复制下面这句话,粘贴给 Claude Code、Codex、Cursor 等 AI 编程工具,它会读取安装说明并在你确认后完成安装。
请阅读 https://ai.atlankj.com/install/asset/gh-channel-message-flows-d72362b3711b ,按照其中的说明把「channel-message-flows」安装到你(当前 AI 工具)中。执行前先告诉我将运行的命令和写入的位置,等我确认。
查看 AI 将读取的安装说明正在读取 GitHub 原文…
内容来自 GitHub 原始文件,由原作者维护。在 GitHub 查看
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.
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
qa/scenarios/channels/channel-message-flows.yamlextensions/qa-channel/src/inbound.tsextensions/qa-lab/src/qa-transport.tsextensions/qa-lab/src/crabline-transport.tsextensions/telegram/src/draft-stream.tsThe scenario covers channels.streaming as primary evidence and
runtime.delivery as secondary evidence.