Can it help with database optimization?

Yes, it includes guidance on query optimization, indexing, N+1 problem resolution, and connection management to improve database throughput.

Does it support automated benchmarking?

Yes, it provides patterns for using tools like Criterion for Rust and the Performance API for TypeScript to create repeatable, high-precision benchmarks.

What is the 'Iron Law' of performance engineering?

The Iron Law states: 'No optimization without measurement.' This prevents wasting time on 'perceived' bottlenecks and ensures every change is backed by profiling data.

What programming languages does this skill support?

While the core methodology applies to any language, the skill includes specific implementation patterns and tooling guidance for TypeScript, Bun, and Rust.

How does this skill handle tail latency?

It provides specific frameworks for measuring and optimizing P95 and P99 metrics, helping engineers address slow outliers that impact user experience.

Performance Engineering

Name: Performance Engineering
Author: outfitter-dev

byoutfitter-dev

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Analíticas y Monitorización

Optimizes software performance through systematic profiling, benchmarking, and evidence-based engineering workflows.

Acerca de

The Performance Engineering skill provides a rigorous framework for identifying and resolving code bottlenecks in TypeScript, Bun, and Rust environments. It enforces the 'Iron Law' of performance—no optimization without measurement—guiding Claude through a structured five-phase workflow: establishing baselines, profiling, analyzing root causes, implementing targeted fixes, and validating improvements. Whether you are debugging memory leaks, reducing tail latency (P99), or optimizing database queries, this skill ensures that performance gains are data-driven, statistically significant, and free of regressions.

Características Principales

Systematic validation to ensure no performance regressions
16 GitHub stars
Comprehensive profiling patterns for TypeScript, Bun, and Rust
Advanced metric tracking for P99 latency, throughput, and CPU/memory usage
Database query optimization and I/O batching strategies
Evidence-based optimization workflow (Measure → Profile → Optimize → Validate)

Casos de Uso

Benchmarking competing implementations to select the most efficient algorithm
Reducing tail latency for critical API endpoints and high-frequency operations
Identifying and fixing memory leaks or excessive heap allocations in production code

Acerca de

Características Principales

Systematic validation to ensure no performance regressions
16 GitHub stars
Comprehensive profiling patterns for TypeScript, Bun, and Rust
Advanced metric tracking for P99 latency, throughput, and CPU/memory usage
Database query optimization and I/O batching strategies
Evidence-based optimization workflow (Measure → Profile → Optimize → Validate)

Casos de Uso

Benchmarking competing implementations to select the most efficient algorithm
Reducing tail latency for critical API endpoints and high-frequency operations
Identifying and fixing memory leaks or excessive heap allocations in production code