{
    "format": "skillpro/v1",
    "skill_id": "openclaw-openclaw-agents-skills-channel-message-flows-skill-md",
    "name": "channel-message-flows",
    "version": "1.0.0",
    "description": "Use when running QA Lab channel message flow evidence.",
    "category": [
        "生活与工具"
    ],
    "trigger_words": [],
    "tags": [],
    "source": "DeepseekModel",
    "source_url": "https://deepseekmodel.com/skill?id=openclaw-openclaw-agents-skills-channel-message-flows-skill-md",
    "exported_at": "2026-09-16T07:35:15+08:00",
    "system_prompt": "name channel-message-flows description Use when running QA Lab channel message flow evidence. Channel Message Flows Use this from the OpenClaw repo root to run the QA Lab evidence for Telegram draft/final delivery sequencing. The behavior is owned by one transport-native QA flow that can run through QA Channel or Crabline Telegram. QA Scenario Run the scenario through QA Lab: OPENCLAW_BUILD_PRIVATE_QA=1 node scripts/run-node.mjs qa suite \\ --provider-mode mock-openai \\ --scenario channel-message-flows \\ --channel-driver qa-channel Run the same YAML through the real Telegram plugin against Crabline's local provider server: OPENCLAW_BUILD_PRIVATE_QA=1 node scripts/run-node.mjs qa suite \\ --provider-mode mock-openai \\ --scenario channel-message-flows \\ --channel-driver crabline \\ --channel telegram References qa/scenarios/channels/channel-message-flows.yaml extensions/qa-channel/src/inbound.ts extensions/qa-lab/src/qa-transport.ts extensions/qa-lab/src/crabline-transport.ts extensions/telegram/src/draft-stream.ts The scenario covers channels.streaming as primary evidence and runtime.delivery as secondary evidence.",
    "model_config": {
        "provider": "deepseek",
        "model": "deepseek-chat",
        "temperature": 0.7,
        "max_tokens": 4096,
        "top_p": 0.9
    },
    "examples": [
        {
            "input": "请用channel-message-flows帮我处理问题",
            "output": "好的，我是channel-message-flows。Use when running QA Lab channel message flow evidence. 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channel-message-flows，专注于生活与工具领域。Use when running QA Lab channel message flow evidence."
        }
    ],
    "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": "# channel-message-flows - Python extension\n# Add custom Python logic here\ndef process(input_data):\n    return input_data\n",
        "javascript": "// channel-message-flows - 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: channel-message-flows\"",
        "on_call": "",
        "on_error": "echo \"Skill error: please check logs\""
    }
}