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Large refactoring tasks (need better decomposition)
Tasks requiring deep architectural knowledge
Open-ended feature requests without clear acceptance criteria
Success Factors:
Clear, concise task descriptions
Well-defined acceptance criteria
Reference to existing patterns in codebase
Incremental approach (smaller PRs)
Visualizations
Generated analysis charts:
Cluster size distribution
Success rate by cluster
Task complexity scatter plot
Charts saved to workflow artifacts
Methodology: TF-IDF vectorization with K-means clustering (k=7) on 2,584 copilot agent task prompts from the last 30 days. Optimal cluster count determined via elbow method. Keywords extracted via frequency analysis.
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Daily NLP-based clustering analysis of copilot agent task prompts.
Summary
Analysis Period: Last 30 days
Total Tasks Analyzed: 2,584
Clusters Identified: 7
Overall Success Rate: 73.1%
Total PRs Merged: 1,888
Key Findings
Full Analysis Report
Cluster Breakdown
Cluster 1: Bug Fixes & Small Improvements
Targeted fixes, small feature additions, and error corrections
Statistics:
Cluster 2: Workflow Configuration
Updates to workflow files, GitHub Actions, and CI/CD configurations
Statistics:
Cluster 3: MCP Server & Integration
MCP server changes, version updates, and integration improvements
Statistics:
Cluster 4: Feature Development
New features, major updates, and general development tasks
Statistics:
Cluster 5: Agentic Workflow Creation
Creating and updating agentic workflow files
Statistics:
Cluster 6: Code Refactoring
Internal refactoring, code cleanup, and maintainability improvements
Statistics:
Cluster 7: Issue Management
Safe output tools, issue creation/updates, and workflow orchestration
Statistics:
Recommendations
Based on the clustering analysis:
Optimize for Bug Fixes: Cluster 1 (bug fixes) has the highest success rate (80.0%). These tasks are well-suited for the agent.
Large-Scale Changes Need Care: Clusters with high complexity (1,500+ lines) show more variability
General Development Tasks: Cluster 3 (40% of tasks) represents general feature work
MCP & Integration Work: Cluster 2 shows good results for technical integrations
Sample Tasks by Cluster
Representative sample showing task distribution and outcomes:
Analysis Insights
What Works Well:
Areas for Improvement:
Success Factors:
Visualizations
Generated analysis charts:
Charts saved to workflow artifacts
Methodology: TF-IDF vectorization with K-means clustering (k=7) on 2,584 copilot agent task prompts from the last 30 days. Optimal cluster count determined via elbow method. Keywords extracted via frequency analysis.
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