🔬 Copilot Agent Prompt Clustering Analysis - December 19, 2025

Daily NLP-based clustering analysis of copilot agent task prompts to identify patterns, success factors, and optimization opportunities.

Analysis Overview

Analysis Period: October 22 - November 18, 2025 (30 days)
Total Tasks Analyzed: 1,000 copilot-created PRs
Clusters Identified: 3 distinct task categories
Overall Success Rate: 77.4% (774 merged PRs)
Average Task Complexity: 13.7 files changed, 632 lines added

Executive Summary

This analysis examined 1,000 copilot agent tasks using TF-IDF vectorization and K-means clustering to identify distinct task patterns. The analysis reveals three primary task categories with varying complexity levels and success rates. The dominant cluster (59.3% of tasks) focuses on workflow and configuration updates, while two smaller clusters handle documentation/instructions and general code modifications.

Key Findings:

• Workflow-focused tasks (Cluster 3) dominate at 59.3% of all tasks
• Documentation/instruction tasks (Cluster 2) achieve the highest success rate at 81.2%
• General code modifications (Cluster 1) show the most complexity with 16.3 files changed on average
• All clusters maintain healthy success rates above 70%, indicating consistent agent performance

Comparative Analysis

Success Rate Comparison

Key Insight: Success rate inversely correlates with task ambiguity, not complexity. Cluster 2 has high complexity (16.1 files) but high success (81.2%) because requirements are typically well-defined. Cluster 1 has similar complexity (16.3 files) but lower success (72.4%) due to more ambiguous infrastructure changes.

Complexity Analysis

Key Insight: Cluster 1 shows the highest deletion ratio (415 lines deleted vs 907 added = 45.7% deletion rate), suggesting more refactoring and code cleanup work. This aligns with lower success rates as refactoring is inherently more complex than additive changes.

Task Distribution

Cluster 3 (Workflows): ████████████████████████████████████████████████ 59.3%
Cluster 2 (Docs/Config): ████████████████ 20.8%
Cluster 1 (General): ███████████████ 19.9%

Key Insight: The task distribution reflects the repository's focus - this is a GitHub Actions workflow system (gh-aw), so it's expected that 60% of agent work involves workflow modifications. The remaining 40% is split between documentation/configuration and general code improvements.

Clustering Methodology

Technical Approach

1. Data Collection: 1,000 PR bodies from copilot-created PRs (Oct 22 - Nov 18, 2025)
2. Text Preprocessing:
   • Removed markdown code blocks, URLs, special characters
   • Extracted task descriptions from PR bodies
   • Normalized whitespace and formatting
3. Feature Engineering:
   • TF-IDF vectorization (150 features)
   • N-gram range: 1-3 (unigrams, bigrams, trigrams)
   • Minimum document frequency: 2
   • Maximum document frequency: 80%
4. Clustering:
   • Algorithm: K-means with k=3
   • Selection method: Elbow method + Silhouette score
   • Random seed: 42 (reproducible)
5. Validation:
   • Silhouette analysis for cluster quality
   • PCA visualization for cluster separation
   • Manual inspection of cluster coherence

Cluster Quality Metrics

The elbow method suggested k=3 clusters, while silhouette score peaked at k=10. We selected k=3 for:

• Interpretability: 3 clusters are easier to understand and actionable
• Balance: Each cluster has sufficient size (199-593 tasks)
• Coherence: Manual inspection confirmed semantic coherence
• Practicality: 3 categories align with observable task types

Key Findings & Insights

Finding 1: Task Type Predicts Success More Than Complexity

Observation: Cluster 2 (Documentation) achieves 81.2% success despite high complexity (16.1 files), while Cluster 1 (General) achieves only 72.4% with similar complexity (16.3 files).

Insight: The clarity and structure of task requirements matter more than the number of files modified. Documentation tasks have clear success criteria (correct information, proper formatting), while infrastructure tasks may have ambiguous requirements or require broader context.

Impact: Task definition quality is the primary success factor, not task size.

Finding 2: Workflow Tasks Show Excellent Efficiency

Observation: Cluster 3 (Workflows) represents 59.3% of tasks but requires only 1.3 comments per PR on average - the lowest engagement level.

Insight: The agent is highly effective at routine workflow modifications. These tasks get right on the first attempt more often, requiring fewer review cycles.

Impact: Copilot agents are ideal for automating workflow maintenance and incremental improvements.

Finding 3: Complex Refactoring Needs More Support

Observation: Cluster 1 shows 45.7% deletion ratio (415/907 lines) and highest comment count (1.9 per PR), but lowest success rate (72.4%).

Insight: Refactoring and code cleanup tasks require more iterations and have higher failure rates. The agent may struggle with understanding the full impact of removing or restructuring code.

Impact: Consider providing additional context, breaking down refactoring tasks, or implementing a review phase for destructive changes.

Recommendations

1. Optimize Prompt Engineering for Each Cluster Type

For Cluster 1 (General Code/Infrastructure):

• ✅ Provide explicit architectural context
• ✅ Include examples of similar successful changes
• ✅ Break down into smaller, focused subtasks when modifying 15+ files
• ✅ Specify testing requirements upfront

For Cluster 2 (Documentation/Configuration):

• ✅ Continue current approach - it's working well (81.2% success)
• ✅ Use this cluster as a template for prompt structure
• ✅ Leverage agent for more documentation tasks

For Cluster 3 (Workflows):

• ✅ Maintain current patterns - low engagement indicates good quality
• ✅ Consider expanding agent use for workflow automation
• ✅ Automate repetitive workflow updates

2. Implement Task Complexity Scoring

Create a pre-task assessment to predict difficulty:

Complexity Score = (files_changed × 1) + (deletions × 2) + (architectural_changes × 5)

Low (0-20): Standard workflow - proceed normally
Medium (21-50): Documentation/config - expect high success
High (51+): Infrastructure/refactor - provide extra context

3. Success Pattern Analysis

Study the top 10% of successful PRs in each cluster to identify:

• Common prompt structures
• Effective context provision
• Optimal task sizing
• Clear success criteria

Apply these patterns to future tasks in the same cluster.

4. Focus on High-Value, High-Success Tasks

Priority Matrix:

High Value, High Success: Cluster 2 (Docs/Config) - 81.2% success
  → Increase task allocation

High Volume, Good Success: Cluster 3 (Workflows) - 77.7% success, 59.3% of tasks
  → Continue current approach

High Complexity, Lower Success: Cluster 1 (General) - 72.4% success
  → Improve prompt engineering and task breakdown

5. Continuous Monitoring

Implement monthly clustering analysis to:

• Track success rate trends over time
• Identify emerging task patterns
• Detect degradation in specific clusters
• Measure impact of prompt engineering improvements

Store clustering results in /tmp/gh-aw/cache-memory/trending/clustering/ for historical comparison.

Sample Task Data

Conclusion

This clustering analysis of 1,000 copilot agent tasks reveals three distinct task categories with varying characteristics and success patterns. The overall 77.4% success rate indicates strong agent performance, with particular excellence in documentation and configuration tasks (81.2% success).

Key Takeaways:

1. Documentation tasks are the agent's sweet spot - high complexity but highest success
2. Workflow tasks dominate the workload and show consistent, efficient performance
3. Infrastructure tasks need more support - consider task breakdown and enhanced context
4. Success correlates with task clarity, not complexity

Next Steps:

1. Implement task complexity scoring for pre-task assessment
2. Study top-performing PRs to extract success patterns
3. Apply cluster-specific prompt engineering strategies
4. Schedule monthly clustering analysis for trend monitoring
5. Store results in cache-memory for historical comparison

**Analysis Meta(redacted)

• Generated: 2025-12-19 06:39:14 UTC
• Method: K-means clustering (k=3) on TF-IDF vectors
• Data: 1,000 PRs from 2025-10-22 to 2025-11-18
• Tools: scikit-learn, pandas, matplotlib, seaborn
• Workflow Run: §20361803000

AI generated by Copilot Agent Prompt Clustering Analysis