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context-fundamentals

Design progressive disclosure and token budgeting for AI agent contexts.

Updated Apr 15, 2025
Search Tags:#progressive-disclosure#context-engineering#ai-agents#context-management#prompt-design#token-budget#retrieval-augmentation
One-click install
npx skills add https://github.com/khrore/nix-config --skill context-fundamentals-khrore
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: context-fundamentals
Source: https://github.com/khrore/nix-config/tree/main/dotfiles/common/.config/opencode/skills/context-fundamentals
Command: npx skills add https://github.com/khrore/nix-config --skill context-fundamentals-khrore

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Context management for AI agents is the challenge of organizing and loading only the information necessary for a task, within token budgets, while keeping flexibility for on-demand detail.

Core Features & Use Cases

  • Progressive disclosure and on-demand detail loading to minimize context size without sacrificing task performance.
  • Token budgeting and context partitioning to maintain responsiveness across long-running tasks.
  • Robust validation and safety checks to detect structure issues and prevent information leakage.

Quick Start

Provide a task description and system prompt, and I will assemble an optimized, token-efficient context for your AI task.

Frequently Asked Questions about context-fundamentals

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

FAQPage Schema
What is progressive disclosure in AI context management?▼

Token budgeting partitions available context space to maintain agent responsiveness during long-running tasks. It allocates specific token limits across system prompts, tool definitions, and retrieved documents to prevent overload.

How do I optimize token budgeting for long-running AI agent tasks?▼

Token budgeting partitions available context space to maintain agent responsiveness during long-running tasks. It allocates specific token limits across system prompts, tool definitions, and retrieved documents to prevent overload.

How to design structured system prompts for AI agents?▼

Provide a task description and system prompt to assemble an optimized, token-efficient context. The process applies structured templates and agent workflows to minimize context load while preserving essential signals.

Does context management work with retrieval-augmented generation pipelines?▼

Context management supports retrieval-augmented generation by treating retrieved documents as an input component. It applies validation checks to detect structure issues and partition retrieved content within the token budget.

What are the limitations of progressive disclosure for prompt design?▼

Progressive disclosure requires robust validation to prevent information leakage during on-demand loading. It is limited by the need for structured system prompts and carefully partitioned tool definitions to maintain essential signals.

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