[prompt-clustering] Copilot Agent Prompt Clustering Analysis — 2026-03-27 #23269
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This discussion was automatically closed because it expired on 2026-03-28T20:22:54.195Z.
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Summary
Analysis Period: Last 30 days (2026-02-25 to 2026-03-27)
Total Tasks Analyzed: 411
Clusters Identified: 7
Overall Success Rate: 76% (311 merged / 411 total)
Cluster Analysis Details
Cluster Overview
Detailed Cluster Analysis
Updates & Dependency Management
update,github,version,cli,mcp,reviewBug Fixes via Issue Resolution
job,failure,error,issue,workflow,stepSafe Outputs & Copilot Feature Dev
issue,section,copilot,workflow,issue copilot,agentCI/CD Failure Investigation
job,fix,workflow,logs identify,actions workflow,analyze workflowAgentic Workflow Architecture
remove,agentic,workflows,agentic workflows,pull,pull requestFeature Planning & Prompt Engineering
plan,discussion github,discussion,issue,context,githubSafe-Outputs Infrastructure
safe,safe output,output,outputs,safe outputs,supportKey Findings & Recommendations
Key Findings
CI/CD Failure Investigation has the highest success rate (86%): Tasks that ask the agent to analyze failing GitHub Actions workflows, identify root causes, and implement fixes are the most reliably completed. The agent excels at structured debugging workflows.
Bug Fixes via Issues is also high-performing (84%): Structured issue-based bug fix requests (with clear issue titles, descriptions, and context) achieve strong merge rates. The structured
<issue_title>+<issue_description>prompt format helps the agent understand scope.Feature Planning & Prompts has the lowest merge rate (65%): Open-ended planning, prompt engineering, and feature expansion tasks are harder — likely due to more subjective review standards and larger scope uncertainty.
Updates & Dependency Management is the largest cluster (29% of all tasks): Routine dependency/version bump tasks dominate the workload with 76% success, indicating these are well-understood patterns but can fail when there are breaking changes.
Safe Outputs & Copilot Feature Dev (66% merge rate): Feature development through issue-based copilot prompts has more variability — larger feature scope leads to more iteration/review cycles.
Recommendations
Prefer structured issue prompts for bug fixes: The bug-fix cluster's 84% success rate correlates with well-structured
<issue_title>+<issue_description>prompt patterns. Apply this format more broadly across task types.Decompose planning tasks into concrete sub-tasks: The 65% success rate on planning/prompt-engineering tasks suggests these benefit from being split into more targeted, actionable sub-tasks before assignment.
CI failure investigation is a strength — lean into it: With 86% success, automated CI failure detection and agent assignment is high-ROI. Consider expanding coverage to more workflow types.
Monitor open PRs in Agentic Architecture cluster: 6 open PRs (14%) in the architecture cluster suggest larger, more complex changes that may need additional review time or scope reduction.
Full PR Data Table (First 80 PRs by cluster)
safe-outputs.stepsfor injecting custom stepsnoopin safe-output tools promptset-issue-typesafe output typesafe-inputstomcp-scriptsagentartifactupdate_issueandupdate_discussionSupportsFirewallfrom agentic engine interfacesupportsLLMGatewaybool tollmGatewayPortfooter: falsesupport toadd-commentaddtriggers:keyword misuse in compilefetchoption tocheckout:for cross-repo branchescopilot-requests: truefeature from all agenticexcluded-filesfield tocreate-pull-requestduplicatestate_reason to close-issueReferences: Workflow Run §23664944049
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