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研究检索敏感数据github未标认证来源可访问许可证需确认审计提醒

datahub-setup数据中心设置

Agent Skill

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。

总安装

1,398

周安装

56

GitHub Stars

19

下载量

452
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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
descriptionstringWhat 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_filterstringFilter 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.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

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

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 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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