muratcankoylan/Agent-Skills-for-Context-Engineering

Registry

Summary

A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require effective context management.

Key features

• Context engineering skills for agent systems
• Multi-agent architecture support
• Production-grade agent system design
• Effective context management techniques
• Debugging and optimization utilities

Use cases

• Building agent systems with context management
• Optimizing multi-agent architectures
• Debugging production agent systems
• Implementing context engineering patterns
• Designing scalable agent workflows

README excerpt

# Agent Skills for Context Engineering A comprehensive, open collection of Agent Skills focused on context engineering and harness engineering principles for building production-grade AI agent systems. These skills teach the art and science of curating context, designing agent operating loops, and evaluating agent behavior across any agent platform. [DeepWiki: Learn more here](https://deepwiki.com/muratcankoylan/Agent-Skills-for-Context-Engineering) ## What is Context Engineering? Context engineering is the discipline of managing the language model's context window. Unlike prompt engineering, which focuses on crafting effective instructions, context engineering addresses the holistic curation of all information that enters the model's limited attention budget: system prompts, tool definitions, retrieved documents, message history, and tool outputs. The fundamental challenge is that context windows are constrained not by raw token capacity but by attention mechanics. As context length increases, models exhibit predictable degradation patterns: the "lost-in-the-middle" phenomenon, U-shaped attention curves, and attention scarcity. Effective context engineering means finding the smallest possible set of high-signal tokens that maximize the likelihood of desired outcomes. ## Recognition This repository is cited in academic research as foundational work on static skill architecture: > "While static skills are well-recognized [Anthropic, 2025b; Muratcan Koylan, 2025], MCE is among the first to dynamically evolve them, bridging manual skill engineering and autonomous self-improvement." 1. [Meta Context Engineering via Agentic Skill Evolution](https://arxiv.org/pdf/2601.21557), Peking University State Key Laboratory of General Artificial Intelligence (2025) 2. [Agent Ha

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