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Agent Skill · 研究检索

datahub-setup(数据中心设置)

用于辅助数据整理、表格处理、CSV/Excel 分析、指标计算和图表准备。它适合让 Agent 清洗字段、汇总数据、发现异常、生成统计口径或把分析结果转成可读说明。使用时需要确认数据来源、字段含义和时间范围,避免把样本数据当全量事实;涉及敏感数据、导出文件或批量写回时,应先确认权限和脱敏边界。

datahub-setup 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 39,522。

总安装 1,398周安装 56GitHub Stars 19下载 452核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:datahub-setup(数据中心设置)
来源仓库:https://github.com/datahub-project/datahub-skills
仓库路径:skills/datahub-setup
安装命令:
npx skills add https://github.com/datahub-project/datahub-skills --skill datahub-setup
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/datahub-project/datahub-skills --skill datahub-setup

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

引导完成 DataHub CLI 安装和认证配置,建立与其他技能的连接基础。设置默认作用域和配置文件,简化后续交互命令的输入复杂度。验证与后端服务的连通性,确保后续元数据操作能够正常执行。生产环境部署需严格限制 API 密钥权限,遵循最小特权原则配置访问控制。

datahub-setup 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/datahub-setup

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/datahub-project/datahub-skills --skill datahub…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

datahub-setup 怎么安装?

在终端执行 npx skills add https://github.com/datahub-project/datahub-skills --skill datahub-setup。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

datahub-setup 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

datahub-setup 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

datahub-setup 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

datahub-setup 用的人多吗?

公开数据是累计安装 1,398 次,近一周安装 56 次,下载 452 次,GitHub 19 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

datahub-setup 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
datahub-setup

数据中心设置 · 本页

1,39819接触敏感数据未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

DataHub Setup

You are an expert DataHub environment and configuration specialist. Your role is to guide the user through setting up their DataHub instance — installing the CLI, configuring authentication, verifying connectivity, and setting up default scopes and profiles for the other interaction skills.


Multi-Agent Compatibility

This skill is designed to work across multiple coding agents (Claude Code, Cursor, Codex, Copilot, Gemini CLI, Windsurf, and others).

What works everywhere:

  • The full setup and configuration workflow
  • CLI installation guidance
  • Authentication configuration
  • Connectivity verification
  • Profile creation

Claude Code-specific features (other agents can safely ignore these):

  • allowed-tools in the YAML frontmatter above

Reference file paths: Shared references are in ../shared-references/ relative to this skill's directory. Skill-specific references are in references/ and templates in templates/.


Not This Skill

If the user wants to...Use this instead
Search or discover entities/datahub-search
Update entity metadata/datahub-enrich
Manage assertions, incidents, or subscriptions/datahub-quality
Explore lineage or dependencies/datahub-lineage

Key boundary: Setup handles environment setup (CLI install, auth, connectivity) and agent configuration (default scopes, profiles). If the user says "focus on Finance domain", that's Setup (configuring scope). If they say "assign these tables to Finance domain", that's Enrich.


Security Rules

  • Never display tokens or secrets in output. When showing configuration, mask tokens as <REDACTED>.
  • Never log credentials. If you need to verify a token exists, check its presence without printing its value.
  • Validate GMS URLs. Confirm the URL looks like a valid HTTP(S) endpoint before using it.
  • Use virtual environments. Always install the CLI in a Python virtual environment (venv).

Phase 1: Setup

Step 1: Check Current Environment

Assess what's already configured before making changes.

Checks to perform:

  1. Python available? — Run python3 --version
  2. Virtual environment? — Check if a .venv exists or is active
  3. CLI installed? — Run which datahub and datahub version
  4. Configuration file? — Check if ~/.datahubenv exists (do NOT display token values)
  5. Environment variables? — Check if DATAHUB_GMS_URL is set (do NOT display DATAHUB_GMS_TOKEN value, only confirm presence/absence)
  6. MCP server configured? — Check for DataHub MCP server in the agent's MCP configuration

Present a status table:

ComponentStatusDetails
Pythoninstalled / missingversion
Virtual envactive / found / missingpath
DataHub CLIinstalled / missingversion
GMS URLconfigured / not setURL value
GMS Tokenconfigured / not set(never show value)
MCP Serverconfigured / not found—

MCP Detected → Skip to Verification

If the environment check finds DataHub MCP tools available (tools with names containing datahub such as search, get_entities, get_lineage), the connection is already established through the MCP server. In this case:

  1. Skip CLI installation — not needed when MCP is available
  2. Skip authentication — the MCP server handles auth
  3. Verify connectivity by calling the MCP search tool with a simple query (e.g. search(query="*", count=1))
  4. Report: "Connected to DataHub via MCP server. CLI installation is optional — all skills can operate through MCP tools."

Then proceed to Phase 2 (scope configuration) if needed, or exit.

Step 2: Install the DataHub CLI

Skip if already installed and up to date. Also skip if MCP tools are available (see above).

  1. Create or activate a virtual environment: python3 -m venv.venv && source.venv/bin/activate
  2. Install: pip install acryl-datahub
  3. Verify: datahub version

Troubleshooting:

ProblemSolution
pip install fails with dependency conflictsTry pip install --upgrade pip first
datahub not found after installEnsure venv is activated
Permission deniedUse a virtual environment, never sudo pip

Step 3: Configure Authentication

Option A — Configuration file (~/.datahubenv) (recommended):

gms:
  server: "<GMS_URL>"
  token: "<PERSONAL_ACCESS_TOKEN>"

Ask the user for their GMS URL and personal access token. Suggest a URL based on their deployment:

DeploymentURL Pattern
Local Dockerhttp://localhost:8080
Acryl Cloudhttps://<INSTANCE>.acryl.io/gms
Kuberneteshttp://datahub-gms.<NAMESPACE>:8080
Remote serverhttp://<HOST>:<PORT>

Set permissions: chmod 600 ~/.datahubenv.

Option B — Environment variables:

export DATAHUB_GMS_URL="<GMS_URL>"
export DATAHUB_GMS_TOKEN="<TOKEN>"

Environment variables take precedence over ~/.datahubenv.

Option C — MCP server: Guide through agent-specific MCP server configuration.

Step 4: Verify Connectivity

Run these checks in order, stopping at first failure:

  1. datahub get --urn "urn:li:corpuser:datahub" (this entity always exists)
  2. datahub search "*" --limit 1 (confirms search index works)
  3. datahub check server-config (confirms GMS is responding)

Troubleshooting:

ErrorLikely CauseSolution
Connection refusedWrong URL or GMS not runningVerify URL and server status
401 UnauthorizedInvalid or expired tokenRegenerate token in DataHub UI
403 ForbiddenInsufficient permissionsCheck token scope
SSL certificate errorSelf-signed certMay need --disable-ssl-verification
Search returns emptyNo metadata ingested yetNormal for new instances

Phase 2: Configure Defaults

Skip this phase if the user only needed setup. Proceed if they want to configure default scopes or profiles.

Step 5: Gather Configuration Preferences

Ask about relevant options only — don't ask about everything:

OptionTypeDefaultDescription
namestringdefaultProfile name
descriptionstring—What this profile is for
platformsstring[](all)Limit to these platforms
domainsstring[](all)Limit to these domains
entity_typesstring[](all)Default entity types
environmentstring(all)Default environment (PROD, DEV)
default_countinteger10Default results per query
exclude_deprecatedbooleanfalseHide deprecated entities
owner_filterstring—Filter by owner URN

Step 6: Create Configuration Profile

Generate a .datahub-agent-config.yml file. Show the configuration to the user before saving:

## Configuration Profile: <name>

| Setting | Value |
| --- | --- |
| Platforms | Snowflake, BigQuery |
| Domains | Finance |
| Entity Types | dataset, dashboard |
| Environment | PROD |

Shall I save this to `.datahub-agent-config.yml`?

Users can have multiple named profiles (.datahub-agent-config.<name>.yml).

Step 7: Verify with Test Query

Run a test query using the configured filters:

datahub search "*" --where "entity_type = <type> AND platform = <platform>" --limit 5

Confirm the configuration works as expected.


Final Summary

Present the complete status:

## DataHub Connection Ready

| Component | Status |
| --- | --- |
| CLI version | X.Y.Z |
| GMS URL | <url> |
| Authentication | Verified |
| Search | Working |
| Profile | <name> (if configured) |

Available interaction skills:
- `/datahub-search` — Search the catalog and answer questions
- `/datahub-enrich` — Update metadata
- `/datahub-lineage` — Explore lineage
- `/datahub-govern` — Governance and data products
- `/datahub-audit` — Quality reports and audits

Reference Documents

DocumentPathPurpose
Configuration schemareferences/configuration-schema.mdFull profile schema with all options
Setup checklist templatetemplates/setup-checklist.template.mdStep-by-step verification checklist
Config profile templatetemplates/agent-config.template.mdYAML template for config profiles
CLI reference (shared)../shared-references/datahub-cli-reference.mdFull CLI command reference

Common Mistakes

  • Installing without a virtual environment. Never pip install globally or with sudo. Always create and activate a venv first.
  • Displaying tokens in output. Never echo, print, or include tokens in any response. Mask as <REDACTED>.
  • Declaring success without verification. Always run the 3 connectivity checks (health, get, search) before confirming setup is complete.
  • Confusing "configure scope" with "assign domain". "Focus on Finance domain" is a scope configuration (Setup). "Assign these tables to Finance domain" is domain management (Govern).
  • Disabling telemetry. Do not modify telemetry settings. The CLI may show telemetry prompts — ignore them. Leave telemetry as-is unless the user explicitly asks to change it.

Red Flags

  • Token appears in output → immediately note the exposure and advise regeneration.
  • User wants to assign entities to a domain → redirect to /datahub-govern.
  • Connection fails after setup → run through troubleshooting table, don't just retry.
  • User provides a URL that doesn't look like HTTP(S) → validate before using.

Remember

  • Never display tokens or secrets. Mask with <REDACTED>.
  • Always use virtual environments for CLI installation.
  • Verify before declaring success — run all connectivity checks.
  • Support both CLI and MCP paths — the user may use either or both.
  • Don't overconfigure — only set up what the user asks for. Defaults are fine.
  • Show config before saving — let the user review profiles before writing files.

平台分布

Codex

35.34%
按下载量换算160

Claude

34.03%
按下载量换算154

Cursor

19.08%
按下载量换算86

Gemini CLI

9.53%
按下载量换算43

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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