How are findings prioritized?

Findings are scored on a 2x2 matrix of Impact vs. Effort, helping you identify 'Quick Wins' versus complex architectural changes that require planning.

Does it support LiveView optimization?

Absolutely. It analyzes LiveViews for common pitfalls like missing streams for large lists, large assigns that grow unbounded, and blocking handle_info processing.

What is the 'Iron Laws' philosophy?

The skill adheres to three rules: always measure before optimizing, prioritize database issues (where 90% of bottlenecks live), and implement one change at a time to isolate impact.

Can it run real database benchmarks?

Yes, when Tidewave MCP is configured, it can execute EXPLAIN ANALYZE on suspicious queries to provide real-world performance metrics directly from your database.

How does this skill detect N+1 queries?

It scans your Ecto schemas and context modules for Repo calls inside loops, Enum.map calls wrapping database access, or missing preloads in common query patterns.

Phoenix Performance Optimizer

Name: Phoenix Performance Optimizer
Author: oliver-kriska

byoliver-kriska

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Analytics & Monitoring

Analyzes Elixir and Phoenix applications to identify and resolve performance bottlenecks, N+1 queries, and memory leaks.

The Phoenix Performance Optimizer is a specialized diagnostic tool for Elixir developers that automates the detection of common performance issues across Ecto queries, LiveView memory usage, and OTP GenServer bottlenecks. By leveraging parallel analysis tracks and an impact-effort prioritization matrix, it helps developers pinpoint high-impact optimizations—such as missing preloads or inefficient stream patterns—before deployment. The skill ensures high-concurrency applications remain responsive and resource-efficient by applying 'Iron Laws' of performance, including database-first optimization and empirical measurement.

Key Features

01Identifies LiveView memory leaks and inefficient assign patterns

02Integrates with Tidewave MCP for live EXPLAIN ANALYZE and process inspection

0362 GitHub stars

04Analyzes OTP GenServer mailbox growth and synchronous bottlenecks

05Detects N+1 queries and missing database indexes in Ecto layers

06Prioritizes findings using an Impact vs. Effort matrix for actionable fixes

Use Cases

01Debugging memory growth and state management in complex LiveView components

02Optimizing data fetching patterns before high-traffic production deployments

03Resolving slow response times or database timeouts in Phoenix applications

Key Features

01Identifies LiveView memory leaks and inefficient assign patterns

02Integrates with Tidewave MCP for live EXPLAIN ANALYZE and process inspection

0362 GitHub stars

04Analyzes OTP GenServer mailbox growth and synchronous bottlenecks

05Detects N+1 queries and missing database indexes in Ecto layers

06Prioritizes findings using an Impact vs. Effort matrix for actionable fixes

Use Cases

01Debugging memory growth and state management in complex LiveView components

02Optimizing data fetching patterns before high-traffic production deployments

03Resolving slow response times or database timeouts in Phoenix applications