analyze-pr-performance

name	analyze-pr-performance
description	Analyze code review pipeline performance for a specific PR. Use when investigating slow PRs, identifying bottlenecks, or debugging performance issues in code reviews.
argument-hint	<prNumber> <orgId> [--days=N] [--legacy] [--env=PATH]

Analyze PR Performance

Analyze code review pipeline performance for a specific PR.

Usage

Run the analyze-pr-performance CLI script with the provided arguments:

npx ts-node scripts/analyze-pr-performance.cli.ts $ARGUMENTS

Arguments

• prNumber (required): The PR number to analyze
• orgId (required): The organization ID

Options

• --days=N: Number of days to search back (default: 7)
• --legacy: Also search in legacy collection (observability_logs)
• --env=PATH: Path to .env file (e.g., --env=.env.prod)

Examples

# Analyze performance for PR #558 in production
/analyze-pr-performance 558 04bd288b-595a-4ee1-87cd-8bbbdc312b3c --env=.env.prod

# Analyze with extended date range
/analyze-pr-performance 723 97442318-9d2a-496b-a0d2-b45fb --days=14 --env=.env.prod

# Analyze with legacy logs included
/analyze-pr-performance 701 97442318-9d2a-496b-a0d2-b45fb --legacy --env=.env.prod

What it analyzes

1. Pipeline identification: Finds the pipelineId and correlationId for the PR

2. Stage times: Shows duration of each pipeline stage:

   • ValidateNewCommitsStage
   • ResolveConfigStage
   • FetchChangedFilesStage
   • PRLevelReviewStage
   • FileAnalysisStage
   • CreateFileCommentsStage
   • UpdateCommentsAndGenerateSummaryStage
   • And all other stages...

3. LLM calls: Details of each LLM operation:

   • Operation name (analyzeCodeWithAI, selectReviewMode, kodyRulesAnalyzeCodeWithAI, etc.)
   • Duration
   • Model used
   • Token counts (input/output)

4. Summary metrics:

   • Total pipeline duration
   • Total LLM calls count
   • Total tokens (input/output)
   • Slow calls count (> 60s)
   • Models used

5. Bottlenecks: Highlights stages and LLM calls taking > 60 seconds

6. Pipeline status: Whether the pipeline completed, failed, or is unknown

Output

The script outputs:

• Pipeline and correlation IDs
• Organization and repository info
• Stage times table with duration and percentage of total
• LLM calls table with model, tokens, and duration
• Summary metrics
• Bottleneck list (stages and LLM calls > 60s)
• Final pipeline status

How to Respond

• Identify the slowest stages and explain why they might be slow
• Look for LLM calls that are taking too long (> 2 minutes is concerning)
• Check if multiple slow LLM calls are running sequentially vs in parallel
• Note any patterns (e.g., all selectReviewMode calls are slow = possible model issue)
• Suggest potential optimizations if bottlenecks are clear
• If FileAnalysisStage is slow, it's usually due to many files or large files being analyzed
• If PRLevelReviewStage is slow, check the KodyRules and PR-level analysis calls
• Compare token counts to durations - high tokens with proportional time is expected, low tokens with high time indicates API latency issues
• Report the pipeline status and check if it completed successfully