KB)
• Largest: smoke-claude.lock.yml (106 KB)
• Median: 58 KB

Key Insight: The vast majority (69%) of lock files fall into the 50-70 KB range, indicating consistent workflow complexity across the repository.

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Trigger Analysis

Most Popular Triggers

Trigger Type	Count	Percentage	Example Workflows
workflow_dispatch	128	88.3%	agent-performance-analyzer, cli-version-checker, daily-code-metrics
schedule	104	71.7%	agent-performance-analyzer, artifacts-summary, daily-workflow-updater
issue_comment	14	9.7%	archie, ai-moderator
pull_request	13	9.0%	changeset, cloclo, security-compliance
issues	13	9.0%	auto-triage-issues, issue-reporter
pull_request_review_comment	6	4.1%	pr-reviewer workflows
discussion_comment	5	3.4%	community engagement workflows
discussion	4	2.8%	discussion-triggered workflows
workflow_run	2	1.4%	dependent workflows
push	1	0.7%	CI/CD workflows

Common Trigger Combinations

1. Schedule + Manual (schedule, workflow_dispatch): 95 workflows (65.5%)

   • Most common pattern for periodic tasks with on-demand capability
   • Examples: daily metrics, version checkers, status reports

2. Manual Only (workflow_dispatch): 19 workflows (13.1%)

   • Pure on-demand workflows
   • Typically used for ad-hoc analysis or testing

3. Multi-Source (pull_request, schedule, workflow_dispatch): 6 workflows (4.1%)

   • Flexible workflows that run on multiple triggers
   • Examples: comprehensive testing and analysis workflows

4. Interactive Multi-Trigger (all event types): 3 workflows (2.1%)

   • Respond to discussions, issues, PRs, and comments
   • Community engagement and support automation

5. Pure Event-Driven (issues, issue_comment, etc.): Various counts

   • React to specific GitHub events
   • Used for triage, moderation, and automated responses

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Schedule Patterns

View Detailed Schedule Distribution

Schedule (Cron)	Count	Description
0 13 * * 1-5	4	Daily at 1:00 PM UTC (weekdays)
0 14 * * 1-5	4	Daily at 2:00 PM UTC (weekdays)
0 11 * * 1-5	4	Daily at 11:00 AM UTC (weekdays)
0 10 * * 1-5	2	Daily at 10:00 AM UTC (weekdays)
0 9 * * 1-5	2	Daily at 9:00 AM UTC (weekdays)
0 15 * * 1-5	2	Daily at 3:00 PM UTC (weekdays)
0 16 * * 1-5	2	Daily at 4:00 PM UTC (weekdays)
0 7 * * 1-5	2	Daily at 7:00 AM UTC (weekdays)
5 12 * * *	1	Daily at 12:05 PM UTC (all days)
31 */12 * * *	1	Every 12 hours at :31 minutes
Various others	80+	Scattered throughout the day

Pattern Insight: Schedules are deliberately staggered throughout business hours (7 AM - 4 PM UTC) to distribute load and avoid resource contention. Most workflows run on weekdays only.

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Safe Outputs Analysis

Safe outputs enable workflows to create GitHub resources (discussions, issues, comments) in a controlled manner.

Safe Output Type	Usage Count	Description
noop	1,243	No-operation transparency logging
missing_tool	1,108	Report missing tool/capability
missing_data	414	Report missing data/information
add-comment	90	Add comments to issues/PRs

Key Findings:

1. Transparency First: The noop safe output is the most common (1,243 uses), indicating workflows frequently log completion status even when no changes are needed. This ensures visibility into workflow execution.

2. Limitations Reporting: missing_tool (1,108) and missing_data (414) are heavily used to report capability gaps and data unavailability, providing valuable feedback about workflow constraints.

3. Controlled Interactions: Only 90 uses of add-comment shows disciplined use of GitHub API mutations, preventing spam and maintaining clean issue/PR threads.

4. Discussion Category: When creating discussions, workflows primarily target the "audits" category for reports and analysis results.

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Structural Characteristics

Job Complexity

• Average Jobs per Workflow: 6.0
• Maximum Jobs: 9 (in firewall-escape.lock.yml)
• Minimum Jobs: 2 (typical for simple workflows)

Typical Job Structure:

1. Activation Job: Checks workflow file timestamps and prerequisites
2. Agent Job: Main agentic execution with Claude/Copilot
3. Collect Output Jobs: Gather and process safe outputs
4. Action Jobs: Execute downstream actions (create discussions, issues, etc.)
5. Notification Jobs: Send updates or summaries

Step Complexity

• Average Steps per Workflow: 71.6
• Maximum Steps: 100 (in daily-copilot-token-report.lock.yml)
• Minimum Steps: ~10 (in simple test workflows)

Step Distribution Pattern:

• Setup steps: 5-10 (checkout, setup tools, configure environment)
• Agent execution: 20-40 (main agentic work)
• Output processing: 10-20 (collect, validate, format results)
• Action execution: 5-20 (create GitHub resources)
• Cleanup/notification: 5-10 (finalization steps)

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Permission Patterns

Most Common Permissions

Permission	Read Count	Write Count	Total
contents	652	74	726
issues	131	314	445
pull-requests	129	240	369
discussions	N/A	270	270

Permission Distribution Insights:

1. Read-Heavy Contents Access: 652 read vs. 74 write for contents permission indicates workflows primarily analyze code rather than modify it. Write access is reserved for specific workflows that need to commit changes.

2. Issue Management: 314 write permissions for issues (vs. 131 read) shows active issue creation and management, likely for reporting, triage, and automation.

3. Pull Request Engagement: 240 write permissions for pull-requests indicates workflows actively comment on, review, or create PRs.

4. Discussion Creation: 270 write permissions for discussions aligns with the repository's emphasis on using discussions for audit reports and analysis results.

5. Minimal Permissions: All workflows follow the principle of least privilege, requesting only necessary permissions for their specific job steps.

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MCP Server Usage

MCP (Model Context Protocol) servers provide specialized capabilities to agentic workflows.

MCP Server	Usage Count	Description
github	35	GitHub API integration (repos, issues, PRs, commits)
playwright	5	Browser automation and web scraping
arxiv	1	Academic paper search and retrieval
deepwiki	1	Deep wiki content exploration

Findings:

1. GitHub-Centric: The github MCP server dominates with 35 uses, reflecting workflows' primary focus on repository analysis, code review, and GitHub resource management.

2. Web Automation: Playwright MCP server (5 uses) enables workflows to interact with web UIs, test web applications, or gather data from web sources.

3. Specialized Research: Arxiv and deepwiki MCP servers show experimental use of specialized knowledge sources, potentially for research-oriented workflows.

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Interesting Findings

1. High Manual Trigger Adoption (88.3%)

   • Nearly all workflows support workflow_dispatch, enabling on-demand execution
   • This provides flexibility for testing, debugging, and ad-hoc analysis
   • Reflects a design philosophy of "automate, but keep manual control"

2. Scheduled Workflow Dominance (71.7%)

   • Over 70% of workflows run on schedules, indicating strong automation culture
   • Schedules are deliberately staggered to prevent resource contention
   • Most run on weekdays only, respecting business hour patterns

3. Consistent File Size (69% in 50-70 KB range)

   • Remarkable consistency in workflow complexity across the repository
   • Suggests standardized workflow patterns and templates
   • Average 61 KB file size indicates substantial but not excessive configuration

4. High Step Count (avg 71.6 steps)

   • Complex multi-stage workflows with detailed orchestration
   • Reflects comprehensive approach: validate → execute → collect → act
   • Step counts correlate with thorough error handling and logging

5. Safe Output Discipline

   • Heavy use of noop (1,243) demonstrates commitment to transparency
   • High missing_tool/missing_data counts (1,522 combined) show workflows gracefully handle limitations
   • Low add-comment count (90) prevents notification spam

6. Multi-Job Architecture (avg 6 jobs)

   • Complex orchestration with clear separation of concerns
   • Activation → Agent → Collection → Action pattern is standard
   • Enables parallel execution and better resource management

7. Weekday-Only Schedules

   • Strong preference for Monday-Friday execution (1-5 in cron)
   • Respects team availability and business hours
   • Reduces weekend noise and notification fatigue

8. Minimal Write Permissions

   • Read permissions vastly outnumber write (especially for contents: 652 vs 74)
   • Reflects security-conscious design and least-privilege principle
   • Write access carefully controlled and audited

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Statistical Profile: The "Typical" Agentic Workflow

Based on median and average values, a typical .lock.yml file in this repository has:

• Size: ~61 KB
• Jobs: 6 jobs
  1. Activation (prerequisite checks)
  2. Agent (main execution)
  3. Collect outputs
  4. Create discussion/issue
  5. Send notifications
  6. Cleanup
• Steps: ~72 steps total across all jobs
• Triggers: schedule + workflow_dispatch (65.5% use this combo)
• Schedule: Weekday business hours (7 AM - 4 PM UTC)
• Permissions:
  • contents: read
  • issues: write
  • pull-requests: read
  • discussions: write
• Safe Outputs: Uses noop for transparency, occasional add-comment
• MCP Servers: GitHub MCP for repository interactions
• Timeout: Configured (144 out of 145 workflows)

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Recommendations

Based on the analysis, here are actionable recommendations:

1. Template Standardization

   • The 69% of files in the 50-70 KB range suggests natural convergence on optimal patterns
   • Consider formalizing these patterns into official templates
   • Document the standard job structure (Activation → Agent → Collection → Action)

2. Schedule Distribution

   • Current staggered scheduling (7 AM - 4 PM UTC) effectively prevents resource contention
   • Continue this pattern as more workflows are added
   • Consider creating a schedule coordination tool to avoid clustering

3. Safe Output Expansion

   • Current safe outputs are well-utilized but limited to 4 types
   • Consider adding create-pull-request and create-issue safe outputs if not already available
   • The low add-comment count suggests conservative use - maintain this discipline

4. MCP Server Adoption

   • GitHub MCP dominates (35 uses), but other servers are underutilized
   • Explore opportunities for playwright (web testing), arxiv (research), and other specialized MCPs
   • Document MCP server best practices and use cases

5. Permission Optimization

   • Current read-heavy pattern (652 read vs 74 write for contents) is ideal
   • Continue principle of least privilege
   • Regularly audit write permissions to ensure necessity

6. Documentation

   • High step counts (avg 71.6) and job counts (avg 6) suggest complex workflows
   • Ensure comprehensive documentation for workflow maintenance
   • Consider adding inline comments or companion .md files explaining workflow logic

7. Monitoring

   • Track file size growth over time (baseline: 61 KB average)
   • Monitor step count trends - increasing counts may indicate complexity debt
   • Alert on workflows exceeding 100 steps or 9 jobs

8. Historical Analysis

   • Establish regular analysis cadence (monthly or quarterly)
   • Track trends: file count growth, size changes, new patterns
   • Use cache memory to maintain historical data for comparison

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Methodology

Analysis Tools: Bash scripts and Python 3 with regex-based YAML parsing (no external dependencies)

Lock Files Analyzed: 145

Cache Memory: Used /tmp/gh-aw/cache-memory/ for script persistence and historical data tracking

Data Sources: All .lock.yml files in .github/workflows/ directory

Analysis Scripts:

• /tmp/gh-aw/cache-memory/scripts/analyze_lockfiles.sh - Bash-based extraction
• /tmp/gh-aw/cache-memory/scripts/comprehensive_analysis_v2.py - Python statistical aggregation

Verification: Cross-referenced multiple data extraction methods to ensure accuracy

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References:

• Workflow Run §21743839890

AI generated by Lockfile Statistics Analysis Agent

• expires on Feb 13, 2026, 8:32 AM UTC

[github-actions[bot]]

This discussion was automatically closed because it expired on 2026-02-13T08:32:30.896Z.

Closed by Workflow