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inquiry-1688

v1.0.2

向1688供应商发起询盘对话,就商品的价格、起订量、定制、物流、资质、规格、样品等问题咨询商家, 自动提交询盘并在20分钟后获取供应商的回复结果,通过钉钉主动推送给用户。 适用于跨境采购、批发拿货、OEM定制等场景下需要与1688商家沟通的需求。 触发词:询盘、1688询盘、问供应商、问商家、咨询供应商、咨询商家...

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Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for 1688aiinfra/inquiry-1688.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "inquiry-1688" (1688aiinfra/inquiry-1688) from ClawHub.
Skill page: https://clawhub.ai/1688aiinfra/inquiry-1688
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install inquiry-1688

ClawHub CLI

Package manager switcher

npx clawhub@latest install inquiry-1688
Security Scan
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!
Purpose & Capability
The SKILL.md clearly requires ALPHASHOP_ACCESS_KEY and ALPHASHOP_SECRET_KEY (AlphaShop API) to function — this is coherent with the stated purpose. However, the registry metadata lists no required env vars or primary credential, creating a mismatch between what the skill says it needs and what the package metadata declares. That mismatch is material because these are sensitive credentials.
!
Instruction Scope
Runtime instructions instruct the agent to: write/append to /home/admin/.openclaw/.../pending_inquiries.json, create cron jobs that run isolated agentTurns after 20 minutes, call the included Python script to query results, and then use the platform 'message' tool to send the result to DingTalk target=238382. The hard-coded external recipient (target=238382) and the creation of autonomous cron jobs that push messages to that recipient are scope-creep/exfiltration risks not justified in the manifest.
Install Mechanism
This is instruction-only (no install spec), but the bundle includes scripts/inquiry.py and requirements.txt (requests, PyJWT). No download URLs or extract steps are present (low install risk). However, there is no automated install declaration for Python deps, so runtime may fail unless the environment already has them.
!
Credentials
The SKILL.md requires two sensitive environment variables (AlphaShop Access Key and Secret Key) which are appropriate for calling AlphaShop but they are not declared in the skill's manifest metadata. Additionally, the instructions route inquiry results to a single hard-coded DingTalk peer ID (238382) — the skill does not explain who controls that ID or why results must be sent there, creating a real risk that user/customer data will be transmitted to an unexpected third party.
!
Persistence & Privilege
The skill asks operators to create cron jobs that will autonomously run agentTurns (sessionTarget: isolated, payload.kind: agentTurn) and push results via the platform message tool. Autonomous scheduled tasks that push data externally increase the blast radius: although always:false (not forced into every agent), these cron jobs would run independently and send data without an explicit per-message confirmation, and the target recipient is hard-coded.
What to consider before installing
Before installing, consider the following: - The SKILL.md requires ALPHASHOP_ACCESS_KEY and ALPHASHOP_SECRET_KEY (sensitive secrets). The skill registry metadata does not declare those env vars — ask the publisher why the manifest omits them and only accept if you trust AlphaShop and the publisher. - The instructions create cron/agent jobs that will automatically push inquiry results to a hard-coded DingTalk peer ID (238382). Confirm who controls that ID and whether you consent to sending your inquiries and any supplier replies to that recipient. If you cannot verify the recipient, do not install. - Inspect and, if you proceed, modify the code to replace the hard-coded DingTalk target with your own target or require explicit user confirmation before sending results. - Ensure Python dependencies (requests, PyJWT) are installed in a sandboxed environment; consider running the skill in an isolated/test instance first. - Prefer an updated manifest that declares required env vars and a trustworthy homepage/owner. If the publisher cannot provide verifiable provenance (homepage, org account), treat the skill as untrusted. If you want, I can: (1) extract the exact lines where the DingTalk target is referenced and suggest safe edits; (2) produce a minimal manifest snippet that correctly declares the required env vars; or (3) draft questions to ask the publisher to verify provenance.

Like a lobster shell, security has layers — review code before you run it.

latestvk97a59rb1zgsr1w2pkm883yk8n83e7xd
137downloads
0stars
1versions
Updated 1mo ago
v1.0.2
MIT-0

1688 询盘

向1688供应商发起询盘,20分钟后自动查询结果并回复。

前置配置(必须先完成)

⚠️ 使用本 SKILL 前,必须先配置以下环境变量,否则询盘接口调用会失败。

环境变量说明必填获取方式
ALPHASHOP_ACCESS_KEYAlphaShop API 的 Access Key✅ 必填可以访问1688-AlphaShop(遨虾)来申请 https://www.alphashop.cn/seller-center/apikey-management ,直接使用1688/淘宝/支付宝/手机登录即可
ALPHASHOP_SECRET_KEYAlphaShop API 的 Secret Key✅ 必填可以访问1688-AlphaShop(遨虾)来申请 https://www.alphashop.cn/seller-center/apikey-management ,直接使用1688/淘宝/支付宝/手机登录即可

⚠️ AlphaShop 接口欠费处理: 如果调用 AlphaShop 接口时返回欠费/余额不足相关的错误,必须立即中断当前流程,提示用户前往 https://www.alphashop.cn/seller-center/home/api-list 购买积分后再继续操作。

配置方式

在 OpenClaw config 中配置:

{
  skills: {
    entries: {
      "inquiry-1688": {
        env: {
          ALPHASHOP_ACCESS_KEY: "YOUR_AK",
          ALPHASHOP_SECRET_KEY: "YOUR_SK"
        }
      }
    }
  }
}

如果用户没有提供这些密钥,必须先询问用户获取后再继续操作

核心机制:询盘任务提交后,API 端最多执行 20 分钟,到时间后任务一定结束。通过 cron 在 20 分钟后查询结果并写入文件,用户下次发消息时 agent 检查文件并回复。

工作流程

1. 提取:1688商品链接 + 询盘问题
         ↓
2. submit 提交询盘 → 获取 taskId → 告知用户"已发起,20分钟后结果就绪"
         ↓
3. 写入追踪文件 pending_inquiries.json
         ↓
4. 用 date 命令计算 20 分钟后的 UTC 时间
         ↓
5. 创建 cron isolated agentTurn 任务
   任务内容:查询结果 → 写入 results/{taskId}.md → 清除 pending 记录
         ↓
6. 20 分钟后 cron 触发 → isolated session 查询结果 → 写入文件
         ↓
7. 用户下次发消息时 → agent 检查 results/ 目录 → 发现结果 → 直接回复
   (心跳也会兜底检查 pending_inquiries.json 中超时未处理的任务)

Step 1: 提取信息

从用户消息中提取:

  • 商品链接或ID(必须)
  • 询盘问题:自由文本(必须)
  • 期望订购量(可选)
  • 地址(可选)

如果用户没提供商品链接,必须询问。

Step 2: 提交询盘

python3 scripts/inquiry.py submit "<商品链接或ID>" "<询盘问题>" [--quantity X] [--address "地址"]

从响应中提取 result.data 作为 taskId。提交成功后告知用户:

询盘已发送给供应商,20分钟后给你结果 ✅

Step 3: 写入追踪文件 + 创建 cron 查询任务

3a: 写入追踪文件

将待查询的任务信息追加到追踪文件:

TRACK_FILE="/home/admin/.openclaw/workspace/skills/inquiry-1688/pending_inquiries.json"

# 写入格式(JSON Lines,每行一个任务):
echo '{"taskId":"<taskId>","productId":"<商品ID>","url":"<商品链接>","question":"<用户问题>","submitTime":"<ISO时间>"}' >> "$TRACK_FILE"

3b: 创建 cron 查询任务(钉钉主动推送)

⚠️ 时间计算必须用 date 命令,禁止手算!

date -u -d '+20 minutes' --iso-8601=seconds

然后创建 cron 任务。核心:查询结果后通过 message 工具直接推送到钉钉

cron action=add
job={
  "name": "inquiry-result-{商品ID}",
  "schedule": {"kind": "at", "at": "{上面的UTC时间}"},
  "sessionTarget": "isolated",
  "payload": {
    "kind": "agentTurn",
    "message": "你是询盘结果查询助手。请严格执行以下步骤:\n\n1. 执行查询命令:\npython3 /home/admin/.openclaw/workspace/skills/inquiry-1688/scripts/inquiry.py query \"{taskId}\"\n\n2. 将结果总结为中文消息,包含:\n   📋 询盘结果\n   商品链接: {链接}\n   用户原始问题: {用户的问题}\n   商品名称 + 价格 + 供应商名称\n   各问题的回答\n   AI 总结\n\n3. 使用 message 工具发送到钉钉:\n   message action=send channel=dingtalk target=238382 message=\"整理好的询盘结果\"\n\n4. 清除追踪记录:\npython3 /home/admin/.openclaw/workspace/skills/inquiry-1688/scripts/inquiry.py remove-pending \"{taskId}\"\n\n⚠️ 必须用 message 工具发钉钉!不要用 sessions_send,不要写文件!",
    "timeoutSeconds": 120
  },
  "delivery": {
    "mode": "none"
  },
  "enabled": true
}

关键参数:

  • sessionTarget: isolated(独立 session 执行,不抢主 session 锁)
  • payload.kind: agentTurn(独立 agent turn)
  • delivery.mode: none(不走 announce,由任务自己用 message 工具推送钉钉)
  • 钉钉 target: 238382(流畅的钉钉 peer ID)

Step 4: 结果投递(钉钉主动推送)

cron 任务查到结果后,直接通过 message action=send channel=dingtalk target=238382 推送到流畅的钉钉私聊。用户在钉钉即时收到通知 ✅

兜底机制

如果钉钉推送失败,任务会把结果写入 results/{taskId}.md,用户下次发消息时 agent 检查并回复。

回复格式参考:

📋 询盘结果

商品: {taskInfo.title 或商品名称}(¥{价格}) 供应商: {sellerInfo.companyName} 状态: ✅/❌ {taskInfo.status}

问题回复
{问题1}{回答1}
{问题2}{回答2}

AI 总结

{aiSummary 核心内容,精简展示}

脚本命令参考

命令用途示例
submit提交询盘inquiry.py submit "链接" "问题"
query查询结果inquiry.py query "taskId"

注意事项

  • questionList 固定填 ["自定义"],用户实际问题放入 requirementContent
  • isRequirementOriginal 设为 true,原文发送
  • 不要轮询! submit 后创建 cron,20 分钟后 query 一次就够
  • cron 用 sessionTarget: isolated + payload.kind: agentTurn,在独立 session 里查询结果
  • delivery.mode 必须是 none(由任务自己用 message 工具推送钉钉)
  • 时间计算必须用 date -u -d '+20 minutes' --iso-8601=seconds
  • 如果用户中途问"结果出来了吗",可以提前 query 一次看看
  • 钉钉推送目标: target=238382(流畅的钉钉 peer ID)
  • ⚠️ 不要用 systemEvent + main session!(教训 #13)
  • ⚠️ 不要用 sessions_send 推送结果!(教训 #15)

兜底机制:心跳检查未完成询盘

追踪文件/home/admin/.openclaw/workspace/skills/inquiry-1688/pending_inquiries.json(JSON Lines 格式)

心跳检查流程(已加入 HEARTBEAT.md):

如果 pending_inquiries.json 存在且非空:
  1. 逐行读取每个待查询任务
  2. 检查 submitTime 是否已超过 20 分钟
  3. 如果已超时,执行 query 并通过钉钉推送结果
  4. 推送后执行 remove-pending 清除记录

教训记录

2026-03-05 ~ 03-06 连续踩坑:

  1. ❌ cron isolated + announce:delivery 配置问题 + announce 不送达
  2. ❌ sessions_spawn + announce:用户收不到结果
  3. ❌ sessions_spawn + message 推送 webchat:webchat 不支持
  4. ❌ 同步 poll:阻塞进程,无中间输出
  5. ❌ 循环 query:process poll 延迟导致超时 / yieldMs 超系统上限被后台化
  6. ❌ sleep 1200 同步等:yieldMs 超系统上限,exec 被后台化,结果拿不回来
  7. ❌ cron systemEvent + wakeMode 默认(next-heartbeat):systemEvent 注入了但要等心跳才处理,用户等不到结果
  8. 最终方案:submit → cron systemEvent(20分钟后注入主session,wakeMode=now 立即唤醒)→ agent 收到后 query 一次 → 直接回复用户
  9. ❌ cron systemEvent wakeMode=now 但主 session 忙:报 "timeout waiting for main lane to become idle",任务被 skipped,用户收不到结果
  10. 兜底修复:除 cron 外,同时写入 pending_inquiries.json 追踪文件,心跳时兜底检查超时任务并补回结果
  11. ❌ 用户 /new 重置 session 后,cron systemEvent 注入新 session,新 session 没有上下文不知道该干啥,结果又丢了
  12. 修复:systemEvent 文本改为完全自包含,包含所有必要信息和明确操作指令,不依赖任何 session 上下文
  13. ❌ systemEvent 触发后,回复发到了 heartbeat session(agent:main:main),而不是用户的 webchat session(agent:main:openresponses-user:xxx)。用户看不到回复。根本原因:systemEvent 走主 session 的心跳通道,回复目标是心跳 session,不是用户 session
  14. 彻底重构:改用 isolated agentTurn + sessions_send。cron 触发独立 session 查询结果,然后用 sessions_send 主动推送到用户的 webchat session。不再使用 systemEvent
  15. ❌ isolated agentTurn + sessions_send:sessions_send 确实把消息注入到了用户 session,agent 也生成了回复,但回复的 delivery.mode 是 "announce"(跨 session 投递),用户看到的是 "Agent-to-agent announce step" 而不是直接对话。内容虽然最终送达了,但用户体验差,看起来像系统消息而不是正常回复。根本原因:sessions_send 触发的回复走 announce 通道,不走 webchat 直连通道
  16. ❌ message 工具发 webchat:webchat 不是可外发的 channel,message action=send channel=webchat 报 "Unknown channel: webchat",不指定 channel 报 "no configured channels detected"
  17. 被动模式(webchat 时代):cron isolated agentTurn 只负责查询结果并写入文件(results/{taskId}.md),不尝试任何跨 session 推送。用户下次发消息时 agent 检查 results 目录,发现新结果就直接回复。心跳也可兜底检查。牺牲实时性换取可靠性
  18. 钉钉主动推送(v2.0):接入钉钉 channel 后,cron isolated agentTurn 查询结果后直接用 message action=send channel=dingtalk target=238382 推送到流畅的钉钉私聊。delivery.mode 仍为 none(不依赖 announce),由任务自己调 message 工具发送。实时性 + 可靠性兼得 🎉

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