Token optimization in the Postman plugin for Claude Code

Anthropic's guide to context engineering highlights the importance of optimizing the context window for AI coding agents, as filling this window can lead to "context rot" and reduced model accuracy. Token optimization is crucial, not only for cost but also for ensuring that AI models can effectively reason about user tasks. The Postman plugin for Claude Code serves as a case study in reducing token usage, achieving a 60% reduction in the largest skill per trigger and a 20% cut in the always-on overhead, resulting in a typical session starting 3,600 tokens lighter. This was accomplished through progressive disclosure, minimizing redundant routing skills, and precise tool schema declarations, which also uncovered permission bugs. These optimizations enhance both efficiency and precision, as demonstrated by a real-world test that showed reduced cost and duration for a complex task. The work underscores the value of measuring token usage and refining context management to improve AI tool performance, with Anthropic's recommendations serving as a guideline for plugin authors.

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