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context-fundamentals

Explain agent context structure, constraints, and management for reliable model behavior.

Updated Jan 15, 2026
Search Tags:#context#prompt engineering#progressive disclosure#tool design#token budgeting#agent systems
One-click install
npx skills add https://github.com/Razaib-khan/Hackathon-2-phases-2-3-4-5- --skill context-fundamentals-razaib-khan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: context-fundamentals
Source: https://github.com/Razaib-khan/Hackathon-2-phases-2-3-4-5-/tree/main/.claude/skills/context-fundamentals
Command: npx skills add https://github.com/Razaib-khan/Hackathon-2-phases-2-3-4-5- --skill context-fundamentals-razaib-khan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you understand how context works in agent systems so you can design more reliable workflows, diagnose context-related failures, and use limited context more effectively.

Core Features & Use Cases

  • Context Architecture: Explains the main parts of agent context, including system prompts, tool definitions, retrieved documents, message history, and tool outputs.
  • Context Optimization: Guides you in managing attention budgets, using progressive disclosure, and reducing token waste for long-running or complex tasks.
  • Practical Skill Use: Supports tasks like calibrating system prompts, deciding what to load into context, and structuring references for on-demand retrieval.

Quick Start

Use the context-fundamentals skill to help me design a context-efficient agent workflow for a long-running task.

Frequently Asked Questions about context-fundamentals

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

FAQPage Schema
What is agent context and why does it fail in long-running tasks?▼

Agent context includes system prompts, tool definitions, retrieved documents, message history, and tool outputs. Context failures occur when token limits are exceeded or attention budgets are mismanaged, degrading model behavior in long-running tasks.

How do I optimize token budgeting for complex agent workflows?▼

Optimize token budgeting by applying progressive disclosure to load only necessary references on-demand, reducing token waste across prompts, tools, messages, and retrieved documents for reliable model behavior.

How do I design a context-efficient agent architecture?▼

Design context-efficient architectures by calibrating system prompts, structuring references for on-demand retrieval, and balancing context quality tradeoffs to maintain attention budgets across complex tasks.

When should I use progressive disclosure in agent systems?▼

Use progressive disclosure in agent systems when managing long-running or complex tasks that require reducing token waste and controlling what gets loaded into context for on-demand retrieval.

How do I debug context-related failures in prompt engineering?▼

Debug context-related failures by analyzing the structure and constraints of agent context, diagnosing attention budget issues, and identifying token waste across prompts, tools, messages, and retrieved documents.

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