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bigquery-basics

Manages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and analysis.

DeepseekModel 官方收录技能 质量 优秀 · 90 v1.0.0

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https://deepseekmodel.com/api/download.php?id=google-skills-skills-cloud-bigquery-basics-skill-md&format=skill
下载 .skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用
.skill 文件中 system_prompt 字段的实际内容。
name bigquery-basics metadata {"category":"BigDataAndAnalytics"} description Manages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and analysis. BigQuery Basics BigQuery is a serverless, AI-ready data platform that enables high-speed analysis of large datasets using SQL and Python. Its disaggregated architecture separates compute and storage, allowing them to scale independently while providing built-in machine learning, geospatial analysis, and business intelligence capabilities. Setup and Basic Usage Enable the BigQuery API: gcloud services enable bigquery.googleapis.com --quiet Create a Dataset: bq mk --dataset --location=US my_dataset Create a Table: Create a file named schema.json with your table schema: [ { "name" : "name" , "type" : "STRING" , "mode" : "REQUIRED" } , { "name" : "post_abbr" , "type" : "STRING" , "mode" : "NULLABLE" } ] Then create the table with the bq tool: bq mk --table my_dataset.mytable schema.json Run a Query: bq query --use_legacy_sql= false \ 'SELECT name FROM `bigquery-public-data.usa_names.usa_1910_2013` \ WHERE state = "TX" LIMIT 10' Reference Directory Core Concepts : Storage types, analytics workflows, and BigQuery Studio features. Change History : Tracking and querying incremental table changes using APPENDS and CHANGES. Continuous Queries : Running continuous SQL statements to analyze incoming data in real time. CLI Usage : Essential bq command-line tool operations for managing data and jobs. Client Libraries : Using Google Cloud client libraries for Python, Java, Node.js, and Go. MCP Usage : Using the BigQuery remote MCP server and Gemini CLI extension. Infrastructure as Code : Terraform examples for datasets, tables, and reservations. IAM & Security : Roles, permissions, and data governance best practices. If you need product information not found in these references, use the Developer Knowledge MCP server search_documents tool. Related Skills BigQuery AI & ML Skill : SKILL.md file for BigQuery AI and ML capabilities (forecast, anomaly detection, text generation).
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下载的 .skill 包内含以下字段。
字段 说明
format格式标识(skill/v1)
skill_id技能唯一 ID
name技能名称
version版本号
description技能描述
category所属分类(数组)
trigger_words触发词列表
tags标签列表
source来源标识
source_url来源链接(本页地址)
exported_at导出时间(每次下载生成)
system_prompt系统提示词正文
model_config模型参数:provider / model / temperature / max_tokens / top_p
examples示例
install_guide各平台导入说明(Coze / Dify / Claude / 自定义框架)
同一份技能可按不同平台格式导出。
.skill 标准格式,含 system_prompt 与 model_config,导入任意 Agent 框架即可使用 下载
.skillpro 增强格式,额外含脚本 / 工具 / 依赖 / 钩子占位 下载
.json 纯 JSON 导出,只含 system_prompt 与模型参数 下载
Coze 带 frontmatter 的 Markdown,Coze 平台导入用 下载
Dify Dify DSL,创建应用后直接导入 下载

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