[prompt-clustering] Copilot Agent Prompt Clustering Analysis - December 2, 2025

[github-actions[bot]]

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

Summary

This analysis performed advanced Natural Language Processing (NLP) clustering on 984 copilot agent tasks from the last 30 days using TF-IDF vectorization and K-means clustering. The goal was to identify common patterns, understand what types of tasks succeed, and provide actionable insights for improving agent performance.

Key Findings:

• 3 distinct task clusters identified with different characteristics and success rates
• Overall success rate: 77.2% (760 merged PRs out of 984 total)
• Cluster 2 (code refactoring/internal improvements) shows the highest success rate at 81.0%
• Cluster 1 (general workflow and documentation updates) represents 54.5% of all tasks
• Average task complexity: 898 total changes (files + additions + deletions)

Full Analysis Report

Cluster Profiles

Cluster 1: General Workflow & Documentation Updates (54.5%)

Size: 536 tasks | Success Rate: 75.7%

Characteristics:

• Largest cluster representing majority of agent work
• Focus on agentic workflow updates, documentation improvements, and general enhancements
• Moderate complexity with highest average file changes (15.4 files per task)
• Highest overall code volume (873 additions, 314 deletions per task)

Top Keywords: agentic, update, workflow, add, agent, file, firewall

Typical Tasks:

• Adding new workflow features and capabilities
• Updating documentation and configuration
• Implementing workflow optimizations
• Adding firewall and security features

Performance Metrics:

• Average files changed: 15.4
• Average lines added: 873
• Average lines deleted: 314
• Average complexity: 1,202
• Average comments per PR: 1.8
• Average reviews per PR: 1.8

Example PRs:

• Add minimal path format syntax reference to imports documentation #2097: Add minimal path format syntax reference to imports documentation
• Add directory creation for copilot engine --add-dir paths #2099: Add directory creation for copilot engine --add-dir paths
• Spread scheduled agentic workflows across 24 hours and add 6-hour schedules to smoke tests #2100: Spread scheduled agentic workflows across 24 hours

Insights:

• This cluster shows the breadth of general maintenance and enhancement work
• Success rate slightly below average suggests need for better scoping of large workflow changes
• High file change count indicates multi-component updates that may benefit from decomposition

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Cluster 2: Code Refactoring & Internal Improvements (16.6%)

Size: 163 tasks | Success Rate: 81.0% ⭐ (Best)

Characteristics:

• Smallest cluster but highest success rate
• Focus on code quality, refactoring, and internal codebase improvements
• Lower file count but substantial code changes
• Well-defined, technical tasks with clear boundaries

Top Keywords: pkg, pkg workflow, workflow, functions, code, validation, githubnext gh

Typical Tasks:

• Extracting duplicate code into shared functions
• Refactoring large files into focused modules
• Consolidating utility functions
• Adding test coverage
• Schema and validation improvements

Performance Metrics:

• Average files changed: 9.7 (lowest)
• Average lines added: 569
• Average lines deleted: 200
• Average complexity: 779
• Average comments per PR: 1.4
• Average reviews per PR: 2.0 (highest)

Example PRs:

• Refactor duplicate MCP code patterns for improved maintainability #2171: Refactor duplicate MCP code patterns for improved maintainability
• Remove .serena files and refactor isValidReaction to reactions.go #2242: Remove .serena files and refactor isValidReaction to reactions.go
• Extract extraction functions from compiler.go to frontmatter_extraction.go #2283: Extract extraction functions from compiler.go to frontmatter_extraction.go

Insights:

• Technical refactoring tasks have highest success rate - agents excel at well-defined code improvements
• Lower comment count suggests cleaner execution with fewer iterations
• Higher review count indicates code quality focus
• These tasks are ideal candidates for agent automation

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Cluster 3: Bug Fixes & Maintenance (29.0%)

Size: 285 tasks | Success Rate: 77.9%

Characteristics:

• Second largest cluster, focused on fixing issues and maintenance
• Mix of bug fixes, version updates, and operational improvements
• Moderate file changes with lower code volume
• Reactive rather than proactive work

Top Keywords: gh, workflows, issue, aw, github, gh aw, workflow

Typical Tasks:

• Fixing workflow failures and bugs
• Updating CLI tool versions
• Addressing integration issues
• Schema fixes and validation updates
• Security improvements

Performance Metrics:

• Average files changed: 14.0
• Average lines added: 327 (lowest)
• Average lines deleted: 101
• Average complexity: 441 (lowest)
• Average comments per PR: 1.2 (lowest)
• Average reviews per PR: 1.8

Example PRs:

• Fix Smoke OpenCode workflow failure and update to version 0.15.13 #2127: Fix Smoke OpenCode workflow failure and update to version 0.15.13
• Fix heredoc delimiter collision causing workflow compilation failures #2193: Fix heredoc delimiter collision causing workflow compilation failures
• Pin all GitHub Actions to specific commit SHAs using centralized golden versions #2310: Pin all GitHub Actions to specific commit SHAs using centralized golden versions

Insights:

• Bug fixes and maintenance show good success rate (77.9%)
• Lower complexity suggests focused, targeted fixes
• Lowest comment count indicates efficient execution
• Version updates and schema fixes are routine tasks agents handle well

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Success Rate Analysis by Cluster

Cluster	Theme	Tasks	Success Rate	Avg Complexity	Avg Files
2	Code Refactoring	163	81.0% ⭐	779	9.7
3	Bug Fixes	285	77.9%	441	14.0
1	Workflow Updates	536	75.7%	1,202	15.4

Key Observation: Well-defined technical tasks (refactoring) outperform broad feature additions (workflow updates) by 5.3 percentage points.

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Overall Statistics

Task Distribution

• Total Tasks Analyzed: 984
• Total Merged PRs: 760 (77.2%)
• Total Closed PRs: 0 (filtered out or converted to merged)
• Total Open PRs: 0 (only completed tasks analyzed)

Code Change Metrics

• Average Files Changed: 14.1
• Average Lines Added: 665
• Average Lines Deleted: 233
• Average Complexity: 912 (sum of files + additions + deletions)

Interaction Metrics

• Average Comments per PR: 1.5
• Average Reviews per PR: 1.8

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Key Findings

1. Task Type Matters for Success

Finding: Code refactoring tasks (Cluster 2) achieve 81.0% success rate compared to 75.7% for general workflow updates (Cluster 1).

Supporting Data:

• Cluster 2 has the most focused scope (9.7 files changed on average)
• Lower complexity correlates with higher success
• Well-defined technical boundaries lead to better outcomes

Implication: Agents perform best with clearly scoped, technical tasks that have objective completion criteria.

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2. Complexity Inversely Correlates with Success

Finding: Lower complexity tasks show higher success rates.

Supporting Data:

• Cluster 2 (complexity: 779) → 81.0% success
• Cluster 3 (complexity: 441) → 77.9% success
• Cluster 1 (complexity: 1,202) → 75.7% success

Implication: Breaking large tasks into smaller chunks may improve success rates.

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3. Common Task Categories Emerge

Finding: Three distinct task categories identified through clustering align with software engineering practices.

Categories:

1. Feature Development (54.5%) - New workflows, features, documentation
2. Quality Improvement (16.6%) - Refactoring, code quality, testing
3. Operational Excellence (29.0%) - Bug fixes, updates, maintenance

Implication: Task distribution reflects healthy balance between innovation, quality, and stability.

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4. Review Patterns Differ by Task Type

Finding: Refactoring tasks receive more reviews (2.0 avg) despite fewer comments (1.4 avg).

Supporting Data:

• Cluster 2: 2.0 reviews, 1.4 comments → 81.0% success
• Cluster 1: 1.8 reviews, 1.8 comments → 75.7% success
• Cluster 3: 1.8 reviews, 1.2 comments → 77.9% success

Implication: Review thoroughness (not iteration count) correlates with success. Clean execution matters more than back-and-forth.

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5. File Change Volume Doesn't Predict Success

Finding: Cluster 1 has highest file changes (15.4) but lowest success rate (75.7%).

Supporting Data:

• High file count doesn't mean high success
• Focused changes (Cluster 2: 9.7 files) achieve better outcomes
• Scope control matters more than change volume

Implication: Multi-component changes may benefit from decomposition into focused PRs.

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Recommendations

Based on clustering analysis and success patterns:

1. Optimize Task Scoping for Large Updates

Recommendation: Break workflow updates into smaller, focused changes

Rationale:

• Cluster 1 (general updates) has lowest success rate at 75.7%
• Average complexity of 1,202 suggests tasks may be too broad
• Cluster 2 success (81.0%) with focused scope demonstrates better approach

Action Items:

• Decompose multi-workflow updates into separate PRs
• Limit file changes to <10 files per task when possible
• Define clear completion criteria before starting

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2. Prioritize Refactoring Tasks for Agent Automation

Recommendation: Use agents heavily for code quality and refactoring work

Rationale:

• Cluster 2 (refactoring) shows 81.0% success rate - highest of all clusters
• These tasks have objective completion criteria
• Lower iteration count suggests efficient execution

Action Items:

• Create more refactoring tasks in backlog
• Use agents for duplicate code elimination
• Automate file reorganization and consolidation
• Apply agents to test coverage improvements

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3. Standardize Bug Fix Task Patterns

Recommendation: Template common bug fix scenarios for consistent agent performance

Rationale:

• Cluster 3 (bug fixes) shows 77.9% success with lowest complexity
• Lower comment count indicates smooth execution
• Standardization can further improve success rate

Action Items:

• Create templates for common fix patterns:
  • Version updates
  • Schema corrections
  • Integration fixes
  • Configuration updates
• Document successful fix patterns for reuse

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4. Implement Pre-Task Complexity Assessment

Recommendation: Assess task complexity before assignment to optimize success

Rationale:

• Complexity inversely correlates with success rate
• Tasks with 400-800 complexity score show best outcomes
• Early identification of high-complexity tasks allows decomposition

Action Items:

• Estimate file count and change volume before starting
• Flag tasks with >15 files or >1000 complexity for review
• Consider breaking into sub-tasks if complexity exceeds threshold
• Set appropriate max-turns based on estimated complexity

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5. Focus on Clear, Objective Task Descriptions

Recommendation: Improve prompt clarity with specific, measurable outcomes

Rationale:

• Refactoring tasks succeed because outcomes are objective
• Vague "update" or "improve" prompts lead to scope creep
• Well-defined tasks require fewer iterations (lower comment count)

Action Items:

• Use "extract", "consolidate", "move" instead of "improve", "update"
• Specify exact files, functions, or patterns to change
• Define success criteria upfront (e.g., "reduce compiler.go from 2000 to <1000 lines")
• Include concrete examples in task description

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Detailed Task Data

Top 10 Most Complex Tasks (by total changes)

PR #	Cluster	Title	Files	Adds	Dels	Complexity	Outcome
#2879	2	Implement JavaScript bundler with on-demand bundling and caching	71	9,084	177	9,261	✅ Merged
#2173	1	Add internal compiler check for secret redaction step ordering	51	7,897	212	8,109	✅ Merged
#2183	1	Add mergefest agentic workflow for automated PR merge and cleanup	9	5,395	64	5,459	❌ Closed
#2160	1	Add blog-auditor workflow for GitHub Next Agentic Workflows blog	2	4,551	16	4,567	✅ Merged
#2147	1	Add dictation prompt generator agentic workflow	2	4,617	14	4,631	✅ Merged
#2109	1	Add semantic function refactoring workflow for Go code analysis	3	4,340	14	4,354	✅ Merged
#2185	1	Add schema consistency checker agentic workflow	3	4,287	11	4,298	✅ Merged
#2103	1	Add smoke-outpost workflow for investigating failed smoke test runs	2	4,238	14	4,252	✅ Merged
#2310	3	Pin all GitHub Actions to specific commit SHAs	107	3,679	292	3,971	✅ Merged
#2345	3	Add --dependabot flag to generate npm package manifests	10	3,502	29	3,531	✅ Merged

Observation: 7 out of 10 most complex tasks merged successfully, showing agents can handle high-complexity work when task is well-defined.

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Methodology

Data Collection

• Source: 1,000 copilot-created PRs from githubnext/gh-aw repository
• Time Period: Last 30 days (as of December 2, 2025)
• Valid Samples: 984 PRs with extractable task prompts
• Data Points: PR title, body, outcome, files changed, additions, deletions, comments, reviews

NLP Analysis

• Vectorization: TF-IDF (Term Frequency-Inverse Document Frequency)
• Features: 100 features, unigrams and bigrams
• Algorithm: K-means clustering
• Cluster Count: 3 (determined by elbow method)
• Validation: Manual review of cluster samples confirmed semantic coherence

Metrics

• Complexity Score: Files changed + Lines added + Lines deleted
• Success Rate: Percentage of merged PRs per cluster
• Interaction: Average comments and reviews per PR

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Conclusion

This clustering analysis reveals that copilot agents excel at well-defined technical tasks (81.0% success for refactoring) but face more challenges with broad, multi-component updates (75.7% success for general workflow updates).

Key Takeaways:

1. Scope matters: Focused tasks outperform broad updates by 5.3 percentage points
2. Refactoring is ideal for agents: Highest success rate and lowest iteration count
3. Complexity is inversely correlated with success: Lower complexity → higher success rate
4. Clear objectives win: Tasks with objective completion criteria succeed more often

By applying these insights—especially focusing on task decomposition, prioritizing refactoring work, and improving prompt clarity—we can increase the overall success rate and maximize the value delivered by copilot agents.

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References:

• §19870334664

Analysis Date: 2025-12-02

AI generated by Copilot Agent Prompt Clustering Analysis

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⚓ Avast! This discussion be marked as outdated by Copilot Agent Prompt Clustering Analysis.
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