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claude-codedevops

Claude-skill-registry analyze-pr-performance

Analyze code review pipeline performance for a specific PR. Use when investigating slow PRs, identifying bottlenecks, or debugging performance issues in code reviews.

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/analyze-pr-performance" ~/.claude/skills/majiayu000-claude-skill-registry-analyze-pr-performance && rm -rf "$T"
manifest: skills/data/analyze-pr-performance/SKILL.md
tags
#pr-analysis#pipeline-performance#code-review#llm-metrics#performance-debugging
safety · automated scan (low risk)
This is a pattern-based risk scan, not a security review. Our crawler flagged:
  • references .env files
Always read a skill's source content before installing. Patterns alone don't mean the skill is malicious — but they warrant attention.
source content

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