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Characteristics: Tasks in this cluster are relatively straightforward (3.3 commits vs 3.7 average).
Example Tasks:
PR #5781: Fix linting errors and bundling failures in JavaScript workflow scripts
PR #7291: Fix choice type conversion to JSON Schema in safe-output jobs
PR #2935: Fix missing JavaScript files and implement security sanitization features
Success Rate by Cluster
Cluster
Theme
Tasks
Success Rate
Avg Commits
Avg Files
Top Keywords
10
Documentation Updates
121
81.0%
3.3
18.4
fix, tests, error
6
New Features (Complex)
193
79.8%
5.4
35.7
file, actions, github
4
New Features
246
76.8%
3.6
12.8
agentic, workflow
9
Updates & Modifications
132
76.5%
3.4
21.0
code, duplicate
1
Testing & Test Coverage
615
74.8%
3.6
20.7
update, make, changes
2
Gh Aw & Gh
601
72.7%
3.2
12.6
gh aw, gh, aw
3
Bug Fixes
272
71.3%
3.3
21.9
cli, version, comments
7
Bug Fixes (Agent)
182
69.2%
3.8
22.0
agent, github, workflow
5
Bug Fixes (Complex)
224
65.6%
4.4
28.8
safe, add, output
8
New Features (MCP)
163
64.4%
4.3
24.1
mcp, server, github
Key Findings
Documentation tasks have the highest success rate (81.0%), suggesting these are well-suited for copilot agents with clear, straightforward requirements.
Complex new features require significant effort - Cluster 6 tasks average 5.4 commits and 35.7 files changed, yet maintain a 79.8% success rate, indicating good agent performance on complex work.
Testing & test coverage is the dominant use case (22.4% of all tasks), demonstrating that test-related work is a primary application for the copilot agent.
MCP-related features have the lowest success rate (64.4% in Cluster 8), suggesting this domain may need more specialized context or examples in prompts.
Task complexity correlates with commit count - Complex clusters (5, 6, 8) average 4.3-5.4 commits vs 3.2-3.6 for straightforward clusters.
Recommendations
Based on the clustering analysis:
Leverage documentation patterns: With an 81% success rate, use similar prompt structures for documentation tasks as templates for other task types.
Improve MCP-related prompts: Given the lower success rate (64.4%), consider adding more technical context, code examples, or breaking MCP tasks into smaller subtasks.
Optimize for testing workflows: Since testing represents 22.4% of tasks, invest in specialized test generation prompts and patterns.
Break down complex features: For tasks involving 30+ files or significant refactoring, consider decomposing into smaller, staged PRs to improve success rates.
Monitor cluster-specific patterns: Track success rates by cluster over time to identify improving or declining task categories.
Methodology: This analysis used TF-IDF vectorization and K-means clustering (k=10, optimized via silhouette score) on 2,749 copilot agent task prompts extracted from PR bodies over the last 30 days.
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Daily NLP-based clustering analysis of copilot agent task prompts.
Analysis Date: 2025-12-31
Analysis Period: Last 30 days
Total Tasks Analyzed: 2,749
Clusters Identified: 10
Overall Success Rate: 73.2%
Summary
Full Analysis Report
Cluster Analysis
Cluster 1: Testing & Test Coverage
Characteristics: Tasks in this cluster are relatively straightforward (3.6 commits vs 3.7 average).
Example Tasks:
Cluster 2: Gh Aw & Gh
Characteristics: Tasks in this cluster are relatively straightforward (3.2 commits vs 3.7 average).
Example Tasks:
Cluster 3: Bug Fixes
Characteristics: Tasks in this cluster are relatively straightforward (3.3 commits vs 3.7 average).
Example Tasks:
Cluster 4: New Features
Characteristics: Tasks in this cluster are relatively straightforward (3.6 commits vs 3.7 average).
Example Tasks:
Cluster 5: Bug Fixes (Complex)
Characteristics: Tasks in this cluster typically require more iterations (4.4 commits vs 3.7 average).
Example Tasks:
Cluster 6: New Features (Complex)
Characteristics: Tasks in this cluster typically require more iterations (5.4 commits vs 3.7 average).
Example Tasks:
Cluster 7: Bug Fixes (Agent-Related)
Characteristics: Tasks in this cluster typically require more iterations (3.8 commits vs 3.7 average).
Example Tasks:
Cluster 8: New Features (MCP-Related)
Characteristics: Tasks in this cluster typically require more iterations (4.3 commits vs 3.7 average).
Example Tasks:
Cluster 9: Updates & Modifications
Characteristics: Tasks in this cluster are relatively straightforward (3.4 commits vs 3.7 average).
Example Tasks:
Cluster 10: Documentation Updates
Characteristics: Tasks in this cluster are relatively straightforward (3.3 commits vs 3.7 average).
Example Tasks:
Success Rate by Cluster
Key Findings
Documentation tasks have the highest success rate (81.0%), suggesting these are well-suited for copilot agents with clear, straightforward requirements.
Complex new features require significant effort - Cluster 6 tasks average 5.4 commits and 35.7 files changed, yet maintain a 79.8% success rate, indicating good agent performance on complex work.
Testing & test coverage is the dominant use case (22.4% of all tasks), demonstrating that test-related work is a primary application for the copilot agent.
MCP-related features have the lowest success rate (64.4% in Cluster 8), suggesting this domain may need more specialized context or examples in prompts.
Task complexity correlates with commit count - Complex clusters (5, 6, 8) average 4.3-5.4 commits vs 3.2-3.6 for straightforward clusters.
Recommendations
Based on the clustering analysis:
Leverage documentation patterns: With an 81% success rate, use similar prompt structures for documentation tasks as templates for other task types.
Improve MCP-related prompts: Given the lower success rate (64.4%), consider adding more technical context, code examples, or breaking MCP tasks into smaller subtasks.
Optimize for testing workflows: Since testing represents 22.4% of tasks, invest in specialized test generation prompts and patterns.
Break down complex features: For tasks involving 30+ files or significant refactoring, consider decomposing into smaller, staged PRs to improve success rates.
Monitor cluster-specific patterns: Track success rates by cluster over time to identify improving or declining task categories.
Methodology: This analysis used TF-IDF vectorization and K-means clustering (k=10, optimized via silhouette score) on 2,749 copilot agent task prompts extracted from PR bodies over the last 30 days.
References:
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