What is context engineering?

Context engineering is the discipline of curating the smallest possible set of high-signal tokens to maximize the likelihood of desired outcomes in language model interactions.

Why is the attention budget important?

Language models have a finite attention budget where performance degrades as context length increases; context engineering helps optimize utility within these constraints.

How does this skill improve agent performance?

It provides strategies to avoid context degradation and the 'lost-in-middle' effect, ensuring the agent remains focused on the most relevant information for the task at hand.

Which platforms support the Context Engineering Kit?

While optimized for Claude Code, the principles and kit support OpenCode, Cursor, Windsurf, and Cline.

What is the progressive disclosure principle?

Progressive disclosure manages context efficiency by loading information only as needed, keeping the agent's context lightweight until specific tools or data are required.

Context Engineering Fundamentals

Name: Context Engineering Fundamentals
Author: NeoLabHQ

byNeoLabHQ

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300

Herramientas para Desarrolladores

Optimizes agent performance by managing context windows, attention budgets, and system prompt architecture.

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The Context Engineering skill provides AI agents with a foundational framework for understanding and optimizing their own context usage. By mastering concepts such as progressive disclosure, attention mechanics, and context degradation patterns, agents can maintain higher precision and efficiency during long-horizon tasks. This skill is essential for developers and agents aiming to reduce token costs while eliminating performance issues like the 'lost-in-middle' phenomenon in complex agentic workflows.

Características Principales

Framework for high-signal context curation and token efficiency
Advanced best practices for attention budget management and context budgeting
Strategies for progressive disclosure and just-in-time document loading
Guidelines for optimizing system prompts and tool definitions
Identification and mitigation techniques for context degradation patterns
300 GitHub stars

Casos de Uso

Optimizing long-running agent sessions to prevent performance decay
Architecting RAG systems with minimal token overhead and maximum relevance
Designing efficient system prompts for specialized autonomous sub-agents

Acerca de

Características Principales

Framework for high-signal context curation and token efficiency
Advanced best practices for attention budget management and context budgeting
Strategies for progressive disclosure and just-in-time document loading
Guidelines for optimizing system prompts and tool definitions
Identification and mitigation techniques for context degradation patterns
300 GitHub stars

Casos de Uso

Optimizing long-running agent sessions to prevent performance decay
Architecting RAG systems with minimal token overhead and maximum relevance
Designing efficient system prompts for specialized autonomous sub-agents