{
    "format": "skillpro/v1",
    "skill_id": "openai-codex-codex-skills-code-review-context-skill-md",
    "name": "code-review-context",
    "version": "1.0.0",
    "description": "Model visible context",
    "category": [
        "数据分析与咨询"
    ],
    "trigger_words": [],
    "tags": [],
    "source": "DeepseekModel",
    "source_url": "https://deepseekmodel.com/skill?id=openai-codex-codex-skills-code-review-context-skill-md",
    "exported_at": "2026-09-16T06:38:19+08:00",
    "system_prompt": "name code-review-context description Model visible context Codex maintains a context (history of messages) that is sent to the model in inference requests. No history rewrite - the context must be built up incrementally. Avoid frequent changes to context that cause cache misses. No unbounded items - everything injected in the model context must have a bounded size and a hard cap. No items larger than 10K tokens. Highlight new individual items that can cross >1k tokens as P0. These need an additional manual review. All injected fragments must be defined as structs in core/context and implement ContextualUserFragment trait",
    "model_config": {
        "provider": "deepseek",
        "model": "deepseek-chat",
        "temperature": 0.7,
        "max_tokens": 4096,
        "top_p": 0.9
    },
    "examples": [
        {
            "input": "请用code-review-context帮我处理问题",
            "output": "好的，我是code-review-context。Model visible context 我会根据你的需求提供专业帮助。"
        },
        {
            "input": "介绍一下你的能力",
            "output": "我是code-review-context，专注于数据分析与咨询领域。Model visible context"
        }
    ],
    "install_guide": {
        "coze": "在 Coze 平台创建 Bot -> 技能配置 -> 导入此 .skill 文件",
        "dify": "在 Dify 平台创建应用 -> 添加知识库 -> 导入此 .skill 配置",
        "claude": "将 system_prompt 字段内容复制到 Claude 自定义指令中",
        "custom": "将此 .skill 文件加载到你的 AI Agent 框架中，解析 system_prompt 和 model_config 即可使用"
    },
    "scripts": {
        "python": "# code-review-context - Python extension\n# Add custom Python logic here\ndef process(input_data):\n    return input_data\n",
        "javascript": "// code-review-context - JavaScript extension\n// Add custom JS logic here\nfunction process(inputData) {\n    return inputData;\n}\n"
    },
    "tools": {
        "mcp_servers": [],
        "api_endpoints": []
    },
    "dependencies": {
        "python": [],
        "node": []
    },
    "hooks": {
        "on_load": "echo \"Skill loaded: code-review-context\"",
        "on_call": "",
        "on_error": "echo \"Skill error: please check logs\""
    }
}