频道消息流

karanmrn2026-10-26查看 948复制 257

在运行 QA Lab 频道消息流证据时使用。

📘如何使用这个技能

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复制技能内容·点击上方「Copy SKILL.md」复制全部内容，或 Download 下载到本地
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打开你的 AI 客户端·支持 Claude Code、Trae、Cursor、ChatGPT、DeepSeek、Kimi 等任意 AI 助手
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粘贴为系统提示/知识文件·Claude Code：放入 .claude/skills/ 目录；普通对话：作为首条消息发送并说「请严格按这份技能文档执行」
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直接提出你的需求·AI 会按技能定义的工作流、格式和约束来处理，无需再解释背景

💡 提示：技能生效后，每次对话只需一句需求，AI 会按 SKILL.md 的流程执行。

SKILL.md1221 chars

---
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:

```bash
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:

```bash
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.

来源：skillsdirectory

频道消息流

📘如何使用这个技能

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