[prompt-clustering] Copilot Agent Prompt Clustering Analysis - Feb 7, 2026 #14278
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Analysis Date: 2026-02-07
Daily NLP-based clustering analysis of copilot agent task prompts to identify patterns and optimization opportunities.
Summary
Analysis Period: 2026-01-21 to 2026-02-07
Total Tasks Analyzed: 984
Clusters Identified: 3
Overall Success Rate: 69.3% (682 merged)
Average Task Size: 511 additions, 396 deletions
Key Findings
View Full Analysis Report
Cluster Overview
Detailed Cluster Analysis
Cluster 3: Dependency & Version Updates
Statistics:
Characteristic Keywords: workflow, test, mcp, url, github, workflows
TF-IDF Terms: update, workflow, project, campaign, agentic
Example Tasks:
Sample Prompt
Full Data Table (First 20 PRs)
Showing first 20 of 984 PRs (full table available in generated report)
Recommendations
Based on the clustering analysis, here are actionable recommendations:
1. Improve Success Rate for Complex Bug Fixes (Cluster 1)
Current State: 54.4% success rate
Issue: These tasks involve complex workflow changes and multiple dependencies
Recommendation:
2. Optimize Large Code Changes (Cluster 2)
Current State: Average 1106 additions per task
Issue: Large PRs are harder to review and more likely to have issues
Recommendation:
3. Leverage Successful Patterns from Dependency Updates (Cluster 3)
Current State: 75.6% success rate
Success Factors: Clear, specific prompts with version references
Recommendation:
4. Improve Prompt Engineering for Bug Fixes
Observation: Bug fix prompts vary significantly in clarity
Recommendation:
Methodology
This analysis used:
Next Steps
Workflow Run: §21774270699
This is an automated daily analysis. Full report and data available in workflow artifacts.
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