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Agent Skill · 研究检索

fabric-cli-core(fabric CLI core 搜索)

fabric-cli-core 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

fabric-cli-core 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,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 41,143。

总安装 1,212周安装 51GitHub Stars 117下载 424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:fabric-cli-core(fabric CLI core 搜索)
来源仓库:https://github.com/microsoft/fabric-cli
仓库路径:skills/fabric-cli-core
安装命令:
npx skills add https://github.com/microsoft/fabric-cli --skill fabric-cli-core
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/microsoft/fabric-cli --skill fabric-cli-core

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。

fabric-cli-core 提供 Microsoft Fabric CLI 的安全一致默认配置,适用于通过 fab 命令操作 Fabric 的各类场景。适合在 Codex、Claude、Cursor、Gemini CLI 中执行自动化脚本、健康检查及路径实体管理任务。

需先安装 fabric-cli-core,再结合具体脚本使用;建议确认权限与维护状态后再运行命令。涉及联网或文件操作时,应评估安全风险并限制最小必要权限范围。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/fabric-cli-core

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/microsoft/fabric-cli --skill fabric-cli-core;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

fabric-cli-core 怎么安装?

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

fabric-cli-core 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。

fabric-cli-core 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。

fabric-cli-core 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。

fabric-cli-core 用的人多吗?

公开数据是累计安装 1,212 次,近一周安装 51 次,下载 424 次,GitHub 117 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。

fabric-cli-core 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。

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
fabric-cli-core

fabric CLI core 搜索 · 本页

1,212117接触敏感数据未明确通过

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

Fabric CLI Core

This skill defines safe, consistent defaults for an AI agent helping users operate Microsoft Fabric via the Fabric CLI (fab).

1 - Fabric CLI mental model (paths and entities)

Automation Scripts

Ready-to-use Python scripts for core CLI tasks. Run any script with --help for full options.

ScriptPurposeUsage
health_check.pyVerify CLI installation, auth status, and connectivitypython scripts/health_check.py [--workspace WS]

Scripts are located in the scripts/ folder of this skill.

Paths and Entities

  • Treat Fabric as a filesystem-like hierarchy with consistent dot (.) entity suffixes in paths (e.g., .Workspace, .Folder, .SemanticModel).
  • The hierarchy structure is:

- Tenant: The top-level container for everything. - Workspace: Personal or team workspace holding folders, items, and workspace-level elements. - Folder: Container for organizing items within a workspace (supports ~10 levels of nesting). - Item: Individual resource within a workspace or folder (e.g., Notebook, SemanticModel, Lakehouse). - OneLakeItem: OneLake storage item residing within a Lakehouse (tables, files, etc.).

  • Prefer and generate paths like:

- /Workspace1.Workspace/Notebook1.Notebook - /Workspace1.Workspace/FolderA.Folder/SemanticModel1.SemanticModel - /Workspace1.Workspace/FolderA.Folder/lh1.Lakehouse/Tables (OneLakeItem)

  • When a user provides an ambiguous identifier, ask for the full path (or infer with stated assumptions).

2 - Modes (interactive vs command line)

  • Be explicit about which mode a user is in:

- Interactive mode behaves like a REPL and runs commands without the fab prefix. - Command line mode runs one command per invocation and is best for scripts/automation.

  • The selected mode is preserved between sessions. If a user exits and logs back in, the CLI resumes in the same mode last used.
  • When you provide instructions, show commands in command line mode unless the user says they're in interactive mode.

3 - Authentication (public-safe guidance)

  • Prefer these auth patterns and do not invent new flows:

1. Interactive user: fab auth login (browser/WAM where supported). 2. Service principal (secret/cert): use environment variables / secure mechanisms; avoid embedding secrets in files. 3. Service principal (federated credential): use the federated token environment variable (FAB_SPN_FEDERATED_TOKEN) and do not persist the raw token. 4. Managed identity: supported for Azure-hosted workloads; no credentials required.

  • Never ask users to paste secrets into chat or print them back.

4 - Sensitive data handling (strict)

  • Never log or output tokens, passwords, client secrets, or raw federated tokens.
  • Validate all user inputs that could affect security:

- Paths: Sanitize file paths and API parameters. - GUIDs: Validate resource identifiers before use. - JSON: Validate JSON inputs for proper format.

  • If a user shares sensitive strings, advise rotating/regenerating them and moving to secure storage.

5 - Hidden entities and discovery

  • Hidden entities are special resources not normally visible, following a dot-prefixed naming convention (similar to UNIX hidden files).
  • Tenant-level hidden entities (accessed from root):

- .capacities — fab ls.capacities / fab get.capacities/<name>.Capacity - .gateways — fab ls.gateways / fab get.gateways/<name>.Gateway - .connections — fab ls.connections / fab get.connections/<name>.Connection - .domains — fab ls.domains / fab get.domains/<name>.Domain

  • Workspace-level hidden entities (accessed within a workspace):

- .managedidentities — fab ls ws1.Workspace/.managedidentities - .managedprivateendpoints — fab ls ws1.Workspace/.managedprivateendpoints - .externaldatashares — fab ls ws1.Workspace/.externaldatashares - .sparkpools — fab ls ws1.Workspace/.sparkpools

  • To show hidden resources, recommend ls -a / ls --all.

6 - Errors and troubleshooting guidance

  • When describing failures, include:

- What the command was trying to do - The likely cause - The next actionable step

  • If the CLI surfaces an error code/message, keep it intact and do not paraphrase away the key identifiers. (Fabric CLI emphasizes stable error codes/messages.)
  • Include request IDs for API errors to aid debugging when available.

7 - Output conventions for the agent

  • Default to concise, runnable steps.
  • When recommending commands, include:

- Preconditions (auth, correct workspace/path) - Expected result - How to verify (e.g., follow-up fab ls / fab get)

8 - Safety defaults

  • Ask before suggesting commands that delete, overwrite, or change access/permissions.
  • If the user explicitly confirms, proceed with a clear rollback note when possible.

9 - Platform and troubleshooting reference

  • Supported platforms: Windows, Linux, macOS.
  • Supported shells: zsh, bash, PowerShell, cmd (Windows command prompt).
  • Python versions: 3.10, 3.11, 3.12, 3.13.
  • CLI file storage (useful for troubleshooting):

- Config files are stored in ~/.config/fab/: - cache.bin — encrypted auth token cache - config.json — non-sensitive CLI settings - auth.json — non-sensitive auth info - context-<session_id> — path context for command-line mode sessions - Debug logs are written to: - Windows: %AppData%/fabcli_debug.log - macOS: ~/Library/Logs/fabcli_debug.log - Linux: ~/.local/state/fabcli_debug.log

10 - Critical operational rules

  • First run: Always run fab auth status to verify authentication before executing commands. If not authenticated, ask the user to run fab auth login.
  • Learn before executing: Always use fab --help and fab <command> --help the first time you use a command to understand its syntax.
  • Start simple: Try the basic fab command alone first before piping or chaining.
  • Non-interactive mode: Use fab in command-line mode when working with coding agents. Interactive mode doesn't work with automation.
  • Force flag: Use -f when executing commands if the flag is available to run non-interactively (skips confirmation prompts).
  • Verify before acting: If workspace or item name is unclear, ask the user first, then verify with fab ls or fab exists before proceeding.
  • Permission errors: If a command is blocked by permissions, stop and ask the user for clarification; never try to circumvent it.

11 - Common item types

ExtensionDescription
.WorkspaceWorkspace container
.FolderFolder within workspace
.SemanticModelPower BI dataset/semantic model
.ReportPower BI report
.DashboardPower BI dashboard
.NotebookFabric notebook
.LakehouseLakehouse
.WarehouseData warehouse
.DataPipelineData pipeline
.SparkJobDefinitionSpark job definition
.EventstreamReal-time event stream
.KQLDatabaseKQL database
.MLModelML model
.MLExperimentML experiment
.CapacityFabric capacity (hidden)
.GatewayData gateway (hidden)
.ConnectionConnection (hidden)

Use fab desc.<ItemType> to explore any item type.

12 - Command references

For detailed command syntax and working examples, see:

平台分布

Codex

39.16%
按下载量换算166

Claude

28.96%
按下载量换算123

Cursor

19.15%
按下载量换算81

Gemini CLI

9.56%
按下载量换算41

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

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。目前只有一个公开来源,建议同时核对源仓库。

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