How does this skill help reduce API costs?

It provides strategies for model selection (switching to Sonnet for simple tasks), reducing context window sizes, and implementing cost tracking to identify expensive operations.

Can I use this with local models?

Yes, the skill includes configurations for using Ollama as a provider for memory and embeddings to eliminate costs for those specific components.

What is 'Extended Thinking' optimization?

It allows you to tune the thinking token budget per agent type, ensuring complex planning gets enough resources while simple coding tasks remain fast and cheap.

Does this improve build speed?

Yes, it offers configurations for parallel agent execution, QA loop limits, and faster spec creation to shorten the overall development lifecycle.

How do I optimize memory usage?

The skill recommends efficient embedding models like text-embedding-3-small and database maintenance practices like compacting and clearing old entries.

Auto-Claude Optimization

Name: Auto-Claude Optimization
Author: adaptationio

byadaptationio

Productividad y Flujo de Trabajo

Optimizes Claude agent performance, reduces API costs, and accelerates build speeds through intelligent configuration and resource management.

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The Auto-Claude Optimization skill is designed for developers using Claude-powered agents who need to balance high-quality output with operational efficiency. It provides specialized guidance for tuning token usage, selecting the most cost-effective models for specific tasks, and managing context windows to prevent bloat. By implementing these performance patterns, users can significantly reduce API latency, lower their monthly costs, and increase the throughput of automated coding and QA tasks without sacrificing code quality.

Características Principales

Performance tuning for extended thinking tokens and API timeouts
Local memory optimization using Ollama for cost-free embedding storage
Advanced cost management and token usage tracking tools
0 GitHub stars
Intelligent model selection (Opus vs. Sonnet) based on task complexity
Context size reduction and codebase scanning optimization

Casos de Uso

Fine-tuning agent performance for complex feature implementation versus simple bug fixes
Reducing monthly API expenditures during large-scale automated development projects
Accelerating build times for rapid prototyping and iterative development

Acerca de

Características Principales

Performance tuning for extended thinking tokens and API timeouts
Local memory optimization using Ollama for cost-free embedding storage
Advanced cost management and token usage tracking tools
0 GitHub stars
Intelligent model selection (Opus vs. Sonnet) based on task complexity
Context size reduction and codebase scanning optimization

Casos de Uso

Fine-tuning agent performance for complex feature implementation versus simple bug fixes
Reducing monthly API expenditures during large-scale automated development projects
Accelerating build times for rapid prototyping and iterative development