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snowflake-semanticviewSnowflake semanticview 搜索

Agent Skill

snowflake-semanticview 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。

总安装

201,960

周安装

8,508

GitHub Stars

31,693

下载量

70,720
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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

3

许可证

MIT

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:snowflake-semanticview(Snowflake semanticview 搜索)
来源仓库:https://github.com/github/awesome-copilot
仓库路径:skills/snowflake-semanticview
安装命令:
npx skills add https://github.com/github/awesome-copilot --skill snowflake-semanticview
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/github/awesome-copilot --skill snowflake-semanticview

简介

使用 Snowflake CLI 以及引导式 DDL 创建和测试来构建和验证 Snowflake 语义视图。

  • 处理完整的语义视图生命周期:起草 DDL、从 Snowflake 表元数据填充同义词和注释、通过 CLI 验证 Snowflake 以及执行最终的 CREATE 或 ALTER 语句
  • 需要一次性安装 Snowflake CLI 并设置连接;在继续验证之前确认先决条件
  • 在应用最终定义之前使用临时视图名称针对 Snowflake 验证所有 DDL,然后运行示例查询以确认功能
  • 通过 SELECT 语句引导事实维度关系和列元数据的发现,并根据所需的语义视图组件管理同义词和注释

SKILL.md

Snowflake Semantic Views

One-Time Setup

Workflow For Each Semantic View Request

  1. Confirm the target database, schema, role, warehouse, and final semantic view name.
  2. Confirm the model follows a star schema (facts with conformed dimensions).
  3. Draft the semantic view DDL using the official syntax:

- https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view

  1. Populate synonyms and comments for each dimension, fact, and metric:

- Read Snowflake table/view/column comments first (preferred source): - https://docs.snowflake.com/en/sql-reference/sql/comment - If comments or synonyms are missing, ask whether you can create them, whether the user wants to provide text, or whether you should draft suggestions for approval.

  1. Use SELECT statements with DISTINCT and LIMIT (maximum 1000 rows) to discover relationships between fact and dimension tables, identify column data types, and create more meaningful comments and synonyms for columns.
  2. Create a temporary validation name (for example, append __tmp_validate) while keeping the same database and schema.
  3. Always validate by sending the DDL to Snowflake via Snowflake CLI before finalizing:

- Use snow sql to execute the statement with the configured connection. - If flags differ by version, check snow sql --help and use the connection option shown there.

  1. If validation fails, iterate on the DDL and re-run the validation step until it succeeds.
  2. Apply the final DDL (create or alter) using the real semantic view name.
  3. Run a sample query against the final semantic view to confirm it works as expected. It has a different SQL syntax as can be seen here: https://docs.snowflake.com/en/user-guide/views-semantic/querying#querying-a-semantic-view Example:
SELECT * FROM SEMANTIC_VIEW(
    my_semview_name
    DIMENSIONS customer.customer_market_segment
    METRICS orders.order_average_value
)
ORDER BY customer_market_segment;
  1. Clean up any temporary semantic view created during validation.

Synonyms And Comments (Required)

  • Use the semantic view syntax for synonyms and comments:
WITH SYNONYMS [ = ] ( 'synonym' [ , ... ] )
COMMENT = 'comment_about_dim_fact_or_metric'
  • Treat synonyms as informational only; do not use them to reference dimensions, facts, or metrics elsewhere.
  • Use Snowflake comments as the preferred and first source for synonyms and comments:

- https://docs.snowflake.com/en/sql-reference/sql/comment

  • If Snowflake comments are missing, ask whether you can create them, whether the user wants to provide text, or whether you should draft suggestions for approval.
  • Do not invent synonyms or comments without user approval.

Validation Pattern (Required)

  • Never skip validation. Always execute the DDL against Snowflake with Snowflake CLI before presenting it as final.
  • Prefer a temporary name for validation to avoid clobbering the real view.

Example CLI Validation (Template)

# Replace placeholders with real values.
snow sql -q "<CREATE OR ALTER SEMANTIC VIEW ...>" --connection <connection_name>

If the CLI uses a different connection flag in your version, run:

snow sql --help

Notes

  • Treat installation and connection setup as one-time steps, but confirm they are done before the first validation.
  • Keep the final semantic view definition identical to the validated temporary definition except for the name.
  • Do not omit synonyms or comments; consider them required for completeness even if optional in syntax.

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

能力 5

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

Claude Code

26.5%
按下载量换算18,741

Gemini CLI

23.14%
按下载量换算16,365

Antigravity

16.66%
按下载量换算11,782

Cursor

11.5%
按下载量换算8,133

OpenCode

7.08%
按下载量换算5,007

Codex

3.28%
按下载量换算2,320

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/github/awesome-copilot --skill snowflake-semanticview;npx skills add github/awesome-copilot --skill "snowflake-semanticview" 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。

来源信息

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