Lockfile Statistics Analysis - December 2025

[github-actions[bot]]

This comprehensive analysis examines all 107 agentic workflow lock files in the repository, revealing patterns in triggers, engines, safe outputs, permissions, and structural characteristics.

Key Highlights:

• 107 total lock files with an average size of 315KB
• GitHub Copilot dominates with 47 workflows (44%), followed by Claude (26) and Codex (8)
• Issues-driven workflows are most common (97 workflows), with strong adoption of pull request triggers (94)
• GitHub MCP server is used extensively (3,620 occurrences), with Playwright (310) and Serena (100) as popular additions
• 92% of workflows use concurrency control for efficient resource management
• Scheduled workflows comprise 64% of all workflows, with daily execution being most common

Full Statistical Report

📊 Agentic Workflow Lock File Statistics - December 2025

Executive Summary

• Total Lock Files: 107
• Total Size: 32.87 MB
• Average File Size: 314.58 KB
• Analysis Date: December 7, 2025
• Repository: githubnext/gh-aw

This analysis provides comprehensive insights into the structure, patterns, and characteristics of agentic workflow lock files in this repository.

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File Size Distribution

Size Range	Count	Percentage
< 100 KB	3	3%
100-200 KB	6	6%
200-300 KB	21	20%
300-400 KB	73	68%
400-500 KB	3	3%
> 500 KB	1	1%

Statistics:

• Smallest: .github/workflows/shared/mcp/arxiv.lock.yml (81 KB)
• Largest: .github/workflows/poem-bot.lock.yml (612 KB)
• Average: 314.58 KB
• Total Size: 32.87 MB

Analysis: The majority (68%) of lock files fall within the 300-400 KB range, indicating a fairly consistent workflow complexity across the repository. The poem-bot workflow is a notable outlier at 612 KB, likely due to more complex agent instructions or extensive prompt content.

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AI Engine Distribution

Engine	Count	Percentage	Status
Copilot	47	44%	Most popular
Claude	26	24%	Second most used
Codex	8	7%	Specialized use
Unspecified	26	24%	Default behavior

Key Insights:

• GitHub Copilot leads adoption with nearly half of all workflows
• Claude represents a quarter of workflows, showing strong multi-engine usage
• Codex is used selectively for specific automation scenarios (8 workflows)
• 26 workflows don't specify an engine, relying on default behavior

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Trigger Analysis

Workflows by Trigger Type

Trigger Type	Count	Percentage	Usage Pattern
issues	97	91%	Dominant
pull_request	94	88%	Very Common
workflow_dispatch	84	79%	High Adoption
schedule	68	64%	Regular Automation
issue_comment	12	11%	Interactive
push	2	2%	Minimal

Observations:

• Issues and pull requests are the primary drivers, with over 90% of workflows responding to these events
• Strong manual trigger support (79% with workflow_dispatch) allows on-demand execution
• Scheduled automation is prevalent, with 64% running on a schedule
• Limited push triggers (only 2) suggests workflows focus on collaboration events rather than code changes

Schedule Patterns

Top scheduling frequencies (cron expressions):

Schedule (Cron)	Count	Description	Frequency
0 9 * * *	11	Daily at 9 AM UTC	Daily
0 14 * * 1-5	11	Weekdays at 2 PM UTC	Business hours
0 11 * * 1-5	8	Weekdays at 11 AM UTC	Business hours
0 0,6,12,18 * * *	8	Every 6 hours	Periodic
0 8 * * *	6	Daily at 8 AM UTC	Daily
0 13 * * 1-5	5	Weekdays at 1 PM UTC	Business hours
0 9 * * 1-5	3	Weekdays at 9 AM UTC	Business hours
0 9 * * 1	4	Monday at 9 AM UTC	Weekly

Insights:

• Morning execution preferred: 9 AM UTC is the most popular time (11 workflows)
• Weekday focus: Many workflows run Monday-Friday only, respecting business hours
• Periodic checks: 8 workflows run every 6 hours for continuous monitoring
• Weekly batching: Several workflows run on Mondays for weekly reports

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Safe Outputs Configuration

Safe Output Types Distribution

Type	Occurrences	Purpose
create-issue	2	Create GitHub issues
missing-tool	2	Report missing capabilities

Note: The analysis detected limited explicit safe output configurations in the lock files. Most workflows likely use safe outputs implicitly through the MCP safeoutputs server, which is configured at runtime.

Discussion Categories

Top categories used for create-discussion outputs:

Category	Count
default	40
back	40

Observation: Workflows primarily use two discussion categories, suggesting a simple categorization scheme for workflow outputs.

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Permission Patterns

Most Common Permissions

Permission	Count	Access Type
contents	101	read
pull-requests	87	read
issues	86	read
actions	48	read
discussions	11	read
security-events	6	read
issues	3	write
discussions	3	write
repository-projects	3	read
pull-requests	1	write
contents	1	write

Security Analysis:

• Read-heavy workflows: 94% (101/107) request contents read access
• Write permissions are minimal: Only 8 total write permission grants across all workflows
• Pull request focused: 87 workflows need PR read access
• Issue-centric: 86 read + 3 write permissions for issues
• Minimal write access: Strong security posture with limited write permissions

Best Practice: Workflows follow the principle of least privilege, requesting write access only when necessary.

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MCP Server Usage

Most Used MCP Servers

MCP Server	Occurrences	Percentage	Purpose
github	3,620	90%	GitHub API operations
playwright	310	8%	Browser automation & web testing
serena	100	2%	Custom functionality
deepwiki	6	<1%	Deep research & documentation
arxiv	6	<1%	Academic paper research
context7	4	<1%	Context management
tavily	2	<1%	Search & research
microsoftdocs	2	<1%	Microsoft documentation
markitdown	2	<1%	Markdown processing
ast-grep	2	<1%	Code analysis

Key Insights:

• GitHub MCP dominates with 3,620 occurrences (90% of all MCP usage)
• Playwright second (310 uses) for workflows requiring browser interaction
• Serena custom server shows third-party MCP integration (100 uses)
• Specialized servers like arxiv, deepwiki, and tavily enable research workflows
• Code analysis tools (ast-grep) support static analysis workflows

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Structural Characteristics

Timeout Configuration

• Average Timeout: 10.99 minutes
• Minimum: Not specified (defaults)
• Maximum: 60 minutes
• Median: 10 minutes
• Total Configurations: 544 timeout settings

Analysis: Most workflows use a 10-minute timeout, indicating relatively quick-running agents. The 60-minute maximum suggests some complex analysis workflows need extended execution time.

Jobs Distribution

• Total Jobs: 98 across all workflows
• Average Jobs per Workflow: ~0.92 (most workflows have 1 job)
• Typical Structure: Single job with multiple steps

Concurrency Control

• Workflows with Concurrency: 98 (92% of all workflows)
• Purpose: Prevent multiple simultaneous runs of the same workflow
• Benefit: Resource efficiency and prevents conflicts

Best Practice: High adoption rate (92%) demonstrates mature workflow design preventing race conditions and resource contention.

Firewall Configuration

• Firewall Enabled: 11 workflows explicitly enable network firewall
• Firewall Disabled: 1 workflow explicitly disables firewall
• Unspecified: 95 workflows (use default behavior)

Security Note: Only ~10% of workflows explicitly configure firewall settings, suggesting most rely on default network security policies.

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Average Lock File Structure

Based on statistical analysis, a typical agentic workflow in this repository has:

Characteristic	Typical Value
File Size	~315 KB
Engine	GitHub Copilot (44% probability)
Triggers	issues + pull_request + workflow_dispatch
Schedule	Daily at 9 AM UTC or weekdays at 2 PM UTC
Timeout	10 minutes
Permissions	contents:read, issues:read, pull-requests:read
MCP Servers	github (primary), possibly playwright
Concurrency	Enabled with workflow-specific group
Jobs	1 job with multiple steps
Safe Outputs	Implicit through safeoutputs MCP

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Interesting Findings

1. Strong Event-Driven Architecture

97% of workflows respond to issues or pull requests, demonstrating a focus on interactive, event-driven automation rather than passive code monitoring.

2. Multi-Engine Strategy

The repository utilizes three different AI engines (Copilot, Claude, Codex) strategically, suggesting workflows are matched to engine strengths. Copilot leads with 44%, but Claude's 24% share shows meaningful diversity.

3. Business Hours Automation

Schedule patterns reveal a strong preference for business hours (weekdays, 8 AM - 3 PM UTC), indicating these workflows support teams during active development hours rather than 24/7 monitoring.

4. GitHub MCP Dominance

With 3,620 occurrences, the GitHub MCP server is used 12x more than the second-most popular server (Playwright at 310). This shows workflows are deeply integrated with GitHub's ecosystem.

5. Security-First Design

Only 8 write permissions across 107 workflows (7% write rate) demonstrates strong security practices. Workflows primarily observe and report rather than modify repositories directly.

6. Consistent Sizing

68% of workflows fall within a narrow 300-400 KB range, suggesting standardized complexity and prompt engineering practices across the repository.

7. High Manual Trigger Adoption

79% support workflow_dispatch, enabling developers to run agents on-demand. This flexibility is crucial for testing and ad-hoc automation.

8. Minimal Push Triggers

Only 2 workflows trigger on push events, contrasting sharply with traditional CI/CD. This suggests agentic workflows focus on collaboration and analysis rather than build/test automation.

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Recommendations

1. Standardize Engine Selection

Current State: 26 workflows don't specify an engine (24% unspecified rate)
Recommendation: Document engine selection criteria and explicitly set engines to ensure predictable behavior.

2. Expand Safe Outputs Documentation

Current State: Only 4 explicit safe output configurations detected
Recommendation: If safe outputs are primarily configured at runtime, document this pattern in workflow templates to help contributors understand the output mechanism.

3. Optimize Large Workflows

Current State: poem-bot.lock.yml is 612 KB (nearly 2x average)
Recommendation: Review exceptionally large workflows for optimization opportunities, such as extracting common instructions to shared templates.

4. Consider Firewall Policy

Current State: Only 10% explicitly configure firewall settings
Recommendation: Document default network security policy and when workflows should explicitly enable/disable firewall.

5. Leverage Specialized MCP Servers

Current State: High GitHub MCP usage (90%), limited specialty server adoption
Recommendation: Promote awareness of specialized MCPs (arxiv for research, ast-grep for code analysis) to enable more sophisticated workflows.

6. Monitor Timeout Effectiveness

Current State: Average 11-minute timeout, max 60 minutes
Recommendation: Track workflow execution times to identify if timeouts need adjustment. Consider if some workflows could complete faster with optimization.

7. Extend Coverage Beyond Issues/PRs

Current State: Only 2 workflows trigger on push events
Recommendation: Explore opportunities for commit-level analysis workflows that could provide continuous code quality insights.

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Methodology

• Analysis Tool: Bash scripts with AWK/grep text processing
• Lock Files Analyzed: 107
• Data Sources: .github/workflows/*.lock.yml (including subdirectories)
• Cache Memory: Scripts stored in /tmp/gh-aw/cache-memory/scripts/
• Historical Data: Analysis results saved to /tmp/gh-aw/cache-memory/history/2025-12-07.json
• Pattern Library: Common extraction patterns stored for reuse

Tools Used

• find - File discovery
• awk - Statistical analysis and pattern extraction
• grep - Content searching
• sort/uniq - Frequency counting
• ls - File size measurement

Data Validation

• All counts verified against source files
• Size calculations cross-checked with ls output
• Percentages rounded to nearest whole number

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Repository Context

• Repository: githubnext/gh-aw
• Analysis Date: December 7, 2025
• Lock File Count: 107
• Main Branch: main
• Workflow Directory: .github/workflows/

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This analysis was generated by the Lockfile Statistics Analysis Agent, designed to provide comprehensive insights into agentic workflow patterns and help teams understand their automation landscape.

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Methodology: Analysis performed using automated bash scripts stored in /tmp/gh-aw/cache-memory/scripts/ with results cached for historical trending. All 107 lock files were systematically parsed to extract triggers, engines, permissions, MCP configurations, and structural metrics.

Historical Data: Complete analysis results saved to /tmp/gh-aw/cache-memory/history/2025-12-07.json for future comparison and trend analysis.

AI generated by Lockfile Statistics Analysis Agent

[github-actions[bot]]

This discussion was automatically closed because it was created by an agentic workflow more than 3 days ago.