context-fundamentals

Explain context engineering fundamentals for language model agents.

10|2|Updated Mar 12, 2026

Search Tags:#llm #context engineering #agent design #progressive disclosure #token budgeting #attention budget

Ready to run

npx skills add https://github.com/viktorbezdek/skillstack --skill context-fundamentals-viktorbezdek

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: context-fundamentals
Source: https://github.com/viktorbezdek/skillstack/tree/main/context-fundamentals/skills/context-fundamentals
Command: npx skills add https://github.com/viktorbezdek/skillstack --skill context-fundamentals-viktorbezdek

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It provides a clear, foundational understanding of context engineering, helping developers and agents manage limited attention budgets and avoid context overload.

Core Features & Use Cases

Fundamental Theory: Defines system prompts, tool definitions, retrieved documents, message history, and tool outputs as core context components.
Practical Guidance: Shows how to apply progressive disclosure and budgeting when designing new agents, onboarding teams, or optimizing token usage.
Reference Integration: Includes technical references for deeper exploration of each component.

Quick Start

Explain the basics of context engineering and how to apply progressive disclosure in my agent.

Frequently Asked Questions about context-fundamentals

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is context engineering and why does it matter for LLM agents?▼

Core context components include system prompts, tool definitions, retrieved documents, message history, and tool outputs, all of which must be structured to meet token budgeting requirements.

How do I optimize token budgets when designing LLM agent architectures?▼

You optimize token budgets by applying progressive disclosure to manage system prompts, tool definitions, retrieved documents, message history, and tool outputs within limited attention constraints.

What is progressive disclosure in the context of language model agents?▼

Progressive disclosure is a context engineering technique that structures system prompts, tool definitions, and retrieved documents to reveal information gradually, preventing context overload and preserving the agent's attention budget.

When should I apply context engineering fundamentals to my agent deployments?▼

Apply context engineering fundamentals when designing new agent architectures, onboarding teams to LLM workflows, or optimizing token budgets to prevent attention budget exhaustion and context overload.

Can I use these context engineering principles for onboarding teams to LLM development?▼

Yes, these fundamentals provide structured guidance on system prompts, tool definitions, retrieved documents, message history, and tool outputs, making it suitable for onboarding teams to LLM agent design.