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performance-reviewer

Identify performance bottlenecks in cross-language codebases using profiling outputs.

3|Updated Feb 26, 2026
Search Tags:#performance#metrics#profiling#bottlenecks#code-review#pprof#explain-analyze
One-click install
npx skills add https://github.com/ralvarezdev/ralvaskills --skill performance-reviewer-ralvarezdev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: performance-reviewer
Source: https://github.com/ralvarezdev/ralvaskills/tree/main/skills/quality/performance-reviewer
Command: npx skills add https://github.com/ralvarezdev/ralvaskills --skill performance-reviewer-ralvarezdev

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Performance bottlenecks across code paths are difficult to detect and quantify without measurements, leading to slow endpoints and unreliable metrics.

Core Features & Use Cases

  • Measurement-driven findings: anchor every finding to concrete outputs like EXPLAIN ANALYZE, pprof, py-spy, or runtime metrics.
  • Cross-language applicability: supports Go, Python, and other stacks to identify N+1, missing indexes, blocking I/O in async, and memory hotspots.
  • Code-review integration: fits into PR reviews to deliver actionable, measurable improvements.

Quick Start

Run a profiling pass on the target endpoint and summarize the top bottlenecks across the critical path.

Frequently Asked Questions about performance-reviewer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I identify performance bottlenecks using profiling outputs like pprof and EXPLAIN ANALYZE?▼

To review performance bottlenecks, run a profiling pass using tools like pprof, py-spy, or EXPLAIN ANALYZE on target endpoints. Summarize the top bottlenecks across the critical path to anchor findings in measured outputs.

What is a measurement-driven performance review and how does it work?▼

A measurement-driven performance review anchors every finding to concrete profiling outputs or runtime metrics. Instead of guessing, it uses data from EXPLAIN ANALYZE or pprof to quantify slow endpoints and propose measurable fixes with re-measurement.

Can I integrate performance profiling into PR code reviews for cross-language codebases?▼

Yes, performance profiling integrates into PR code reviews to deliver actionable, measurable improvements. It supports cross-language applicability across Go, Python, and other stacks to identify blocking I/O in async, N+1 queries, and memory hotspots.

Does performance review work with both Go and Python to find blocking I/O in async code?▼

Yes, performance review supports cross-language applicability for Go, Python, and other stacks. It specifically targets blocking I/O in async operations, N+1 queries, missing indexes, and memory hotspots using profiling outputs from pprof and py-spy.

Why do I need measured outputs to detect N+1 queries and memory hotspots?▼

Measured outputs are needed because performance bottlenecks across code paths are difficult to detect and quantify without measurements. Relying on profiling data prevents speculative fixes and ensures endpoints are evaluated using reliable runtime metrics.

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