How do tool outputs impact context usage?

Tool outputs often consume the majority of tokens in an agent's trajectory (up to 83%), requiring strategies like masking or selective retention to prevent performance degradation.

Where is information most effective in a long prompt?

Research suggests models typically attend more effectively to information placed at the beginning and the end of the context window rather than the middle.

What is context engineering?

Context engineering is the discipline of curating the smallest, highest-signal token set available to a model at inference time to maximize accuracy while minimizing cost.

What is the progressive disclosure principle?

It is a strategy that manages context efficiency by loading detailed information only when needed, keeping the initial agent state lightweight and responsive.

Context Engineering Fundamentals

Name: Context Engineering Fundamentals
Author: shipshitdev

byshipshitdev

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数据科学与机器学习

Master the mechanics, constraints, and optimization strategies of context within AI agent architectures to improve performance and reduce costs.

Context Engineering Fundamentals provides a comprehensive framework for understanding how the state available to an LLM—including system prompts, tool definitions, and message history—impacts agent behavior. It introduces the 'Attention Budget' constraint and the 'Progressive Disclosure' principle to help developers design systems that are both token-efficient and highly accurate. By treating context as a finite resource, this skill enables the creation of robust agent architectures capable of handling long-horizon tasks without degradation in reasoning quality.

主要功能

01Implementation of progressive disclosure for just-in-time data loading

02Techniques for context budgeting and automated compaction triggers

03Deep dive into context components like system prompts and tool outputs

04Optimization of token placement for maximum model attention

0510 GitHub stars

06Strategies for managing the quadratic (n²) attention budget constraint

使用场景

01Debugging reasoning failures caused by context saturation or noise

02Designing scalable agent architectures for complex, multi-step workflows

03Reducing inference costs by curating high-signal context sets

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add shipshitdev/library context-fundamentals

For use in Claude.ai and ChatGPT

Download Skill