How does this skill help reduce LLM costs?

It implements compaction and compression strategies designed to reduce token usage by 50-70% while maintaining less than 5% loss in reasoning quality.

Does it include tools for evaluating agent performance?

Yes, it provides an LLM-as-Judge framework and probe-based evaluation scripts to measure and validate agent outputs against established baselines.

What are the recommended token utilization thresholds?

The skill suggests a warning threshold at 70% token utilization and recommends triggering automated optimization once usage hits 80%.

When should I use multi-agent patterns over a single agent?

This skill recommends multi-agent patterns for parallelizable tasks or when context limits constrain performance, using isolation to prevent context degradation.

What is Context Engineering for Claude Code?

It is a specialized skill that provides Claude with the frameworks and scripts needed to curate high-signal token sets, improving reasoning accuracy and reducing API costs.

Context Engineering for AI Agents

Name: Context Engineering for AI Agents
Author: mrgoonie

bymrgoonie

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1,395

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Data Science & ML

Optimizes LLM performance by curating high-signal token sets, managing memory architectures, and implementing multi-agent coordination patterns.

This skill provides a comprehensive framework for mastering context management within AI agent systems, enabling developers to design efficient architectures and debug performance failures. It offers specialized techniques for token optimization—such as compaction, masking, and caching—alongside advanced memory systems for cross-session persistence. By leveraging multi-agent coordination patterns and LLM-as-Judge evaluation metrics, users can maximize reasoning quality while significantly reducing costs and latency in complex LLM-powered pipelines.

Key Features

01Comprehensive evaluation frameworks using LLM-as-Judge and probe-based testing

02Memory system architectures for cross-session persistence and knowledge graphs

03Automated context health analysis and degradation detection scripts

04Multi-agent coordination patterns designed for context isolation and partitioning

05Advanced context optimization techniques including compaction, masking, and caching

061,395 GitHub stars

Use Cases

01Designing and debugging complex autonomous agent architectures

02Building scalable multi-agent workflows with strict context boundary management

03Reducing operational costs and latency in high-volume LLM pipelines

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mrgoonie/claudekit-skills context-engineering

For use in Claude.ai and ChatGPT

Download Skill