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Copy file name to clipboardExpand all lines: README.md
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</details>
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### Agent Skills
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**MS-Agent Skills** is an **Implementation** of the [**Anthropic-Agent-Skills**](https://docs.claude.com/en/docs/agents-and-tools/agent-skills) protocol, enabling agents to autonomously explore and execute complex tasks by leveraging predefined or custom "skills".
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#### Key Features
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- 📜 **Standard Skill Protocol**: Fully compatible with the [Anthropic Skills](https://github.com/anthropics/skills) protocol
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- 🧠 **Heuristic Context Loading**: Loads only necessary context—such as `References`, `Resources`, and `Scripts` on demand
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- 🤖 **Autonomous Execution**: Agents autonomously analyze, plan, and decide which scripts and resources to execute based on skill definitions
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- 🔍 **Skill Management**: Supports batch loading of skills and can automatically retrieve and discover relevant skills based on user input
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- 🛡️ **Code Execution Environment**: Optional local direct code execution or secure sandboxed execution via [**ms-enclave**](https://github.com/modelscope/ms-enclave), with automatic dependency installation and environment isolation
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- 📁 **Multi-file Type Support**: Supports documentation, scripts, and resource files
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- 🧩 **Extensible Design**: The skill data structure is modularized, with implementations such as `SkillSchema` and `SkillContext` provided for easy extension and customization
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#### Quick Start
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> 💡 Note:
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> 1. Before running the following examples, ensure that you have set the `OPENAI_API_KEY` and `OPENAI_BASE_URL` environment variables to access the required model APIs.
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> 2. Agent Skills requires ms-agent >= 1.4.0
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**Installation**:
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```shell
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pip install ms-agent
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```
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**Usage**:
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> This example demonstrates how to configure and run an Agent Skill that generates generative art code based on p5.js flow fields.
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Refer to: [Run Skills](projects/agent_skills/run.py)
Copy file name to clipboardExpand all lines: projects/code_genesis/PR_ARTICLE.md
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Code Genesis provides **two configurable workflow modes** to balance between generation quality and execution speed, enabling users to select the optimal strategy based on project complexity:
The standard pipeline implements a rigorous 7-stage process optimized for complex, production-ready projects:
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This workflow guarantees architectural coherence, eliminates reference errors through explicit dependency modeling, and produces enterprise-grade codebases with comprehensive documentation.
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