The Model Selection Guide is a specialized Claude Code skill designed to optimize development workflows by matching specific tasks to the most appropriate AI model. It provides a systematic framework for balancing speed, cost, and output quality, recommending a 20/60/20 distribution across Opus, Sonnet, and Haiku. Whether you are performing high-stakes security audits, routine feature implementation, or simple documentation updates, this skill offers clear escalation rules, thinking mode parameters, and phase-based selection strategies to ensure you get the best results while managing your token budget effectively.
Key Features
01Clear escalation and downgrade rules based on task complexity
020 GitHub stars
03Token cost optimization strategies to reduce overhead by up to 80%
04Task-to-model mapping for every phase of the development lifecycle
05Phase-specific recommendations from discovery to final review
06Strategic guidelines for enabling and budgeting Thinking mode
