What problem does it solve? Long-running LLM agents silently degrade as context grows: critical instructions get lost in the middle of the window, hallucinated facts poison downstream reasoning, and irrelevant documents dilute attention. This Skill provides detection patterns, mitigation frameworks, and Python tooling to identify and fix these failures before they cascade. ## Core Features & Use Cases - Five Degradation Patterns: Recognize and mitigate lost-in-middle, context poisoning, distraction, confusion, and clash with pattern-specific detection signals and recovery procedures. - Health Analysis Toolkit: Run composite context health checks combining attention distribution, poisoning detection, and utilization metrics via the included degradation_detector.py script. - Four-Bucket Mitigation Framework: Apply Write, Select, Compress, and Isolate strategies matched to the active degradation pattern. - Use Case: An agent's output quality drops after 40K tokens of conversation. Use this Skill to measure attention distribution, detect that key instructions sit in the degraded middle region, and restructure context so critical information sits at attention-favored positions. ## Quick Start Analyze my agent's conversation context for degradation symptoms and recommend whether to compact, truncate, or restructure it.
