{
    "app": {
        "name": "channel-message-flows",
        "description": "Use when running QA Lab channel message flow evidence.",
        "mode": "advanced-chat",
        "model_config": {
            "provider": "deepseek",
            "model": "deepseek-chat",
            "parameters": {
                "temperature": 0.7,
                "max_tokens": 4096
            }
        }
    },
    "instructions": "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.",
    "variables": [],
    "opening_statement": "你好，我是 channel-message-flows，Use when running QA Lab channel message flow evide...",
    "suggested_questions": [],
    "source": "DeepseekModel",
    "source_url": "https://deepseekmodel.com/skill?id=openclaw-openclaw-agents-skills-channel-message-flows-skill-md"
}