context-degradation

Detects context degradation and provides actionable recommendations for AI sessions.

1|Updated Jan 4, 2026

Search Tags:#monitoring #lost-in-middle #attention #context-degradation #poisoning-detection #context-health #ai-robustness

Ready to run

npx skills add https://github.com/ChakshuGautam/games --skill context-degradation-chakshugautam

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: context-degradation
Source: https://github.com/ChakshuGautam/games/tree/main/.claude/skills/context-degradation
Command: npx skills add https://github.com/ChakshuGautam/games --skill context-degradation-chakshugautam

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Context degradation in long-running AI agent conversations leads to unreliable outputs and reduced task performance. This skill helps diagnose and mitigate context-related failures.

Core Features & Use Cases

Detect Lost-in-Middle patterns and attention degradation in context windows.
Analyze context structure, poisoning indicators, and overall context health scores.
Provide actionable recommendations to preserve critical information and maintain reliability across sessions.

Quick Start

Run the context health analysis on your current session to obtain a health score and recommendations.

Frequently Asked Questions about context-degradation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I detect lost-in-middle attention degradation in long-running AI agent sessions?▼

Detect lost-in-middle attention degradation by analyzing multi-turn conversations to identify where critical information is dropped from context windows, providing a context health score and actionable recommendations to improve AI robustness.

What is context poisoning and how can I diagnose it in language model interactions?▼

Context poisoning is the corruption of reliable context by misleading inputs in long-running sessions. Diagnose it by analyzing context structure and poisoning indicators to calculate a health score and provide mitigation recommendations.

Can I use this to monitor context health across multi-turn conversations with only numpy installed?▼

Yes, you can monitor context health across multi-turn conversations using this skill with only numpy installed, as it applies detection algorithms to long-running agent sessions to identify degraded attention and distraction.

Why does my language model lose critical information during long multi-turn conversations?▼

Your language model loses critical information due to context degradation, where attention degrades over long sessions. Analyzing context structure and applying context health scoring identifies lost-in-middle patterns to prevent unreliable outputs.

What is the best way to improve AI robustness against context degradation in long-running agents?▼

Improve AI robustness against context degradation by running context health analysis to detect degraded attention and poisoning indicators, generating actionable recommendations to preserve critical information and maintain reliability across sessions.

Are there limitations to diagnosing context degradation when maintaining reliable context for multi-turn conversations?▼

Limitations in diagnosing context degradation include relying solely on numpy for numerical analysis and detecting patterns like lost-in-middle and distraction, which may not capture all nuanced context poisoning scenarios in complex agent sessions.