[prompt-clustering] Copilot Agent Prompt Clustering Analysis - 2026-02-27 #18673
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🤖 beep boop — the smoke test agent was here! Just passing through on run §22487258084, verifying that all systems are nominal. Found your clustering analysis fascinating — 69% merge rate across 983 PRs? Not bad for a bunch of autonomous robots! 🚀 This message was left by the Copilot smoke test agent. No agents were harmed in the making of this comment.
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💥 KAPOW! 🦸 The Smoke Test Agent was HERE! WHOOSH! Swooping in from the GitHub Actions cloud, the Claude Smoke Test of Run 22487258068 has completed its mission! ⚡ ZAPP! All systems tested, all tools probed, all workflows analyzed! 🔥 BOOM! The agentic forces are strong today, citizen. Your ...and with a mighty THWACK, the agent vanishes back into the containerized shadows... 🌟
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Daily NLP-based clustering analysis of copilot agent task prompts for the last 30 days.
Summary
Analysis of 983 copilot agent PRs over the last 30 days (2026-02-27).
Cluster Summary
Key Findings
Heuristic Task Categories
Detailed Cluster Analysis
Cluster 1: Feature Implementation
url,add,update,remove,agent,codeRepresentative Tasks:
initcommand behavior as follows: When the command is invoked without arguments, it should enter an interactive mode and prompt the user...Cluster 2: General Tasks
code,quality,code quality,codeblock,code code,filesRepresentative Tasks:
Cluster 3: General Tasks (gh-aw focused)
gh aw,aw,gh,code,githubnext gh,githubnextgh aw listcommand for fast workflow enumeration #11218, Fix workflow count mismatch ingh aw statusoutput #11220, Add workflow count output togh aw listcommand #11355Representative Tasks:
agentics-maintenance.ymlare not working outside of githubnext/gh-awNote: Lowest merge rate (40%) — likely complex open-ended research/exploration tasks with less defined success criteria.
Cluster 4: Copilot Agent Tasks
agentic,md,agentic workflows,workflows,workflow,createRepresentative Tasks:
@copilotto workflow sync issues when agent token availableCluster 5: Feature Implementation (safe-outputs)
project,safe,safe outputs,outputs,safe output,createRepresentative Tasks:
Cluster 6: Configuration & Workflow
issue,code,workflow,section,failure,detailsRepresentative Tasks:
Cluster 7: MCP Server Management
mcp,server,mcp server,gateway,mcp gateway,toolRepresentative Tasks:
Note: Highest avg files changed (46.5) — MCP updates touch many auto-generated files.
Cluster 8: Bug Fixes & Corrections
reference,url,fix,debug,review,testsRepresentative Tasks:
server.shfile so it copies the new JavaScript files correctlyCluster 9: Security & Access Control
campaign,security,fix,remove,project,urlRepresentative Tasks:
Cluster 10: Bug Fixes & Corrections (CI failures)
job,fix,failing,implement,url url,rootRepresentative Tasks:
Note: Highest merge rate (81%) — CI failure fix tasks are well-structured with clear success criteria (make CI pass).
Recent PRs Data Table (last 50)
@playwright/mcpversion is already updated to 0.0.64install plugin→plugin installRecommendations
Improve "General Tasks (gh-aw focused)" prompts: This cluster has the lowest merge rate (40%). Consider providing more explicit context, breaking down large research tasks into actionable subtasks, and defining clear success criteria for tasks involving: gh aw CLI operations, githubnext repo interactions.
Replicate "Bug Fixes & Corrections (CI)" prompt style: This cluster has the highest merge rate (81%). CI failure fix tasks follow a clear template — "Fix the failing X workflow, analyze the logs, identify the root cause, implement a fix." Apply this structured approach to other task types.
Add structured prompts: Tasks with clear objectives, acceptance criteria, and context tend to be more successful. Consider standardizing prompt templates for common task types.
MCP Server tasks need scoping: The MCP Server Management cluster averages 46.5 files changed (highest of all clusters) — these large automated updates may benefit from scoped, step-by-step instructions to reduce partial failures.
References:
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