[prompt-clustering] 🔬 Copilot Agent Prompt Clustering Analysis - January 2026 #12473
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This discussion was automatically closed because it expired on 2026-02-05T06:47:02.424Z. |
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Daily NLP-based clustering analysis of copilot agent task prompts to identify patterns, trends, and optimization opportunities.
Summary
Analysis Period: 2025-10-22 to 2026-01-29
Total Tasks Analyzed: 4,389
Clusters Identified: 7
Overall Success Rate: 70.0%
Key Findings
Notable Trend
View Detailed Analysis
Cluster Breakdown
🔒 Cluster 6: Security Fixes
Example task prompt
Success Rate Comparison
Sample Tasks by Cluster
Recommendations
Improve MCP/Gateway Tasks: Cluster 2 (MCP-related) has the lowest success rate (61.4%) and highest iteration cycles (4.5 comments). Consider:
Reduce Iteration Cycles: Tasks averaging 4+ comments indicate unclear requirements. Consider:
Investigate January 2026 Decline: Success rate dropped from 76.2% (Nov 2025) to 64.8% (Jan 2026). Investigate:
Leverage Successful Patterns: Cluster 6 (74.5% success) shows successful characteristics:
Temporal Trends
Monthly Activity
Task Complexity Distribution
Insight: Medium and Complex tasks (5-30 files) have better success rates than Simple tasks, suggesting agents perform better with well-scoped, moderately complex tasks than with very simple or very complex ones.
Methodology
This analysis uses NLP clustering (K-means with TF-IDF vectorization) on 4,389 copilot agent task prompts extracted from PRs. Tasks are grouped into 7 semantic clusters based on prompt content, then enriched with success metrics and complexity measures.
Analysis Date: 2026-01-29 06:44 UTC
Workflow Run: §21468085708
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