notebooklm

This skill optimizes AI model selection by reading quota states and recommending the best Claude/Codex/Gemini allocation for each task. It provides quota-aware routing and headroom displays, making it ideal for work sessions with multiple queued items or when approaching quota limits. Developers should use it before starting sessions with 3+ work items or when Claude quotas drop below 50% remaining.

This skill provides automated model selection guidance for Claude Code based on task complexity, cost, and latency needs. It offers a decision tree and quick reference table to help developers choose between Opus, Sonnet, and Haiku models efficiently. Use it when starting new tasks to optimize performance and resource usage.

This skill displays remaining API quota percentages for different AI providers in a formatted table with color-coded status indicators. It helps developers choose the most appropriate model based on availability and recommended use cases. The output includes cache freshness information and visual status thresholds for quick quota assessment.

This skill provides default AI model routing logic for different coding task complexities, independent of quota limits. It maps simple tasks to Codex, standard tasks to Claude Sonnet, and complex architectural work to Claude Opus, with fallback options specified. Developers should use this as the baseline configuration for optimizing AI agent usage in their coding workflows.