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Byted Bytehouse Diagnostics

Name: Byted Bytehouse Diagnostics
Author: volcengine-skills

作者 volcengine-skills · GitHub ↗ · v1.0.0 · MIT-0

cross-platform ⚠ suspicious

总下载

当前安装

版本数

在 OpenClaw 中安装

/install byted-bytehouse-diagnostics

功能描述

ByteHouse集群诊断和健康检查工具，用于检查ByteHouse集群健康状态、诊断集群问题和异常、查看集群节点状态、分析集群性能指标。当用户需要检查ByteHouse集群健康状态、诊断集群问题和异常、查看集群节点状态、分析集群性能指标时，使用此Skill。

使用说明 (SKILL.md)

ByteHouse 诊断集群 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，提供完整的集群诊断和健康检查能力

描述

ByteHouse集群诊断和健康检查工具。

当以下情况时使用此 Skill: (1) 需要检查ByteHouse集群健康状态 (2) 需要诊断集群问题和异常 (3) 需要查看集群节点状态 (4) 需要分析集群性能指标 (5) 用户提到"集群诊断"、"健康检查"、"节点状态"、"集群问题"

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
cluster_diagnostics.py - 集群诊断主程序
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="\x3CByteHouse-host>"
export BYTEHOUSE_PORT="\x3CByteHouse-port>"
export BYTEHOUSE_USER="\x3CByteHouse-user>"
export BYTEHOUSE_PASSWORD="\x3CByteHouse-password>"
export BYTEHOUSE_SECURE="true"
export BYTEHOUSE_VERIFY="true"

🎯 功能特性

1. 集群健康检查

检查集群节点状态
检查副本同步状态
检查数据分区状态
检查系统表完整性

2. 节点状态诊断

获取集群节点列表
检查节点存活状态
查看节点资源使用情况
分析节点性能指标

3. 查询历史分析

查询执行历史统计
慢查询识别
查询错误分析
查询性能趋势

4. 系统表检查

检查system.parts表
检查system.replicas表
检查system.clusters表
检查system.mutations表

🚀 快速开始

方法1: 运行集群健康检查

cd /root/.openclaw/workspace/skills/bytehouse-diagnostics

# 先设置环境变量（复用bytehouse-mcp的配置）
export BYTEHOUSE_HOST="\x3CByteHouse-host>"
export BYTEHOUSE_PORT="\x3CByteHouse-port>"
export BYTEHOUSE_USER="\x3CByteHouse-user>"
export BYTEHOUSE_PASSWORD="\x3CByteHouse-password>"
export BYTEHOUSE_SECURE="true"
export BYTEHOUSE_VERIFY="true"

# 运行集群诊断
uv run cluster_diagnostics.py

诊断内容包括：

集群健康状态
节点状态检查
副本同步状态
数据分区检查
查询历史分析
系统表完整性检查

输出文件（保存在 output/ 目录）：

health_check_{timestamp}.json - 健康检查报告
node_status_{timestamp}.json - 节点状态报告
query_stats_{timestamp}.json - 查询统计报告

💻 诊断检查项

健康检查项

检查项	说明	状态
集群连接	测试ByteHouse连接性	✅/❌
系统表访问	检查system.*表是否可访问	✅/❌
副本状态	检查数据副本同步状态	✅/⚠️/❌
分区状态	检查数据分区完整性	✅/⚠️/❌
节点存活	检查集群节点存活状态	✅/❌
Mutation状态	检查mutation执行状态	✅/⚠️/❌

诊断指标

集群级别: 总节点数、活跃节点数、副本数、分区数
节点级别: CPU使用率、内存使用率、磁盘使用率、查询数
查询级别: 总查询数、慢查询数、错误查询数、平均查询时间

📊 诊断报告示例

健康检查报告

{
  "cluster_name": "bh_log_boe",
  "check_time": "2026-03-12T21:00:00",
  "overall_status": "healthy",
  "checks": [
    {
      "name": "cluster_connection",
      "status": "pass",
      "message": "成功连接到ByteHouse"
    }
  ]
}

📚 更多信息

详细使用说明请参考 bytehouse-mcp skill

最后更新: 2026-03-12

安全使用建议

This skill appears to implement ByteHouse diagnostics, but it has several red flags you should consider before installing or running it: 1) It expects BYTEHOUSE_HOST/PORT/USER/PASSWORD but the registry metadata does not declare those required environment variables — verify and supply only the minimum needed. 2) The script copies your entire environment and passes it to a spawned subprocess that pulls code from GitHub via a hardcoded '/root/.local/bin/uvx' command — that can leak unrelated secrets and executes externally fetched code. Only run this in an isolated environment (non-root, no unrelated secrets in env) and after verifying the uvx binary path and contents. 3) Prefer to confirm the exact MCP server code the skill will fetch (review the referenced GitHub subdirectory) before allowing network pulls, or configure/use a known, locally installed bytehouse-mcp skill implementation instead. 4) If you must run it, audit the output and the downloaded code, and restrict environment variables (unset sensitive vars) so only ByteHouse credentials are present. If you cannot validate the fetched code or the local 'uvx' binary, treat the skill as untrusted.

功能分析

Type: OpenClaw Skill Name: byted-bytehouse-diagnostics Version: 1.0.0 The skill is a diagnostic tool for ByteHouse clusters that uses the Model Context Protocol (MCP) to perform health checks and analyze system metrics. The core logic in `scripts/cluster_diagnostics.py` connects to an official ByteHouse MCP server hosted on the Volcengine GitHub repository and executes standard SQL queries against system tables (e.g., `system.parts`, `system.mutations`) to generate JSON reports. The behavior is transparent, well-documented, and strictly aligned with the stated purpose of cluster diagnostics without any evidence of malicious intent or unauthorized data exfiltration.

能力评估

⚠ Purpose & Capability

The skill claims to depend on a 'bytehouse-mcp' skill and to reuse its configuration, which is reasonable. However the package/metadata declares no required env vars or primary credential while both SKILL.md and the script clearly require BYTEHOUSE_HOST/PORT/USER/PASSWORD. The script also directly invokes an MCP server implementation via a git+ URL rather than exclusively reusing an existing local MCP skill, which is inconsistent with the 'reuse bytehouse-mcp skill' claim.

⚠ Instruction Scope

The runtime instructions and script perform only diagnostic queries (list_databases, run_select_query), which is consistent with the purpose. But the Python script copies os.environ and passes it intact to a spawned stdio MCP server subprocess — that gives the subprocess access to all environment variables (potentially leaking unrelated secrets). The SKILL.md tells users to set BYTEHOUSE_* variables but the manifest does not declare them; the README and SKILL.md refer to a uv binary at /root/.local/bin/uv, while the script hardcodes '/root/.local/bin/uvx' — an inconsistency that could cause unexpected behavior.

⚠ Install Mechanism

There is no install spec, but the script configures StdioServerParameters to run a command that pulls 'git+https://github.com/volcengine/mcp-server@main#subdirectory=server/mcp_server_bytehouse' (a remote git+ URL) through a hardcoded local binary '/root/.local/bin/uvx'. That effectively results in runtime download-and-execute of external code and depends on a specific local executable path. Runtime fetching of code from external sources increases supply-chain risk and is disproportionate for a simple diagnostic wrapper unless explicitly justified.

⚠ Credentials

The skill requests no env vars in metadata, but both documentation and the script require BYTEHOUSE_HOST/PORT/USER/PASSWORD and related flags. Worse, the script passes the entire process environment to the spawned MCP subprocess (env=os.environ.copy()), which could expose unrelated secrets (AWS keys, other service tokens) to the fetched server process. The number and sensitivity of env variables used are not declared in the registry metadata.

ℹ Persistence & Privilege

The skill does not request always:true and does not modify other skills or global configuration. Autonomous invocation is allowed (platform default) but is not by itself a new risk here. The main privilege concern is transient: executing a hardcoded local binary that downloads/executes remote code — this increases runtime privilege of whatever is run but is not expressed as persistent always-on presence.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install byted-bytehouse-diagnostics
安装完成后，直接呼叫该 Skill 的名称或使用 /byted-bytehouse-diagnostics 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v1.0.0

byted-bytehouse-diagnostics v1.0.0 - 首次发布，提供ByteHouse集群健康检查和诊断能力 - 支持集群节点状态检测、副本及数据分区一致性检查、系统表完整性校验 - 提供查询历史分析，包括慢查询、查询性能趋势和错误识别 - 诊断结果以报告形式输出，包含健康状态、节点指标及查询统计 - 依赖bytehouse-mcp skill，复用其ByteHouse连接配置

元数据

Slug byted-bytehouse-diagnostics

版本 1.0.0

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 1

常见问题