context-engineering

Analyze token utilization and implement compression strategies for LLM context windows.

Updated Feb 11, 2026

Search Tags:#context #optimization #llm #agent design #performance tuning #token usage

Ready to run

npx skills add https://github.com/IoT-VN/tuya-energy-dashboard --skill context-engineering-iot-vn

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: context-engineering
Source: https://github.com/IoT-VN/tuya-energy-dashboard/tree/main/.opencode/skills/context-engineering
Command: npx skills add https://github.com/IoT-VN/tuya-energy-dashboard --skill context-engineering-iot-vn

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python-json-logger, pytest, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenges of managing and optimizing the input (context) provided to Large Language Models, ensuring efficient token usage and maximizing reasoning quality.

Core Features & Use Cases

Context Limit Management: Monitor and manage token usage to stay within model limits.
Token Optimization: Employ strategies to reduce token count without sacrificing essential information.
Debugging Context Failures: Identify and resolve issues like "lost-in-middle" or context degradation.
Use Case: When building an AI agent that processes long documents or maintains extended conversations, this Skill helps ensure the agent doesn't exceed its context window, leading to performance degradation or errors.

Quick Start

Analyze the context usage for the current session to identify potential optimization opportunities.

Frequently Asked Questions about context-engineering

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

FAQPage Schema

What is LLM context engineering and when do I need to optimize token usage?▼

LLM context engineering manages input windows to maximize reasoning quality. You need it when processing long documents or extended conversations, ensuring agents avoid exceeding limits and prevent performance degradation.

How do I debug LLM context failures like lost-in-middle or context poisoning?▼

Debug context failures by analyzing token utilization to identify degradation patterns. This process isolates lost-in-middle and poisoning issues, enabling targeted compression and masking strategies to restore agent reasoning quality.

What is the best way to reduce token count in large language models without losing information?▼

The best way to reduce token count is implementing compression and masking strategies. These techniques minimize token usage while preserving essential information, keeping agent architectures and memory systems within model limits.

Does this context optimization approach work for extended conversations in agent architectures?▼

Yes, this approach works for extended conversations by actively monitoring token usage. It manages memory systems in agent architectures, preventing context window overflow that leads to lost-in-middle degradation or processing errors.

Why does my AI agent lose information when processing long documents?▼

Your agent loses information due to context degradation patterns like lost-in-middle. Analyzing token utilization identifies these failures, allowing you to apply compression strategies that maintain reasoning quality across long document inputs.

Do I need Python and pytest to implement context window management?▼

You need Python and pytest to implement and test context window management. These dependencies support the scripts and references required to analyze token usage and validate optimization strategies.