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byted-bytehouse-data-asset-analyzer AI Agent Skill

View Source: bytedance/agentkit-samples

Medium

Installation

npx skills add bytedance/agentkit-samples --skill byted-bytehouse-data-asset-analyzer

12

Installs

ByteHouse 数据资产和血缘分析 Skill

🔵 ByteHouse 品牌标识

「ByteHouse」—— 火山引擎云原生数据仓库，极速、稳定、安全、易用

本Skill基于ByteHouse MCP Server，提供完整的数据资产盘点和血缘分析能力

描述

基于ByteHouse MCP Server，生成数据资产目录和血缘分析的技能。

当以下情况时使用此 Skill:
(1) 需要获取数据库表结构和字段信息
(2) 需要生成数据资产目录
(3) 需要分析表之间的血缘关系
(4) 用户提到"数据资产"、"血缘分析"、"表结构"、"字段分析"

前置条件

  • Python 3.8+
  • uv (已安装在 /root/.local/bin/uv)
  • ByteHouse MCP Server Skill - 本skill依赖 bytehouse-mcp skill提供的ByteHouse访问能力

依赖关系

本skill依赖 bytehouse-mcp skill，使用其提供的MCP Server访问ByteHouse。

确保 bytehouse-mcp skill已正确配置并可以正常使用。

📁 文件说明

  • SKILL.md - 本文件，技能主文档
  • data_asset_analyzer.py - 数据资产和血缘分析主程序
  • README.md - 快速入门指南

配置信息

ByteHouse连接配置

本skill复用 bytehouse-mcp skill的配置。请确保已在 bytehouse-mcp skill中配置好：

export BYTEHOUSE_HOST="<ByteHouse-host>"
export BYTEHOUSE_PORT="<ByteHouse-port>"
export BYTEHOUSE_USER="<ByteHouse-user>"
export BYTEHOUSE_PASSWORD="<ByteHouse-password>"
export BYTEHOUSE_SECURE="true"
export BYTEHOUSE_VERIFY="true"

🎯 功能特性

1. 完整Schema获取

  • 获取指定数据库的所有表
  • 获取每张表的所有字段
  • 提取表引擎、注释等元数据
  • 解析CREATE TABLE语句

2. 数据资产目录生成

  • 表统计（总表数、总列数）
  • 引擎分布统计
  • 自动标签生成
  • 表资产详情

3. 血缘分析

  • 表关系识别（Distributed → Local）
  • 列相似性分析
  • 关系可视化

🚀 快速开始

方法1: 运行数据资产和血缘分析

cd /root/.openclaw/workspace/skills/data-asset-analyzer

# 先设置环境变量（复用bytehouse-mcp的配置）
export BYTEHOUSE_HOST="<ByteHouse-host>"
export BYTEHOUSE_PORT="<ByteHouse-port>"
export BYTEHOUSE_USER="<ByteHouse-user>"
export BYTEHOUSE_PASSWORD="<ByteHouse-password>"
export BYTEHOUSE_SECURE="true"
export BYTEHOUSE_VERIFY="true"

# 运行分析工具
uv run data_asset_analyzer.py

分析内容包括：

  • 数据库完整schema（所有表和字段）
  • 数据资产目录（表统计、引擎分布、自动标签）
  • 血缘分析（表关系、列相似性）

输出文件（保存在 output/ 目录）：

  1. schema_{database}_{timestamp}.json - 完整的数据库schema
  2. catalog_{database}_{timestamp}.json - 数据资产目录
  3. lineage_{database}_{timestamp}.json - 血缘分析报告

💻 程序化使用

使用分析器模块

#!/usr/bin/env python3
# /// script
# dependencies = [
#   "mcp>=1.0.0",
# ]
# ///

import asyncio
import sys
import os

# 添加bytehouse-mcp skill的路径
BYTEHOUSE_MCP_PATH = os.path.join(
    os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
    "bytehouse-mcp"
)
sys.path.insert(0, BYTEHOUSE_MCP_PATH)

from data_asset_analyzer import DataAssetAnalyzer

async def main():
    analyzer = DataAssetAnalyzer()
    await analyzer.connect()
    
    # 分析数据库
    result = await analyzer.analyze_database("default")
    
    # result 包含:
    # - schema: 完整的数据库schema
    # - catalog: 数据资产目录
    # - lineage: 血缘分析
    # - files: 生成的文件路径

asyncio.run(main())

📊 输出文件说明

1. Schema文件 (schema_*.json)

包含数据库的完整结构：

{
  "database": "default",
  "analyzed_at": "2026-03-12T19:50:00",
  "tables": [
    {
      "name": "conversation_feedback",
      "comment": "",
      "engine": "Distributed",
      "columns": [
        {
          "name": "session_id",
          "type": "String",
          "comment": ""
        }
      ],
      "create_table_query": "CREATE TABLE ..."
    }
  ]
}

2. 数据资产目录 (catalog_*.json)

包含数据资产的统计信息：

{
  "database": "default",
  "generated_at": "2026-03-12T19:50:00",
  "summary": {
    "total_tables": 8,
    "total_columns": 45,
    "engines": {
      "Distributed": 4,
      "HaMergeTree": 3,
      "MergeTree": 1
    }
  },
  "tables": [
    {
      "name": "conversation_feedback",
      "comment": "",
      "engine": "Distributed",
      "column_count": 10,
      "columns": [...],
      "tags": ["distributed", "user-feedback"]
    }
  ]
}

3. 血缘分析 (lineage_*.json)

包含表关系和列相似性：

{
  "database": "default",
  "generated_at": "2026-03-12T19:50:00",
  "table_relationships": [
    {
      "source_table": "conversation_feedback",
      "relationships": [
        {
          "type": "distributed_to_local",
          "target_table": "conversation_feedback_local",
          "description": "Distributed表指向Local表"
        }
      ]
    }
  ],
  "column_similarities": [
    {
      "column_name": "session_id",
      "column_type": "String",
      "found_in_tables": [
        "conversation_feedback",
        "conversation_feedback_local"
      ]
    }
  ]
}

🏷️ 自动标签生成

分析器会根据表名和引擎自动生成标签：

标签 说明
merge-tree 使用MergeTree引擎
distributed 使用Distributed引擎
high-availability 使用HaMergeTree或HaUniqueMergeTree
log-table 表名包含"log"
user-feedback 表名包含"feedback"
local-table 表名以"_local"结尾
test-table 表名包含"test"

📚 更多信息

详细使用说明请参考 bytehouse-mcp skill

最后更新: 2026-03-12

Installs

Installs 12
Global Rank #601 of 601

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How to use this skill

1

Install byted-bytehouse-data-asset-analyzer by running npx skills add bytedance/agentkit-samples --skill byted-bytehouse-data-asset-analyzer in your project directory. Run the install command above in your project directory. The skill file will be downloaded from GitHub and placed in your project.

2

No configuration needed. Your AI agent (Claude Code, Cursor, Windsurf, etc.) automatically detects installed skills and uses them as context when generating code.

3

The skill enhances your agent's understanding of byted-bytehouse-data-asset-analyzer, helping it follow established patterns, avoid common mistakes, and produce production-ready output.

What you get

Skills are plain-text instruction files — not executable code. They encode expert knowledge about frameworks, languages, or tools that your AI agent reads to improve its output. This means zero runtime overhead, no dependency conflicts, and full transparency: you can read and review every instruction before installing.

Compatibility

This skill works with any AI coding agent that supports the skills.sh format, including Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider, and other tools that read project-level context files. Skills are framework-agnostic at the transport level — the content inside determines which language or framework it applies to.

Data sourced from the skills.sh registry and GitHub. Install counts and security audits are updated regularly.

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