What metrics does this skill track?

It measures setup time, execution time, integration overhead, parallel efficiency percentages, and resource utilization (CPU/RAM) to prevent system swapping and slowdowns.

How does the Performance Optimizer speed up Claude Code?

It identifies sequential bottlenecks in the setup phase—like issue and worktree creation—and suggests parallel patterns that allow subagents to initialize concurrently, reducing setup time from O(n) to O(1).

Can this skill help reduce my AI token costs?

Yes, it provides ROI analysis and implementation strategies for modular context engineering that can result in 95% fewer tokens and up to an 81% cost reduction by leveraging specialized agent roles.

What is the 'Parallel Setup Pattern'?

It is an optimized workflow where each subagent is responsible for creating its own GitHub issue and worktree simultaneously, rather than the main agent performing these tasks one after another.

Performance Optimizer

Name: Performance Optimizer
Author: Shakes-tzd

byShakes-tzd

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Productivity & Workflow

Analyzes and optimizes parallel development workflows to maximize execution speed and minimize operational costs.

About

The Performance Optimizer skill is a specialized diagnostic and tuning tool designed for high-concurrency development environments using Claude Code. It identifies systemic bottlenecks—such as sequential setup phases, resource contention, and hidden dependencies—and provides actionable implementation patterns to reach theoretical maximum speedups. By applying Amdahl's Law analysis and precise benchmarking, it enables developers to scale workflows efficiently, achieve up to 95% token savings through modular context engineering, and significantly reduce costs using multi-model ROI analysis.

Key Features

Amdahl's Law analysis to measure parallelization efficiency
Automated benchmarking scripts for workflow timing and ROI
Cost-optimization strategies comparing Haiku and Sonnet performance
1 GitHub stars
Bottleneck identification for parallel issue and worktree creation
Resource utilization monitoring for CPU, RAM, and Disk I/O

Use Cases

Debugging slow execution in multi-agent parallel workflows
Optimizing resource allocation for high-concurrency tasks
Scaling repository-wide transformations with minimum token overhead

About

Key Features

Amdahl's Law analysis to measure parallelization efficiency
Automated benchmarking scripts for workflow timing and ROI
Cost-optimization strategies comparing Haiku and Sonnet performance
1 GitHub stars
Bottleneck identification for parallel issue and worktree creation
Resource utilization monitoring for CPU, RAM, and Disk I/O

Use Cases

Debugging slow execution in multi-agent parallel workflows
Optimizing resource allocation for high-concurrency tasks
Scaling repository-wide transformations with minimum token overhead