Does it support Model Context Protocol (MCP)?

Yes, it includes advanced MCP auto-deferral logic that automatically moves tool definitions to a discovery mode when context usage exceeds 10%, saving thousands of tokens per session.

How does this differ from prompt engineering?

While prompt engineering focuses on the specific instructions or 'ask', context engineering manages the entire environment the model sees, including message history, tool outputs, and document retrieval.

What is the 'Lost in the Middle' phenomenon?

It is an LLM behavioral trait where models pay the highest attention to information at the start and end of a context window, often struggling to retrieve or process information located in the middle.

What is Context Engineering?

Context Engineering is the practice of curating the smallest high-signal token set to achieve optimal LLM outcomes, focusing on how information is positioned and managed within the context window.

How does this skill help reduce API costs?

It implements progressive disclosure, sliding window history with compression, and tool output truncation, ensuring you only pay for the tokens strictly necessary for the task.

Context Engineering

Name: Context Engineering
Author: yonatangross

byyonatangross

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Developer Tools

Optimizes LLM context windows through strategic positioning, token budgeting, and attention-aware design to maximize signal and reduce costs.

About

Context Engineering is a specialized skill for Claude Code designed to master the discipline of curating high-signal token sets for AI agents. It goes beyond simple prompting by managing the entire context lifecycle—system instructions, tool definitions, RAG retrieval, and message history. By leveraging attention mechanics to combat the 'lost in the middle' phenomenon and implementing dynamic compression triggers, this skill ensures that agents remain performant and cost-effective even as conversation complexity grows. It is essential for developers building complex multi-agent systems, deep RAG pipelines, or long-running AI sessions where token efficiency is critical.

Key Features

Dynamic management of five context layers: Identity, Capability, Knowledge, Memory, and Observation
Intelligent MCP auto-discovery and deferred loading to minimize token overhead
Strategic attention-aware positioning to combat 'lost in the middle' degradation
Automated token budget allocation and progressive compression triggers
Optimized tool definition patterns for clearer agent trigger conditions
29 GitHub stars

Use Cases

Managing high-token conversation histories in persistent multi-turn interactions
Optimizing RAG retrieval pipelines for efficient long-document analysis
Designing complex agent system prompts for high-stakes production environments

About

Key Features

Dynamic management of five context layers: Identity, Capability, Knowledge, Memory, and Observation
Intelligent MCP auto-discovery and deferred loading to minimize token overhead
Strategic attention-aware positioning to combat 'lost in the middle' degradation
Automated token budget allocation and progressive compression triggers
Optimized tool definition patterns for clearer agent trigger conditions
29 GitHub stars

Use Cases

Managing high-token conversation histories in persistent multi-turn interactions
Optimizing RAG retrieval pipelines for efficient long-document analysis
Designing complex agent system prompts for high-stakes production environments