Pace your Claude-code to avoid hitting limits (github.com)

A new plugin called "cc-limit-pacer" has been released for Claude Code, designed to optimize the usage of AI models within specific session limits. This tool tracks the user's five-hour and weekly usage, automatically adjusting model tiers (e.g., from Fable to Opus) when limits are nearing, thereby preventing lockouts. Additionally, it holds automated runs until the usage window resets and compacts sessions more efficiently, preserving context better than previous settings. The plugin also offers detailed audits of usage, highlighting potential savings and wasted resources. This development holds significant implications for the AI/ML community, especially for users who rely on Claude Code for intensive tasks. By better managing resource allocation, the cc-limit-pacer can enhance the longevity of model usage and reduce the frustration of hitting limits. It supports Python 3.9+, is accessible via standard library functionalities, and provides users with tools for calibration, simulation, and auditing—making it easier to fine-tune operations based on real-world usage data. These optimizations can lead to more effective usage of AI resources, ultimately enhancing productivity and engagement in AI-driven projects.