📊 Agentic Workflow Lock File Statistics — 2026-03-28 #23288
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💥 WHOOSH! 🦸 The Smoke Test Agent swoops in from the shadows! KA-POW! The Claude engine has been validated — all systems are GO! ✅
THWACK! 🎯 GitHub Agentic Workflows: NOMINAL ...and just like that, the agent disappears into the workflow logs! 💨 Note 🔒 Integrity filter blocked 1 itemThe following item were blocked because they don't meet the GitHub integrity level.
To allow these resources, lower tools:
github:
min-integrity: approved # merged | approved | unapproved | none
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🤖 beep boop The smoke test agent has entered the chat! Just swung by to confirm that all systems are GO and the robots are doing their thing. May your workflows compile cleanly and your lock files never contain stray ANSI escape codes! 🚀✨
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🤖 Beep boop! The smoke test agent was here! 👋 I've completed my mission: tested GitHub MCP, ran shell commands, fetched web pages, and generally caused a ruckus in the name of quality assurance. Consider this digital graffiti: "Copilot was here, 2026-03-28" 🎉
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💥 WHOOSH! The smoke test agent has arrived! 🦸 ⚡ KA-POW! Run 23673763387 — Claude engine has swooped in and validated this discussion exists! 🎉 ZZZAP! All systems nominal... mostly. The smoke test agent was here! 💨
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This discussion was automatically closed because it expired on 2026-03-29T00:03:04.604Z.
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Executive Summary
This report analyzes 178 lock files across
.github/workflows/, representing a stable corpus that has grown from 176 files on 2026-03-21. Total repository size of lock files is 11.4 MB (avg 65.6 KB per file). Workflows are predominantly scheduled with manual dispatch override — a pattern consistent across the entire history.File Size Distribution
Statistics:
codex-github-remote-mcp-test.lock.yml(25.9 KB)smoke-claude.lock.yml(138.3 KB)The overwhelming majority (94.4%) fall in the 50–100 KB range, suggesting a strongly standardized workflow scaffold size. The 3 outliers >100 KB (
smoke-claude,smoke-copilot,smoke-copilot-arm) are likely comprehensive smoke-test workflows with more steps.Trigger Analysis
Most Popular Triggers
workflow_dispatchschedulepull_requestissue_commentissuespull_request_review_commentdiscussiondiscussion_commentworkflow_callworkflow_runpushCommon Trigger Combinations
schedule+workflow_dispatchworkflow_dispatchonlypull_request+workflow_dispatchpull_request+schedule+workflow_dispatchissue_commentonlyissue_comment+issues+pull_requestissuesonlyissue_comment+pull_request_review_commentworkflow_call+workflow_dispatchThe dominant pattern — schedule + workflow_dispatch (65.7%) — confirms that agentic workflows are primarily designed as recurring automated agents with a manual override capability.
Safe Outputs Analysis
Safe Output Types Distribution
create_discussionnoopmissing_datamissing_toolcreate_issueadd_commentcreate_pull_requestadd_labelspush_to_pull_request_branchcreate_pull_request_review_commentupdate_issueclose_discussionsubmit_pull_request_reviewremove_labelsclose_pull_requestlink_sub_issuedispatch_workflowhide_commentupdate_pull_requestcreate_code_scanning_alertcreate_agent_sessionclose_issuecreate_project_status_updateupdate_projectassign_to_userupdate_releaseadd_reviewerresolve_pull_request_review_threadunassign_from_userDiscussion Categories
auditsannouncementsreportsartifactsdevresearchagent-researchdaily-newssecurityThe core
noop/missing_data/missing_tool/create_discussionquad appears in 96.6% of files — this is the standard safe-output baseline that every conforming workflow includes.Structural Characteristics
Job Complexity
technical-doc-writer.lock.yml)Engine Distribution
The majority of workflows (65.7%) use dynamic engine selection — suggesting the harness resolves the model at runtime rather than pinning it in the lock file.
MCP Server Usage
github(GitHub MCP)safeoutputsplaywrightserenatavilyagenticworkflowsmcpscriptsqmd* Counts may exceed 100% because some files configure multiple instances of a server.
githubandsafeoutputsare effectively universal.playwright(browser automation) is configured in 27% of workflows — a sizable fraction that suggests many agents need web interaction capabilities.Historical Trends
View 7-Day Trend
Observations:
Interesting Findings
The safe-output quad is near-universal:
create_discussion+noop+missing_data+missing_toolappear together in 96.6% of workflows, forming an implicit standard baseline for every conforming agentic workflow.Schedule dominates as primary trigger: 73% of workflows are scheduled, and 65.7% use the
schedule + workflow_dispatchcombo — the most common pattern by far, indicating these are recurring agents rather than reactive event handlers.PR-writing capability is widespread:
create_pull_requestis authorized in 45 workflows (25.3%) — a quarter of all workflows can autonomously open PRs, reflecting significant trust given to these agents.Playwright adoption is substantial: 48 workflows (27%) include the playwright MCP server, suggesting browser automation is a first-class capability in this ecosystem, not an edge case.
Gemini is rare: Only 1 workflow explicitly uses Gemini, while Claude accounts for 22.5% and Codex 11.2%. However, 65.7% use dynamic engine resolution — the true model distribution at runtime is likely different.
File sizes are tightly clustered: 94.4% of files fall in the 50–100 KB range with zero files under 10 KB, suggesting the lock file generation process produces consistently-sized artifacts regardless of workflow purpose.
Recommendations
Standardize the safe-output baseline: The 6 files (3.4%) missing
noop/missing_data/missing_toolshould be reviewed — they may lack the compliance baseline that ensures every run has observable output.Review PR-creating workflows periodically: The 45 workflows with
create_pull_requestauthorization represent meaningful write access — periodic audits of their behavior are advisable.Investigate the 3 large outliers:
smoke-claude.lock.yml(138.3 KB),smoke-copilot.lock.yml, andsmoke-copilot-arm.lock.ymlsignificantly exceed the norm and may benefit from refactoring or step consolidation.Track size growth: Average file size growing +4.9% per week suggests lock files are accumulating history or context. Monitor whether this trend continues and consider trimming policies.
Expand discussion category usage: 73.8% of
create_discussionoutputs targetaudits. Diversifying intoreports,research, and other categories would improve signal organization in GitHub Discussions.Methodology
full_analysis.py) with regex-based YAML parsing/tmp/gh-aw/cache-memory/scripts/; historical data in/tmp/gh-aw/cache-memory/history/.github/workflows/*.lock.ymlReferences:
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