task-complexity-router

AI API cost reduction is achieved by routing tasks to different model tiers based on complexity. This skill uses keyword and context heuristics to match simple tasks to cheaper models and complex tasks to premium LLMs, saving 60-90% on costs.

LLM tiering categorizes AI models into capability levels to optimize model selection. This skill uses a 4-tier system ranging from native tools and Haiku to Sonnet and Opus, matching task requirements to the appropriate tier for cost efficiency and speed.

Dynamic tier escalation for multi-agent coordination systems is supported. The skill analyzes task requirements and can escalate to higher model tiers when needed, while integrating with multi-agent coordination workflows to manage cost tracking.

The best way to analyze task complexity for AI orchestration is using keyword and context heuristics. This skill evaluates task requirements to select optimal model tiers, ensuring simple requests like adding comments use fast models while architecture design uses premium LLMs.

AI model routing works without external dependencies. The skill operates independently using internal references to analyze task complexity and route requests across the 4-tier system, from native tools to premium models like Claude Opus.

Automated task complexity routing should not be used when precise manual control over specific model selection is required. The skill relies on keyword and context heuristics, which may not perfectly align with specialized routing preferences for unique edge cases.