token-optimizer

To reduce Claude API costs in long sessions, apply context compaction strategies like proactive /compact and /clear, use subagents, and select the smallest model tier (Haiku, Sonnet, or Opus) that matches your task complexity.

Choosing between Claude Haiku, Sonnet, and Opus for code generation depends on task complexity; the token-optimizer provides model selection guidance to balance capability and cost, recommending the smallest model that fulfills your specific coding requirements.

Prompt engineering affects token usage by applying concise templates and inclusion or exclusion rules to minimize unnecessary tokens while preserving intent, directly lowering recurring costs and preventing context window exhaustion during multi-file workflows.

You should use context compaction when you need to retain relevant session history without exceeding the context window, whereas clearing the session is better for starting fresh tasks to maximize token efficiency and avoid hitting hard limits.

Yes, you can estimate API costs using rough cost ratios and token-per-word heuristics provided by the optimizer, along with prompt caching recommendations, to accurately forecast and lower recurring expenses for multi-file workflows.

Your Claude session hits the context window limit during debugging because accumulated conversational context and code files grow continuously; applying proactive context compaction schedules and subagent usage prevents this while maintaining session efficiency.