跳到正文
cccskills
免费在 GitHub 公开

spanner-basics

Plan and manage Cloud Spanner databases on Google Cloud, design tables and primary keys, write SQL and client code, and investigate slow queries using database statistics to identify resource-intensive operations.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

Assists in provisioning instances and databases, designing performant schemas, and querying data in Cloud Spanner on Google Cloud. Use when designing primary keys, writing SQL queries or client library code, or diagnosing performance issues.

查看安装方法

适用场景

  • Create Cloud Spanner instances and databases
  • Design primary keys and related tables
  • Write SQL queries and client library code
  • Investigate slow or CPU-intensive queries

包含的文件(10)

  • SKILL.md3.4 KB
  • references/cli-usage.md1.3 KB
  • references/client-library-usage.md3.5 KB
  • references/core-concepts.md1.1 KB
  • references/iam-security.md1.2 KB
  • references/mcp-usage.md1.5 KB
  • references/postgresql-dialect.md951 B
  • references/queues-usage.md10.8 KB
  • references/schema-design.md3.4 KB
  • references/terraform-usage.md928 B

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Spanner Basics

This skill provides core workflows and guidance for administering and developing with Cloud Spanner on Google Cloud, a fully managed, mission-critical database service offering global transactional consistency and automatic, synchronous replication for high availability.

Core Principles

  • Performance First: Spanner scales horizontally. Efficiency is tied to Primary Key design. Always warn against using monotonically increasing/decreasing values (like sequential timestamps) as the first part of a primary key to avoid hotspots.
  • Schema Design: Prefer interleaved tables for strongly related parent-child data that is frequently accessed together.

Safety

[!CAUTION] CRITICAL INSTRUCTION: You MUST obtain explicit user confirmation before making any non-emulator database changes (DML or DDL) or destructive operations (such as dropping tables, indexes, or any other Spanner resources). Do not execute them automatically; instead, output the command (e.g., gcloud spanner databases ddl update) and ask for explicit user approval. When database access is unavailable or authentication fails, do not block on trying to verify the existence of the instance, database, or table. Assume the provided resources exist and directly generate the DDL commands.

Common Workflows

Schema Evolution & DDL

  1. Use gcloud spanner databases ddl update to apply schema updates (such as CREATE, ALTER, or DROP tables and indexes).
  2. Reference schema-design.md for guidelines on primary key selection and interleaved tables.

Diagnosing Performance Issues

  1. Use SPANNER_SYS tables to identify slow or resource-intensive queries.
  2. For example, query SPANNER_SYS.QUERY_STATS_TOP_HOUR to find queries with the highest CPU usage.

Reference Directory

  • Core Concepts: Explanation of Spanner internals, architecture, and design.
  • CLI Usage: Essential gcloud spanner command-line operations for managing instances and databases.
  • IAM Security: Roles, permissions, and data governance best practices for Spanner.
  • Client Library Usage: Using Google Cloud client libraries for Spanner (Java, Go, Python, Node.js).
  • Terraform Usage: Infrastructure as Code examples for provisioning Spanner instances and databases.
  • MCP Usage: Using the Spanner remote MCP server.
  • PostgreSQL Dialect: Best practices and examples for using the PostgreSQL interface in Spanner.
  • Queues Usage: Cloud Spanner Queues: mental model, design constraints, and links related to transactional message queues.
  • Schema Design: Guidelines on primary key selection and interleaved tables for performance.

If you need product information that's not found in these references, use the search_documents tool of the Developer Knowledge MCP server.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Create Terraform files that define alerts for AI agent latency, errors, token use, safety, security, and response quality. Check telemetry and existing policies first, then validate the generated configuration. Quality alerts require Vertex AI.

中文简介正在准备中。当前显示英语简介。

  • Configure alerts for agent latency and error rates
  • Monitor rapid increases in token usage
  • Monitor safety triggers and permission denials
google/skills2.1万2026年10月17日 更新

Deploy Model Garden models to Agent Platform endpoints, the addresses used for model requests. Check supported hardware and estimated hourly costs, inspect deployment progress, test predictions, and remove deployed resources after use.

中文简介正在准备中。当前显示英语简介。

  • Choose a deployable model and supported hardware
  • Estimate hourly deployment costs before approval
  • Check deployment progress and test predictions
google/skills2.1万2026年10月17日 更新

Manages Agent Platform endpoints, the stable URLs used to serve online model predictions. It supports listing, inspecting, creating, updating, and deleting endpoints, plus troubleshooting permission errors, quota limits, and resources that are busy.

中文简介正在准备中。当前显示英语简介。

  • Listing endpoints and checking their configuration
  • Creating an endpoint before model deployment
  • Updating endpoint display names or labels
google/skills2.1万2026年10月17日 更新

Evaluate AI models and agents on Google Cloud using test datasets and scoring criteria. Generate simulated user scenarios, analyze failures, and compare results across changes to guide improvements to prompts and code.

中文简介正在准备中。当前显示英语简介。

  • Build evaluation datasets from conversation logs
  • Generate simulated user scenarios
  • Choose scoring criteria for responses and tool use
google/skills2.1万2026年10月17日 更新

Call Gemini and OpenMaaS models such as Llama, DeepSeek, and Qwen through Google Cloud Agent Platform. Prepare authentication and SDK code, run prompts, configure connection endpoints, and troubleshoot common request errors.

中文简介正在准备中。当前显示英语简介。

  • Send a test prompt to Gemini
  • Call Llama or DeepSeek with the OpenAI SDK
  • Write code to call an existing custom endpoint
google/skills2.1万2026年10月17日 更新

Guides migration from Google AI Studio’s Gemini API to Gemini Enterprise Agent Platform on Google Cloud, covering billing, access permissions, authentication, and connection settings for SDKs and supported agent tools.

中文简介正在准备中。当前显示英语简介。

  • Moving a Gemini application to Google Cloud
  • Using Google Cloud credits for Gemini API calls
  • Unifying agent authentication and billing
google/skills2.1万2026年10月17日 更新

查看 google 的全部技能

报告此技能的问题