How does this skill reduce Claude API costs?

It implements strategies such as using more affordable models like Sonnet for standard tasks, limiting the context window size, and utilizing local embedding models to minimize external API calls.

What is the benefit of parallel execution in Auto-Claude?

By increasing the MAX_PARALLEL_AGENTS setting, the system can spawn multiple subagents to handle frontend, backend, and testing tasks simultaneously, reducing the overall time to completion.

Can I use local models for memory with this skill?

Yes, the skill provides configurations for using Ollama as a local provider for both LLM and embedding tasks, which can significantly reduce costs and improve privacy.

When should I use Claude Opus versus Claude Sonnet?

The skill recommends using Opus for complex features and initial specification creation where high reasoning is critical, while switching to Sonnet for standard coding tasks and rapid iterations to save time and money.

Auto-Claude Performance Optimizer

Name: Auto-Claude Performance Optimizer
Author: adaptationio

byadaptationio

Productivity & Workflow

Optimizes Claude agent performance and manages API costs through strategic model selection and token efficiency.

About

Auto-Claude Performance Optimization provides a comprehensive framework for tuning the efficiency of Claude-based autonomous agents. It enables developers to significantly reduce API costs by balancing model usage between high-reasoning and high-speed variants, streamlining context windows, and implementing parallel execution. Whether you are looking to accelerate build iterations, minimize token overhead, or optimize memory embeddings using local providers like Ollama, this skill provides the configuration patterns necessary to maximize agent productivity while maintaining strict budget control.

Key Features

Memory system optimization supporting OpenAI and local Ollama embedding models
0 GitHub stars
Automated cost tracking and usage reporting for better budget management
Strategic model routing between Claude Opus and Sonnet based on task complexity
Granular token management including context window limits and thinking budget overrides
Build speed acceleration via parallel agent execution and adjustable QA iterations

Use Cases

Scaling autonomous coding projects while maintaining manageable API expenditures
Reducing development latency by tuning agent timeouts and parallel processing limits
Implementing local memory systems to enhance privacy and eliminate embedding costs

About

Key Features

Memory system optimization supporting OpenAI and local Ollama embedding models
0 GitHub stars
Automated cost tracking and usage reporting for better budget management
Strategic model routing between Claude Opus and Sonnet based on task complexity
Granular token management including context window limits and thinking budget overrides
Build speed acceleration via parallel agent execution and adjustable QA iterations

Use Cases

Scaling autonomous coding projects while maintaining manageable API expenditures
Reducing development latency by tuning agent timeouts and parallel processing limits
Implementing local memory systems to enhance privacy and eliminate embedding costs