What is the progressive disclosure pattern?

It's a design architecture where only essential instructions (SKILL.md) are loaded initially, with detailed reference materials and guides loaded only when specifically needed for a task.

How does this skill save tokens?

It implements strategies like progressive disclosure, unloading unnecessary files, and separating planning from execution to minimize the total data sent to the model in each turn.

What is context engineering in Claude Code?

It is the practice of systematically managing and optimizing the context window to improve performance, reduce costs, and maintain conversation quality during AI-assisted development.

When should I use context-engineering?

Use it when your context usage exceeds 60-70%, when building large custom skills, or when you notice performance degradation or 'drift' in long conversation sessions.

Can I reduce API costs with this skill?

Yes, by reducing the number of tokens processed in each turn through proactive context management, you directly lower the API costs associated with running Claude Code.

Context Engineering for Claude Code

Name: Context Engineering for Claude Code
Author: adaptationio

byadaptationio

Productividad y Flujo de Trabajo

Optimizes Claude Code context usage and token efficiency through advanced monitoring and reduction strategies.

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Context Engineering is a specialized skill designed to maximize the effectiveness of Claude Code by managing the context window and reducing token consumption. It provides systematic strategies for monitoring real-time usage, implementing progressive disclosure patterns, and separating planning from execution phases to prevent conversation drift. By utilizing file-based optimization and strategic context editing, developers can achieve up to 95% token savings and significantly improve the performance and response quality of multi-turn conversations in large-scale projects.

Características Principales

Stale tool result and redundant file removal strategies
Real-time context window and token usage monitoring
File-based optimization for handling large datasets
0 GitHub stars
Progressive disclosure patterns for efficient skill design
Separation of planning and execution sessions to reduce bloat

Casos de Uso

Reducing API costs by optimizing context-heavy operations
Managing large-scale codebase interactions without hitting token limits
Maintaining high conversation quality in extended development sessions

Acerca de

Características Principales

Stale tool result and redundant file removal strategies
Real-time context window and token usage monitoring
File-based optimization for handling large datasets
0 GitHub stars
Progressive disclosure patterns for efficient skill design
Separation of planning and execution sessions to reduce bloat

Casos de Uso

Reducing API costs by optimizing context-heavy operations
Managing large-scale codebase interactions without hitting token limits
Maintaining high conversation quality in extended development sessions