Claude Skill
MassLab-SII/open-agent-skills
开源Python框架，用于构建具有卓越性能、更高确定性和更低成本的智能体技能。在保持一致性的同时减少上下文消耗。
概览
仓库信息
安装这个 Skill
git clone https://github.com/zjtco-yr/open-agent-skills.gitRegistry 信息
项目简介
Open Agent Skills 是一个 Python 项目，致力于构建一套开放的智能体技能体系，旨在以更低的成本与更少的上下文消耗，为目标任务提供卓越性能、更高确定性与更强一致性的解决方案。
We are dedicated to building a set of open agent skills that deliver superior performance, higher determinism, and greater consistency on targeted tasks, while operating at a lower cost and with reduced context usage.
要点
- 针对目标任务提供卓越性能
- 更高的确定性与一致性
- 更低的运行成本
- 更少的上下文消耗
- 开放且可访问的技能集
使用场景
- 构建高性价比的AI智能体
- 开发确定性的任务自动化
- 创建一致的AI驱动解决方案
- 减少大语言模型上下文窗口需求
- 实现专业化的智能体技能
README 摘要
<div align="center"> <img src="assets/logo.png" alt="Open Agent Skills" width="400"> **Enhancing AI Agent Efficiency Through Domain-Specific Skills**  </div> --- > **"Model Context Protocol (MCP) connects Claude to third-party tools, and skills teach Claude how to use them well."** > — *[Extending Claude's capabilities with skills and MCP servers](https://claude.com/blog/extending-claude-capabilities-with-skills-mcp-servers)*, Anthropic ## 📖 Introduction As AI agents evolve, there is a growing need for modular, reusable approaches to equip them with domain-specific expertise while mitigating issues like excessive MCP context consumption. To address this, Anthropic introduced **[Agent Skills](https://agentskills.io/home)** as an open standard on December 18, 2025, allowing agents to dynamically load structured instructions and resources for more effective task execution. Although platforms such as **[OpenAI Codex](https://developers.openai.com/codex/skills/)** have adopted this standard, native support remains limited to specific ecosystems. However, many LLM providers have not yet adopted the Agent Skills standard, leaving this efficient approach temporarily inaccessible to a broader audience. We, **the Project Q team at SII**, bridge this gap by providing a lightweight, efficient open-source framework fully compatible with the Agent Skills standard, extending these capabilities to any LLM provider. Our implementation focuses on the synergy between **Model Context Protocol (MCP)** and **Skills**: MCP provides access to external tools and systems, while Skills provide the procedural knowledge to utilize tools (including MCP) effectively. With our skill-based implementation, we achieved up to **~20x context reduction*
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