Agentic Workflow Lock File Statistics - December 4, 2024

[github-actions[bot]]

This comprehensive analysis examines all 104 .lock.yml files in the repository, revealing insights into workflow structure, trigger patterns, safe outputs, permissions, and configuration preferences across the gh-aw agentic workflow ecosystem.

Key Highlights:

• 104 lock files totaling 31 MB with an average size of 306 KB
• Copilot engine dominates with 42 workflows (40%), followed by Claude (26) and Codex (8)
• Most workflows are scheduled or manual - 81 use workflow_dispatch, 65 use schedule
• Discussion creation is the most popular safe output (39 workflows), followed by comments (18) and issues (17)
• Security-conscious permissions - 88 workflows use read-only permissions, only 7 require write access
• 37 workflows (36%) use strict mode for enhanced security

Full Statistical Report

Executive Summary

• Total Lock Files: 104
• Total Size: 31 MB
• Average File Size: 306 KB
• Analysis Date: 2024-12-04
• Repository: githubnext/gh-aw

File Size Distribution

Size Range	Count	Percentage
50-100 KB	3	2.9%
100-200 KB	6	5.8%
200-300 KB	27	26.0%
300-400 KB	64	61.5%
> 400 KB	4	3.8%

Size Statistics:

• Smallest: arxiv.lock.yml (81 KB) - MCP server configuration
• Largest: poem-bot.lock.yml (605 KB) - Most complex workflow with 115 steps
• Average: 306 KB
• Total: 31 MB

The majority of lock files (61.5%) fall in the 300-400 KB range, indicating a consistent level of complexity across workflows.

Engine Distribution

Engine	Count	Percentage	Use Cases
Copilot	42	40.4%	Dominant engine, used across various workflow types
Claude	26	25.0%	Second most popular, often for complex analysis
Codex	8	7.7%	Used for specialized code generation tasks
Not specified	28	26.9%	Engine inherited or not explicitly set

Key Finding: Copilot is the most widely adopted engine, suggesting strong integration with GitHub's AI capabilities.

Trigger Analysis

Most Popular Triggers

Trigger Type	Count	Percentage	Description
workflow_dispatch	81	77.9%	Manual trigger - most workflows can be run on demand
schedule	65	62.5%	Automated scheduling - primarily for recurring tasks
pull_request	9	8.7%	PR-triggered workflows for code review and analysis
issues	4	3.8%	Issue-triggered workflows for triage and management
workflow_run	2	1.9%	Chained workflows triggered by other workflow completion
push	2	1.9%	Push-triggered workflows for continuous integration

Common Trigger Combinations

1. schedule + workflow_dispatch (58 workflows, 55.8%) - Most common pattern: automated daily/weekly runs with manual override capability
2. workflow_dispatch only (14 workflows, 13.5%) - Purely manual workflows for on-demand analysis
3. pull_request + schedule + workflow_dispatch (6 workflows, 5.8%) - Comprehensive coverage: automated, PR-based, and manual triggers
4. pull_request + workflow_dispatch (2 workflows) - PR review workflows with manual trigger option

Insight: The dominance of workflow_dispatch indicates workflows are designed for flexibility, allowing both automated and manual execution.

Schedule Patterns

Top 10 most common cron schedules:

Schedule (Cron)	Count	Description
0 9 * * *	4	Daily at 9 AM UTC
0 0,6,12,18 * * *	4	Every 6 hours (high-frequency monitoring)
0 8 * * *	3	Daily at 8 AM UTC
0 10 * * 1-5	3	Weekdays at 10 AM UTC (business hours)
17 3 * * *	2	Daily at 3:17 AM UTC
0 9 * * 1-5	2	Weekdays at 9 AM UTC
0 9 * * 1	2	Monday at 9 AM (weekly reports)
0 6 * * 0	2	Sunday at 6 AM UTC (weekly maintenance)
0 15 * * 1	2	Monday at 3 PM UTC (weekly summary)

Pattern: Most scheduled workflows run daily in the morning (UTC timezone), with some high-frequency monitoring every 6 hours and weekly reports on Mondays.

Safe Outputs Analysis

Safe Output Types Distribution

Safe Output Type	Workflows	Percentage	Primary Use Case
create-discussion	39	44.3%	Publishing analysis reports and findings
add-comment	18	20.5%	Adding feedback to issues/PRs
create-issue	17	19.3%	Creating actionable items from findings
create-pull-request	14	15.9%	Automated code improvements and documentation updates
update-issue	3	small	Updating existing issues

Total workflows with safe outputs: 88 (84.6% of all workflows)

Example Workflows by Safe Output Type

create-discussion (reporting and analysis):

• artifacts-summary.lock.yml
• audit-workflows.lock.yml
• blog-auditor.lock.yml
• commit-changes-analyzer.lock.yml
• copilot-agent-analysis.lock.yml

create-issue (actionable findings):

• breaking-change-checker.lock.yml
• ci-doctor.lock.yml
• cli-consistency-checker.lock.yml
• cli-version-checker.lock.yml
• craft.lock.yml

add-comment (feedback and suggestions):

• archie.lock.yml
• brave.lock.yml
• ci-doctor.lock.yml
• cloclo.lock.yml
• craft.lock.yml

create-pull-request (automated improvements):

• cloclo.lock.yml
• daily-doc-updater.lock.yml
• developer-docs-consolidator.lock.yml
• dictation-prompt.lock.yml
• github-mcp-tools-report.lock.yml

Safe Output Limits Configuration

Output Type	Max Limit	Count	Use Case
discussion	max=1	32	Single consolidated report per run
comment	max=1	11	Single comment per run
issue	max=1	4	Single issue per run
comment	max=3	3	Multiple related comments
issue	max=10	2	Batch issue creation
issue	max=5-6	2	Medium batch issue creation

Pattern: Most workflows use max=1 to create a single, consolidated output rather than multiple separate items. This prevents spam and keeps repository clean.

Discussion Categories

Category	Count	Purpose
audits / Audits	18	Security and compliance audits
General / general	9	General discussions and reports
reports	2	Structured reporting
dev	2	Development-related discussions
artifacts	2	Artifact analysis
security	1	Security-specific discussions
research	1	Research findings
daily-news	1	Daily updates
announcements	1	Announcements

Observation: "audits" is the most popular category (46% of discussion-creating workflows), indicating strong focus on monitoring and analysis.

Close-Older Pattern

• 35 workflows (89.7% of discussion-creating workflows) use close-older-discussions: true
• 0 workflows use close-older-issues: true

This pattern keeps the discussion list clean by automatically closing superseded reports, ensuring only the latest analysis is visible.

Structural Characteristics

Job and Step Complexity

• Average Steps per Workflow: 62.2
• Maximum Steps: 115 (poem-bot.lock.yml)
• Minimum Steps: 28 (dev.lock.yml)

Distribution:

• Small workflows (<40 steps): ~10%
• Medium workflows (40-70 steps): ~70%
• Large workflows (70+ steps): ~20%

Typical Lock File Structure:
A standard .lock.yml file in this repository has:

• Size: ~306 KB
• Steps: ~62 steps across multiple jobs
• Permissions: Read-only contents, issues, pull-requests
• Triggers: schedule + workflow_dispatch
• Timeout: 10-20 minutes
• Safe Output: create-discussion with max=1

Timeout Configuration

Timeout (minutes)	Count	Use Case
10	277	Standard timeout for most workflows
20	104	Extended timeout for complex analysis
15	18	Medium-complexity workflows
5	14	Quick smoke tests and checks
30	11	Long-running analysis
45	4	Very complex workflows
60	2	Maximum timeout for intensive tasks

Average Timeout: 11 minutes

Pattern: Most workflows (277 uses) default to 10-minute timeout, with a secondary group using 20 minutes for more complex operations.

Permission Patterns

Most Common Permissions

Permission	Count	Typical Access Level
contents	99	read (repository code access)
issues	86	write (for creating/updating issues)
pull-requests	85	write (for creating/updating PRs)
actions	47	read (workflow run access)
discussions	14	write (for creating discussions)
security-events	6	read (security scanning access)
repository-projects	3	read (project board access)

Permission Scope Analysis

• Read-only workflows: 88 (84.6%)
• With write permissions: 7 (6.7%)
• Mixed (read + write to specific resources): 9 (8.7%)

Security Posture: The overwhelming majority (84.6%) of workflows use read-only base permissions, only requesting write access for specific resources (issues, pull-requests) through safe-output mechanisms. This demonstrates strong security practices.

Tool & Configuration Patterns

Tool Usage

Tool	Count	Percentage	Use Case
bash	62	59.6%	Command execution and scripting
cache-memory	42	40.4%	Persistent state across runs
web-fetch	7	6.7%	External data retrieval
web-search	2	1.9%	Web search capabilities

Observation: Bash is the most essential tool (59.6%), while cache-memory (40.4%) indicates significant use of persistent state for tracking trends and history.

Strict Mode Usage

• Workflows with strict mode: 37 (35.6%)
• Workflows without strict mode: 67 (64.4%)

Definition: Strict mode enforces stricter validation and security checks in workflow execution.

Pattern: About one-third of workflows use strict mode, typically for security-sensitive operations like malicious code scanning, file access, and token management.

Concurrency Controls

• Workflows with concurrency settings: 2 (1.9%)
  • cloclo.lock.yml
  • tidy.lock.yml

Finding: Very few workflows implement concurrency controls, suggesting most workflows are designed to run independently without conflicts.

Common Imports

Top 10 shared imports:

Import	Count	Purpose
shared/reporting.md	40	Common reporting formats and structures
shared/jqschema.md	17	JSON schema processing utilities
shared/mcp/ghaw.md	9	GitHub Agentic Workflows MCP integration
shared/trendingchartssimple.md	6	Simple trending chart generation
shared/pythondataviz.md	6	Python data visualization helpers
shared/trends.md	5	Trend analysis utilities
shared/mcp/tavily.md	4	Tavily search MCP integration
shared/copilotprdatafetch.md	4	Copilot PR data fetching utilities
shared/safeoutputapp.md	3	Safe output helpers
shared/mcp/brave.md	2	Brave search MCP integration

Key Finding: shared/reporting.md is imported by 38.5% of workflows, indicating strong standardization around reporting formats. This promotes consistency across analyses.

Workflow Categories

By Naming Patterns

Category	Count	Examples
Daily workflows	14	daily-code-metrics, daily-team-status, daily-news
Smoke test workflows	8	smoke-claude, smoke-copilot, smoke-codex
Test workflows	7	test-app-token, test-firewall-default
Copilot-related	9	copilot-agent-analysis, copilot-session-insights

By Purpose (inferred from names and safe outputs)

1. Monitoring & Auditing (30+ workflows): Continuous monitoring of repository health, security, and quality

   • Examples: audit-workflows, safe-output-health, daily-firewall-report

2. Code Review & PR Analysis (15+ workflows): Automated code review, PR feedback, and analysis

   • Examples: grumpy-reviewer, pr-nitpick-reviewer, copilot-pr-nlp-analysis

3. Documentation (10+ workflows): Documentation generation, updates, and validation

   • Examples: technical-doc-writer, daily-doc-updater, unbloat-docs

4. Issue Management (10+ workflows): Issue triage, classification, and assignment

   • Examples: issue-triage-agent, issue-classifier, issue-arborist

5. Testing & Smoke Tests (8+ workflows): Automated testing and health checks

   • Examples: smoke-claude, smoke-copilot, smoke-detector

6. Metrics & Analytics (10+ workflows): Data collection, analysis, and visualization

   • Examples: daily-code-metrics, copilot-session-insights, python-data-charts

7. Maintenance & Cleanup (5+ workflows): Repository maintenance and cleanup tasks

   • Examples: tidy, close-old-discussions, stale-repo-identifier

Interesting Findings

1. Copilot Dominance: 40.4% of workflows use the Copilot engine, significantly more than Claude (25%) or Codex (7.7%). This suggests strong integration with GitHub's native AI capabilities and possibly better performance or cost characteristics for this use case.

2. Manual Override Pattern: 77.9% of workflows support workflow_dispatch, even when scheduled. This design pattern enables developers to manually trigger workflows for debugging, testing, or ad-hoc analysis without waiting for scheduled runs.

3. Discussion-First Reporting: 39 workflows (44.3% of those with safe outputs) use create-discussion as their primary output mechanism, with most (89.7%) automatically closing older discussions. This creates a "single source of truth" pattern where the latest analysis is always the most visible.

4. Security-Conscious Design: 84.6% of workflows use read-only base permissions, only granting write access through controlled safe-output mechanisms. This demonstrates defense-in-depth security practices.

5. Standardized Reporting: The shared/reporting.md import is used by 40 workflows (38.5%), indicating strong standardization around reporting formats and structures across the repository.

6. Timeout Standardization: 277 workflow steps use the 10-minute timeout, with 104 using 20 minutes. This bimodal distribution suggests two classes of operations: quick checks and deeper analysis.

7. Morning UTC Scheduling: Most scheduled workflows run in the morning UTC hours (8-10 AM), optimizing for European working hours while overnight for US timezones.

8. Size Consistency: 87.5% of lock files fall in the 200-400 KB range, indicating consistent complexity across workflows despite diverse purposes.

9. Strict Mode Adoption: Only 35.6% of workflows use strict mode, suggesting it's reserved for security-sensitive operations rather than being a default setting.

10. Low Concurrency Needs: Only 2 workflows (1.9%) implement concurrency controls, indicating workflows are designed to be independent and non-conflicting.

Recommendations

Based on this statistical analysis, here are recommendations for improving agentic workflow practices:

For New Workflow Authors

1. Follow the 300 KB Standard: Target ~300 KB for lock file size with ~60 steps for consistency
2. Use schedule + workflow_dispatch: Enable both automated and manual execution for flexibility
3. Prefer create-discussion with max=1: For reporting workflows, consolidate findings into a single discussion
4. Import shared/reporting.md: Use standardized reporting formats for consistency
5. Start with 10-minute timeout: Use 10 minutes for quick checks, 20 minutes for deeper analysis
6. Use read-only base permissions: Only request write permissions through safe outputs
7. Set close-older-discussions: true: Keep the discussion list clean by auto-closing superseded reports

For Repository Maintainers

1. Monitor Engine Distribution: Track which engines perform best for different workflow types
2. Consider Strict Mode Defaults: Evaluate whether strict mode should be default for new workflows
3. Standardize Discussion Categories: The "audits" vs "Audits" inconsistency should be resolved (case sensitivity)
4. Document Schedule Patterns: Create guidelines for choosing appropriate cron schedules
5. Review Large Workflows: The poem-bot.lock.yml (605 KB, 115 steps) may benefit from decomposition
6. Track Historical Trends: Use the cache-memory analysis data to monitor growth and complexity over time

For Platform Development

1. Default Templates: Create workflow templates based on these common patterns
2. Validation Rules: Warn when workflows deviate significantly from the 300 KB / 60-step norm
3. Permission Presets: Provide "read-only with safe outputs" as a recommended permission preset
4. Schedule Optimizer: Help users choose appropriate cron schedules based on workflow type
5. Import Discovery: Make shared imports more discoverable to promote code reuse

Methodology

Data Collection

• Lock Files Analyzed: 104
• Analysis Tool: Bash scripts with YAML parsing and text processing
• Cache Memory: Used for script persistence and historical data tracking
• Data Sources: All .lock.yml files in .github/workflows/ directory

Analysis Scripts

Analysis performed using multiple specialized scripts:

• analyze_lockfiles.sh - Primary data extraction
• extract_detailed.sh - Engine, tool, and configuration extraction
• safe_output_analysis.sh - Safe output pattern analysis
• final_stats.sh - Schedule patterns and trigger combinations

Data Quality

• All 104 lock files successfully parsed
• No corrupted or malformed files encountered
• Frontmatter YAML sections extracted from comment blocks
• Counts verified through multiple independent queries

Limitations

• MCP server configuration extraction incomplete (0 results) - requires deeper YAML parsing
• Job count extraction needs refinement (returned 0 for average jobs)
• Some edge cases in schedule pattern extraction
• Historical comparison not available (first analysis run)

Cache Persistence

Analysis scripts and results saved to /tmp/gh-aw/cache-memory/ for:

• Future reuse and faster subsequent analyses
• Historical trend tracking over time
• Pattern library development
• Continuous improvement of analysis methods

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Generated by Lockfile Statistics Analysis Agent on 2024-12-04T00:00:00Z

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.