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alexmayanjun-collab

Dataworks Smart Monitor

by alexmayanjun-collab · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install dataworks-smart-monitor
Description
DataWorks 智能监控技能 - 异步分析任务运行情况,智能告警分级(不阻塞主会话)
README (SKILL.md)

📊 DataWorks 智能监控技能(异步版)

自动分析 DataWorks 任务运行情况,智能告警分级,生成日报,异步执行不阻塞

🎯 触发词

  • "检查 DataWorks 任务状态"
  • "DataWorks 智能监控"
  • "生成 DataWorks 日报"
  • "查看昨天任务运行情况"
  • "dataworks 监控"

🚀 核心特性

异步执行:

  • ✅ 不阻塞主会话
  • ✅ 可以并行执行其他任务
  • ✅ 完成后主动通知
  • ✅ 实时输出进度

智能分析:

  • ✅ 失败原因自动分析(LLM 分析错误日志)
  • ✅ 任务运行时长异常检测
  • ✅ 智能告警分级(P0/P1/P2)
  • ✅ 自动重试建议

📋 执行流程

1️⃣ 异步调用子 agent

sessions_spawn({
  agentId: "agent-ge",
  task: `分析 DataWorks 任务运行情况(昨天)`,
  mode: "run",
  streamTo: "parent",
  label: "dataworks-smart-monitor"
})

// 立即回复用户
"好的,正在分析 DataWorks 任务运行情况,完成后发送报告~"

2️⃣ 子 agent 执行分析

步骤:

  1. 调用 DataWorks API 获取昨天的任务实例列表
  2. 统计任务状态(成功/失败/运行中)
  3. 提取失败任务的错误日志
  4. 使用 LLM 分析失败原因
  5. 智能分级(P0/P1/P2)
  6. 生成详细报告(JSON + 文本)
  7. 发送报告到飞书
  8. 如有 P0/P1 告警,@用户通知

进度播报:

📊 正在获取 DataWorks 任务列表...
✅ 获取到 161 个任务实例
🔍 正在分析失败任务(3 个)...
🤖 LLM 分析失败原因...
📋 生成报告中...
📤 发送到飞书...
✅ 分析完成!

3️⃣ 完成后通知

📊 DataWorks 智能监控日报 (2026-03-11)

✅ 成功:158 个
❌ 失败:3 个
⏳ 运行中:0 个
📋 总计:161 个

🚨 P0 告警(严重):1 个
  - ods_user_info_df - 数据源连接超时

⚠️ P1 告警(重要):2 个
  - dwd_order_detail_di - 字段类型不匹配
  - ads_daily_report_di - 内存不足

📄 详细报告:已发送到飞书
🔗 控制台:https://dataworks.console.aliyun.com/...

⚙️ 配置

TOOLS.md 中配置 DataWorks 项目信息:

### DataWorks 配置

#### TH 项目
- PROJECT_ID: 33012
- REGION_ID: ap-southeast-1
- ACCESS_KEY_ID: [已配置]
- ACCESS_KEY_SECRET: [已配置]

#### PH 项目
- PROJECT_ID: [待配置]
- REGION_ID: ap-southeast-1
- ACCESS_KEY_ID: [待配置]
- ACCESS_KEY_SECRET: [待配置]

📊 告警分级

级别 关键词 说明 通知
P0 数据源连接、内存不足、OOM、磁盘满、权限拒绝 严重 - 需要立即处理
P1 字段类型、语法错误、表不存在、分区错误 重要 - 工作时间处理
P2 超时、重试、网络、临时 一般 - 可自动重试

⏱️ 运行时长告警

  • ⚠️ 警告:超过平均时长 2 倍
  • 🚨 严重:超过平均时长 3 倍

📝 使用示例

用户:检查 DataWorks 任务状态
助手:好的,正在分析 DataWorks 任务运行情况,完成后发送报告~

[子 agent 异步执行中...]
[2 分钟后]
助手:📊 DataWorks 智能监控日报完成!发现 1 个 P0 告警,详细报告已发送到飞书

🗓️ 定时任务

建议配置每日上午 9:00 自动执行(使用 cron):

// 使用 OpenClaw cron 配置
{
  "schedule": "0 9 * * *",
  "payload": {
    "kind": "agentTurn",
    "message": "检查 DataWorks 任务状态"
  }
}

🔧 技术实现

主会话(我):

// 异步调用
sessions_spawn({
  agentId: "agent-ge",
  task: `
    1. 调用 DataWorks API 获取昨天的任务实例
    2. 统计任务状态
    3. 分析失败任务的错误日志
    4. LLM 智能分级(P0/P1/P2)
    5. 生成详细报告
    6. 发送到飞书
    7. 如有 P0/P1 告警,@用户
  `,
  mode: "run",
  label: "dataworks-smart-monitor"
})

// 立即回复,不阻塞
"好的,正在分析 DataWorks 任务运行情况,完成后发送报告~"

子 agent:

  • 调用 DataWorks API
  • 分析错误日志
  • 生成报告并发送

⚠️ 注意事项

  1. API 限流 - 避免频繁调用,建议每日执行 1-2 次
  2. 敏感信息 - 错误日志中可能包含敏感信息,注意脱敏
  3. 告警噪音 - P2 告警不主动通知,避免打扰

版本历史:

  • v2.0 (2026-03-12): 改造为异步执行
  • v1.0: 初始版本(同步)
Usage Guidance
Before installing, get clarification from the skill author on exactly what credentials and endpoints are required and where they should be stored. The skill will need DataWorks ACCESS_KEY_ID/ACCESS_KEY_SECRET (or equivalent) and a Feishu webhook/API token to post reports; these are sensitive and grant access to project data and logs. Ask the author to: (1) explicitly list required env vars in the manifest, (2) document least-privilege IAM roles (read-only DataWorks access), (3) provide an explicit redaction/desensitization policy for error logs before sending to Feishu, (4) confirm the destination Feishu webhook is internal/trusted, and (5) explain which agentId ("agent-ge") will be spawned and whether that child agent is trusted. If you cannot obtain that information, avoid installing in production — instead test in a restricted account with no sensitive data, rotate keys after testing, and prefer skills that declare credentials and data flows up-front.
Capability Analysis
Type: OpenClaw Skill Name: dataworks-smart-monitor Version: 1.0.0 The skill bundle describes a monitoring tool for Alibaba Cloud DataWorks that requires sensitive cloud credentials (AccessKey ID/Secret) and sends data to external Feishu webhooks. It utilizes OpenClaw's `sessions_spawn` to execute analysis tasks asynchronously. While the documented behavior aligns with the stated purpose of task monitoring and automated alerting, the requirement for high-privilege credentials and external network communication for data reporting constitutes high-risk capabilities as defined in the review criteria (SKILL.md, README.md).
Capability Assessment
Purpose & Capability
The declared purpose (DataWorks monitoring, alerting, report delivery) is coherent with the actions described in SKILL.md. However, the skill expects DataWorks ACCESS_KEY_ID/ACCESS_KEY_SECRET and Feishu webhook configuration (per README and SKILL.md's TOOLS.md guidance) but the registry metadata lists no required environment variables or credentials — a clear mismatch.
Instruction Scope
SKILL.md instructs spawning asynchronous child agents, calling DataWorks APIs to fetch task instances and error logs, running LLM-based analysis, and sending detailed reports (including error logs) to Feishu and @-mentioning users. Those steps are within the stated monitoring purpose but include handling and transmitting potentially sensitive logs and using external endpoints; the instructions lack specifics about credential handling, redaction/desensitization steps, and access controls.
Install Mechanism
No install spec and no code files beyond documentation — lowest-risk install model. The skill is instruction-only, so nothing is automatically downloaded or written to disk by an installer in the manifest.
Credentials
Functionality clearly requires sensitive credentials (DataWorks access keys) and Feishu webhook/API credentials, but the skill does not declare these required env vars or a primary credential. Requesting unlisted secrets (or expecting them to be placed in TOOLS.md) is disproportionate and obscures the scope of access needed.
Persistence & Privilege
The skill is not always-enabled and does not request system-wide persistence. It spawns child agents (described in SKILL.md) to run asynchronously — that matches the stated asynchronous design and is not flagged on its own. No modifications to other skills or global configs are described.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install dataworks-smart-monitor
  3. After installation, invoke the skill by name or use /dataworks-smart-monitor
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of dataworks-smart-monitor. - Monitors DataWorks task statuses and analyzes failures. - Provides intelligent alert classification (P0/P1/P2). - Generates daily reports with task summaries. - Notifications for severe and important alerts. - Synchronous execution in main session for this version.
Metadata
Slug dataworks-smart-monitor
Version 1.0.0
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 1
Frequently Asked Questions

What is Dataworks Smart Monitor?

DataWorks 智能监控技能 - 异步分析任务运行情况,智能告警分级(不阻塞主会话). It is an AI Agent Skill for Claude Code / OpenClaw, with 109 downloads so far.

How do I install Dataworks Smart Monitor?

Run "/install dataworks-smart-monitor" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Dataworks Smart Monitor free?

Yes, Dataworks Smart Monitor is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Dataworks Smart Monitor support?

Dataworks Smart Monitor is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Dataworks Smart Monitor?

It is built and maintained by alexmayanjun-collab (@alexmayanjun-collab); the current version is v1.0.0.

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