[prompt-analysis] 🤖 Copilot PR Prompt Pattern Analysis - 2025-11-20

[github-actions[bot]]

🤖 Copilot PR Prompt Pattern Analysis - 2025-11-20

This report analyzes 1,000 Copilot-generated PRs from the last 30 days to identify which prompt patterns lead to successful merges versus closed PRs.

Summary

Analysis Period: Last 30 days
Total PRs: 1,000 | Merged: 766 (76.6%) | Closed: 233 (23.3%) | Open: 1

Key Finding: Overall, 76.6% of Copilot PRs are successfully merged, indicating strong effectiveness of the Copilot coding agent when properly prompted.

Full Analysis Details

Prompt Categories and Success Rates

Category	Total	Merged	Success Rate
Documentation	455	360	79.1% ⭐
Testing	395	308	78.0%
Update/Modify	536	420	78.4%
Bug Fix	530	413	77.9%
Refactoring	216	166	76.9%
Feature Addition	551	422	76.6%
Other	83	62	74.7%

Insight: Documentation and testing prompts have the highest success rates, suggesting these are well-defined tasks that Copilot handles effectively.

Prompt Characteristics Analysis

✅ Successful Prompt Patterns

Common characteristics in merged PRs:

• Average prompt length: 174.6 words (median: 59 words)
• URL/Link inclusion: 48.8% include GitHub issue/run links for context
• File references: 49.2% reference specific files or extensions
• Imperative start: 33.6% start with clear action verbs (fix, add, update, etc.)

Most effective keywords in merged PRs:

• issue, workflow, github - Context references
• update, add, section - Clear actions
• command, files, description - Specific targets

Example successful prompts:

1. Short & Specific with Context (PR Fix broken documentation links in troubleshooting and how-it-works pages #4374) → MERGED

   Fix docs broken links https://github.com/githubnext/gh-aw/actions/runs/19522662827/job/55889074029
   

   • Clear action: "Fix"
   • Specific target: "docs broken links"
   • Context: GitHub Actions run URL

2. Clear Imperative (PR Fix test isolation in collect_ndjson_output.test.cjs #4365) → MERGED

   Fix javascript tests
   

   • Simple, direct action
   • Specific scope: JavaScript tests

3. Detailed with Issue Context (Multiple examples) → MERGED

   • Include issue title and description
   • Provide background and objectives
   • Reference specific files or components

❌ Unsuccessful Prompt Patterns

Common characteristics in closed PRs:

• Average prompt length: 176.5 words (median: 62 words) - Similar to merged!
• URL/Link inclusion: 42.3% - Slightly lower than merged PRs
• File references: 39.9% - Notably lower (49.2% for merged)
• Imperative start: 30.8% - Slightly lower clarity

Keywords more common in closed PRs:

• Technical specifics that may be overly complex
• Less clear action verbs
• Broader, less focused objectives

Example unsuccessful prompts:

1. Vague/Incomplete (PR [WIP] Remove common keywords and phrases from analysis #4346) → CLOSED

   On copilot-pr-prompt-analysis.md, ignore common keywords and phrases in copilot-pr-prompt-analysis
   

   • Unclear action ("ignore" is ambiguous in code context)
   • Lacks specificity about what to do

2. Potentially Too Complex (PR [WIP] Skip conclusion job if agent job is cancelled #4370) → CLOSED

   Make sure that the conclusion job is skipped if the agent job is cancelled
   

   • Multiple conditions and dependencies
   • May require manual workflow expertise

Key Insights

📊 Pattern 1: Context is King

• Prompts with GitHub links (issue/run URLs) have 48.8% inclusion in merged PRs vs 42.3% in closed
• Including specific context helps Copilot understand the full picture

📏 Pattern 2: File References Matter

• 49.2% of merged PRs reference specific files vs 39.9% of closed PRs
• Being specific about what files to modify increases success by ~10%

🎯 Pattern 3: Prompt Length is Neutral

• Both merged and closed PRs average ~175 words
• Quality over quantity: Specificity matters more than length
• Short prompts (< 10 words) work well when clear: 8.8% merged vs 6.2% closed

🔧 Pattern 4: Documentation & Testing Excel

• Highest success rates at 79.1% and 78.0% respectively
• These tasks are well-defined and less ambiguous
• Clear acceptance criteria built into the task nature

Recommendations

Based on the analysis of 1,000 Copilot PRs:

✅ DO: Include Context Links

Recommendation: Always include GitHub issue or workflow run URLs when relevant

• Why: 48.8% of merged PRs include context links vs 42.3% of closed
• Example: Fix docs broken links https://github.com/org/repo/actions/runs/12345

✅ DO: Reference Specific Files

Recommendation: Mention specific files, paths, or file extensions when possible

• Why: 49.2% of merged PRs reference files vs 39.9% of closed
• Example: Update the authentication logic in auth.js to handle token refresh

✅ DO: Use Clear Imperative Verbs

Recommendation: Start with action verbs: fix, add, update, remove, implement

• Why: 33.6% of merged PRs vs 30.8% of closed use imperative starts
• Example: Fix javascript tests instead of The tests need fixing

✅ DO: Be Specific About Scope

Recommendation: Define clear boundaries for the task

• Why: Focused tasks (docs, tests) have higher success rates (78-79%)
• Example: Add error handling to the API client not Improve error handling

❌ AVOID: Vague Instructions

Recommendation: Don't use ambiguous verbs like "improve", "enhance", "optimize" without specifics

• Why: These require judgment calls and may not align with expectations
• Example: BAD: Improve the code | GOOD: Refactor the parser to use switch statements

❌ AVOID: Multiple Complex Dependencies

Recommendation: Break down tasks with multiple conditions into simpler prompts

• Why: Complex workflow logic often requires human judgment
• Example: Instead of conditional workflow changes, specify exact desired behavior

💡 IDEAL PROMPT TEMPLATE

[Action Verb] [Specific Target] [Context Link]

Examples:
✅ Fix broken links in documentation https://github.com/org/repo/issues/123
✅ Add error handling to src/api/client.js for network timeouts
✅ Update test coverage for authentication module to include edge cases
✅ Remove deprecated API calls from services/legacy.ts

Statistical Highlights

• 76.6% overall merge rate - High success indicator
• Documentation tasks: 79.1% success (best category)
• Testing tasks: 78.0% success (second best)
• Feature additions: 76.6% success (matches overall average)
• Prompts with GitHub links: ~6% higher success rate
• Prompts with file references: ~9% higher success rate

Conclusion

The analysis reveals that Copilot coding agent is highly effective with a 76.6% merge rate. Success is strongly correlated with:

1. Specific context (GitHub links, file references)
2. Clear actions (imperative verbs, defined scope)
3. Well-defined tasks (documentation, testing excel)

Developers can improve success rates by providing specific context, referencing exact files, and using clear imperative instructions rather than vague improvement requests.

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Analysis Period: 2025-11-20 (Last 30 days)
Data Source: 1,000 Copilot-generated PRs from githubnext/gh-aw

AI generated by Copilot PR Prompt Pattern Analysis

[github-actions[bot]]

This discussion was automatically closed because it was created by an agentic workflow more than 1 week ago.