| 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 analyzeorgId (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-pr-performance 558 04bd288b-595a-4ee1-87cd-8bbbdc312b3c --env=.env.prod
/analyze-pr-performance 723 97442318-9d2a-496b-a0d2-b45fb --days=14 --env=.env.prod
/analyze-pr-performance 701 97442318-9d2a-496b-a0d2-b45fb --legacy --env=.env.prod
What it analyzes
-
Pipeline identification: Finds the pipelineId and correlationId for the PR
-
Stage times: Shows duration of each pipeline stage:
- ValidateNewCommitsStage
- ResolveConfigStage
- FetchChangedFilesStage
- PRLevelReviewStage
- FileAnalysisStage
- CreateFileCommentsStage
- UpdateCommentsAndGenerateSummaryStage
- And all other stages...
-
LLM calls: Details of each LLM operation:
- Operation name (analyzeCodeWithAI, selectReviewMode, kodyRulesAnalyzeCodeWithAI, etc.)
- Duration
- Model used
- Token counts (input/output)
-
Summary metrics:
- Total pipeline duration
- Total LLM calls count
- Total tokens (input/output)
- Slow calls count (> 60s)
- Models used
-
Bottlenecks: Highlights stages and LLM calls taking > 60 seconds
-
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
