What is 'Lost-in-Middle' in LLMs?

It is a phenomenon where language models show 10-40% lower recall for information located in the middle of a large context compared to information placed at the very beginning or end.

Why does performance drop with larger context windows?

As context grows, attention mechanics struggle to filter relevance, leading to non-linear performance degradation, increased hallucinations, and cognitive bottlenecks despite high token limits.

How do I fix context poisoning?

Recovery involves truncating the context to a point before the error occurred, explicitly flagging the poisoning, and re-evaluating the logic using a clean context window.

What is the Four-Bucket approach for context management?

It is a strategy that manages context by either writing data outside the window, selecting only relevant snippets, compressing information to preserve tokens, or isolating tasks across multiple sub-agents.

LLM Context Health & Diagnostics

Name: LLM Context Health & Diagnostics
Author: eyadsibai

byeyadsibai

Analytics & Monitoring

Diagnoses and mitigates performance drops in language models caused by long context windows and information saturation.

About

This skill provides specialized guidance for debugging and preventing AI agent failures related to large context windows. It helps developers identify common failure patterns like 'Lost-in-the-Middle' recall drops, context poisoning from compounding errors, and distraction caused by irrelevant information. By implementing proven mitigation strategies—such as the Four-Bucket approach for context management and optimizing critical info placement—users can ensure their Claude Code workflows maintain high accuracy and reasoning capabilities even as repository complexity and token counts increase.

Key Features

Detection and recovery patterns for context poisoning
Strategic context compaction and isolation techniques
Best practices for information placement and namespacing
Model-specific degradation threshold monitoring
0 GitHub stars
Diagnostics for 'Lost-in-Middle' recall failures

Use Cases

Debugging why an agent ignores specific instructions in long chat histories
Recovering from persistent hallucinations caused by corrupted or clashing context
Optimizing RAG pipelines to prevent context distraction from irrelevant documents

About

Key Features

Detection and recovery patterns for context poisoning
Strategic context compaction and isolation techniques
Best practices for information placement and namespacing
Model-specific degradation threshold monitoring
0 GitHub stars
Diagnostics for 'Lost-in-Middle' recall failures

Use Cases

Debugging why an agent ignores specific instructions in long chat histories
Recovering from persistent hallucinations caused by corrupted or clashing context
Optimizing RAG pipelines to prevent context distraction from irrelevant documents