Claude Skill
muratcankoylan/Agent-Skills-for-Context-Engineering
一套全面的智能体技能集，专为上下文工程、多智能体架构与生产级智能体系统设计。高效上下文管理，助力构建、优化与调试。
概览
仓库信息
安装这个 Skill
git clone https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering.gitRegistry 信息
项目简介
一套全面的智能体技能集，专为上下文工程、多智能体架构与生产级智能体系统设计。适用于构建、优化或调试需要高效上下文管理的智能体系统。
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.
要点
- 智能体系统的上下文工程技能
- 多智能体架构支持
- 生产级智能体系统设计
- 高效的上下文管理技术
- 调试与优化工具
使用场景
- 构建具有上下文管理的智能体系统
- 优化多智能体架构
- 调试生产级智能体系统
- 实现上下文工程模式
- 设计可扩展的智能体工作流
README 摘要
# 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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