Git Tools Get Pr Review

Bismillah! I'll retrieve and analyze the PR/MR review feedback to help you improve your code based on the reviewer's suggestions.

Arguments: [PR_NUMBER_OR_URL] [--github|--gitlab]

Platform Selection

Flags:

• --github - Fetch PR review from GitHub using gh CLI (default)
• --gitlab - Fetch MR review from GitLab using glab CLI

Auto-Detection (default):

• URLs containing github.com → GitHub
• URLs containing gitlab.com → GitLab
• Plain numbers → GitHub (default)

Platform Detection Logic

# Parse platform flag or auto-detect from URL
ARGS="<PR number or URL and platform flags supplied when invoking this skill>"
if [[ "$ARGS" == *"--gitlab"* ]]; then
    PLATFORM="gitlab"
    CLI_TOOL="glab"
    REVIEW_TYPE="merge request"
elif [[ "$ARGS" == *"--github"* ]]; then
    PLATFORM="github"
    CLI_TOOL="gh"
    REVIEW_TYPE="pull request"
elif [[ "$ARGS" == *"gitlab.com"* ]]; then
    PLATFORM="gitlab"
    CLI_TOOL="glab"
    REVIEW_TYPE="merge request"
elif [[ "$ARGS" == *"github.com"* ]]; then
    PLATFORM="github"
    CLI_TOOL="gh"
    REVIEW_TYPE="pull request"
else
    # Default to GitHub for plain numbers
    PLATFORM="github"
    CLI_TOOL="gh"
    REVIEW_TYPE="pull request"
fi

echo "📌 Platform: $PLATFORM ($REVIEW_TYPE review)"

Using with Extended Reasoning

If an extended-reasoning mode is available in your environment, use it for this task. When running with extended reasoning, I will perform an exceptionally deep and comprehensive analysis:

Advanced Review Analysis

• Deep Context Understanding: Analyze the entire codebase context, not just the PR changes
• Cross-Reference Analysis: Connect reviewer feedback to broader architectural patterns and project conventions
• Hidden Issue Detection: Identify potential issues that reviewers might have missed
• Performance Impact Assessment: Evaluate performance implications of suggested changes
• Security Review: Conduct security analysis beyond basic review comments
• Maintainability Evaluation: Assess long-term code maintainability implications

Reviewer Psychology & Communication

• Intent Interpretation: Deeply analyze the underlying intent behind each reviewer comment
• Communication Style Analysis: Understand reviewer preferences and communication patterns
• Priority Inference: Determine true urgency levels beyond surface-level feedback
• Relationship Dynamics: Consider team dynamics and reviewer expertise areas

Strategic Implementation Planning

• Multi-Level Solution Analysis: Explore multiple approaches for each piece of feedback
• Dependency Mapping: Create detailed dependency graphs for implementation order
• Risk Assessment Matrix: Evaluate risks and benefits of each potential change
• Future-Proofing: Consider how changes will affect future development and scalability

Advanced Code Quality Enhancement

• Pattern Recognition: Identify recurring patterns in feedback for systematic improvements
• Best Practice Synthesis: Combine reviewer suggestions with industry best practices
• Optimization Opportunities: Find performance and efficiency improvements beyond the review scope
• Technical Debt Analysis: Evaluate and address technical debt highlighted by review feedback

This intensive analysis ensures not just addressing review feedback, but elevating the entire codebase quality and team collaboration effectiveness.

Let me:

1. Identify and fetch the PR/MR review

   • Parse PR/MR number or URL from arguments or detect current branch's PR/MR
   • Use appropriate CLI based on platform:
     • GitHub: gh pr view <number> --json reviews,comments
     • GitLab: glab mr view <number> and glab api for discussions
   • Retrieve all review threads, suggestions, and approval status

2. Analyze the review feedback comprehensively

   • Identify automated/bot reviews vs. human reviews for specialized handling
   • Categorize feedback by type (bugs, improvements, style, architecture, security)
   • Distinguish between critical issues that must be addressed vs. suggestions
   • Extract specific code locations and recommended changes with line numbers
   • Assess overall review sentiment and approval likelihood
   • Parse the automated reviewer's structured feedback format and recommendations

3. Create actionable improvement plan

   • Prioritize feedback items by impact and effort required
   • Generate specific code changes needed for each comment
   • Suggest commit organization for addressing feedback
   • Identify any breaking changes or architectural considerations

4. Provide clear recommendations

   • If review is positive: Highlight what's working well, celebrate good practices, note minor improvements
   • If the automated reviewer found the code satisfactory: Summarize positive aspects and readiness for merge
   • If review needs work: Create step-by-step action items with specific code examples
   • For automated-reviewer feedback: Interpret AI suggestions in context of best practices
   • Suggest response strategy for reviewer comments and how to address concerns
   • Estimate time and complexity for implementing each change
   • Recommend whether to request re-review after changes

5. Generate implementation roadmap

   • Break down changes into logical, atomic commits with clear messages
   • Suggest order of implementation to minimize conflicts and build failures
   • Identify dependencies between different feedback items
   • Provide templates for updating PR description with changes made
   • Include testing strategy for validating fixes
   • Suggest documentation updates if needed

6. Execute and verify improvements

   • Apply high-priority fixes immediately where possible
   • Run existing tests to ensure no regressions
   • Validate that feedback has been properly addressed
   • Prepare summary of changes made for reviewer response

7. Commit and push improvements

   • Stage all implemented changes with git add .
   • Create descriptive commit message summarizing the addressed feedback
   • Use format: "address PR feedback: [specific changes made]"
   • Push changes to remote branch to update the PR
   • Ensure all improvements are visible to reviewers before requesting re-review

8. Conditional re-review request (SMART EXECUTION)

   IF changes were made AND $REVIEWER_BOT is set (a GitHub/GitLab user or app that reviews PRs):

   • Post re-review request comment using appropriate CLI
   • Use exact format: "@$REVIEWER_BOT I've addressed the review feedback. Please re-review the changes:"
   • Include specific changes made with file:line references
   • List all modified files
   • End with "Ready for re-review ✅"
   • Verify comment was posted successfully

   IF changes were made but $REVIEWER_BOT is NOT set:

   • Report completion to the user only - NO PR/MR comment needed
   • Provide a summary of the changes made

   IF NO changes were made:

   • Report to user only - NO PR/MR comment needed
   • Provide summary: "Analysis complete: No improvements required"
   • Explain why no changes were needed (already implemented, meets standards, etc.)
   • Confirm PR/MR is ready for merge
   • Note: "No re-review request posted (no changes made)"

   Implementation Logic:

   # Track if any changes were made during the process
   CHANGES_MADE=false
   
   # After each improvement attempt, check for actual changes
   if ! git diff --quiet HEAD~1 HEAD; then
       CHANGES_MADE=true
   fi
   
   # Final decision - only post a re-review comment if changes exist AND a reviewer bot is configured
   if [ "$CHANGES_MADE" = true ] && [ -n "${REVIEWER_BOT:-}" ]; then
       if [ "$PLATFORM" = "github" ]; then
           gh pr comment $PR_NUMBER --body "@$REVIEWER_BOT I've addressed the review feedback..."
       else
           glab mr note $MR_NUMBER --message "@$REVIEWER_BOT I've addressed the review feedback..."
       fi
   else
       echo "✅ Analysis complete - reporting to user only"
   fi
   

9. Provide suggested improvements

   • Based on deep analysis, present additional code quality improvements beyond the review feedback
   • Suggest architectural enhancements, performance optimizations, or maintainability improvements
   • Recommend best practices that could elevate the code further
   • Offer insights on patterns, testing strategies, or documentation that could benefit the codebase

Usage Examples

# Get GitHub PR review (default)
Invoke with: git-tools-get-pr-review 42

# Get GitHub PR review (explicit)
Invoke with: git-tools-get-pr-review 123 --github

# Get GitLab MR review
Invoke with: git-tools-get-pr-review 45 --gitlab

# Auto-detect from GitHub URL
Invoke with: git-tools-get-pr-review https://github.com/owner/repo/pull/15

# Auto-detect from GitLab URL
Invoke with: git-tools-get-pr-review https://gitlab.com/owner/repo/-/merge_requests/45

Alhamdulillah, let me fetch and analyze the review feedback to help you create an excellent PR/MR!