What metrics does this skill help me track?

It focuses on key indicators like token utilization (targeting 70-80%), token variance, KV-cache hit rates, and compaction targets to ensure peak model performance.

When should I use this Claude Code skill?

Activate this skill when designing complex agent architectures, debugging performance drops in long conversations, or optimizing LLM API expenses.

Does this skill include tools for evaluation?

Yes, it includes scripts for context health analysis and compression quality evaluation to ensure your optimizations don't result in information loss.

What is context engineering?

Context engineering is the practice of curating the smallest, highest-signal set of tokens for LLM tasks to maximize reasoning quality while minimizing token usage and latency.

How does this help with multi-agent systems?

It provides specific patterns for context isolation, ensuring that sub-agents only receive the specific data they need, which prevents model degradation and 'lost-in-the-middle' failures.

Context Engineering

Name: Context Engineering
Author: nordeim

bynordeim

数据科学与机器学习

Optimizes LLM context windows to maximize reasoning quality while minimizing token costs and latency.

关于

This skill provides a comprehensive framework for mastering context engineering in AI agent systems, focusing on the curation of high-signal tokens to improve model performance. It equips developers with techniques for debugging context failures, implementing persistent memory architectures, and managing multi-agent coordination through smart isolation and compression strategies. By applying principles like progressive disclosure and the U-shaped attention curve, users can build more efficient, cost-effective, and reliable LLM-powered pipelines and agentic workflows.

主要功能

Advanced context optimization techniques including compaction, masking, and caching
Memory system architectures for cross-session persistence and knowledge management
Multi-agent coordination patterns for improved context isolation and task partitioning
LLM-as-Judge evaluation frameworks for measuring agent performance and token efficiency
Automated context health analysis and degradation detection scripts
0 GitHub stars

使用场景

Debugging 'lost-in-the-middle' performance issues in large context window models
Building complex agentic workflows that require long-term memory and state management
Optimizing high-traffic LLM applications to reduce token costs and API latency

关于

主要功能

Advanced context optimization techniques including compaction, masking, and caching
Memory system architectures for cross-session persistence and knowledge management
Multi-agent coordination patterns for improved context isolation and task partitioning
LLM-as-Judge evaluation frameworks for measuring agent performance and token efficiency
Automated context health analysis and degradation detection scripts
0 GitHub stars

使用场景

Debugging 'lost-in-the-middle' performance issues in large context window models
Building complex agentic workflows that require long-term memory and state management
Optimizing high-traffic LLM applications to reduce token costs and API latency