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The Context Degradation skill provides a specialized framework for identifying and resolving the inherent performance drops that occur as LLM context length increases. It equips developers with diagnostic patterns for recognizing the 'lost-in-middle' phenomenon, context poisoning, distraction, and architectural confusion that can derail multi-agent systems. By applying empirical benchmarks and model-specific thresholds for leading LLMs like Claude and GPT, this skill enables the design of resilient context management strategies—such as partitioning, isolation, and recovery—essential for production-grade agentic workflows.
