Verwaltet Datasets, Tabellen und Jobs in BigQuery und integriert BigQuery ML und Gemini für erweiterte Datenanalyse und KI-gestützte Einblicke. Verwenden Sie dies, wenn Sie mit BigQuery interagieren, SQL-Abfragen ausführen, BigQuery-Ressourcen verwalten oder die integrierten ML-Funktionen von BigQuery nutzen möchten. Auch geeignet für Datenanalyse, das Laden von Daten in BigQuery oder die Entwicklung von KI-Anwendungen auf BigQuery.
Diese Fähigkeit ermöglicht es KI-Agenten, nahtlos mit Google BigQuery zu interagieren, einem vollständig verwalteten Cloud-Data-Warehouse. Sie können Datasets und Tabellen erstellen, verwalten und löschen, SQL-Abfragen ausführen, BigQuery-Jobs überwachen und Daten importieren oder exportieren. Darüber hinaus unterstützt die Fähigkeit BigQuery ML, sodass Agenten Modelle für maschinelles Lernen direkt in BigQuery mit SQL erstellen und bewerten können. Die Integration mit Gemini ermöglicht fortschrittliche KI-gesteuerte Analysen, z. B. das Erstellen von Embeddings oder das Durchführen semantischer Suchen. Plattformunabhängig konzipiert, funktioniert es mit jedem bq-kompatiblen Tool und vereinfacht Workflows für Datenpipelines, Ad-hoc-Analysen und die Entwicklung von KI-Anwendungen, ohne dass umfangreiche manuelle Konfiguration erforderlich ist.
npx skills add https://github.com/google/skills/tree/main/skills/bigquery-basicsInstalled? Explore more Recherche & Datenanalyse skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Verwaltet Datasets, Tabellen und Jobs in BigQuery und integriert BigQuery ML und Gemini für erweiterte Datenanalyse und KI-gestützte Einblicke. Verwenden Sie dies, wenn Sie mit BigQuery interagieren, SQL-Abfragen ausführen, BigQuery-Ressourcen verwalten oder die integrierten ML-Funktionen von BigQuery nutzen möchten. Auch geeignet für Datenanalyse, das Laden von Daten in BigQuery oder die Entwicklung von KI-Anwendungen auf BigQuery.
This repo contains 18 individual skills — each has its own dedicated page.
Interact with the Gemini Enterprise Agent Platform Skill Registry to create and search for available skills. Use this skill to enable agents to register functionality or discover new capabilities.
Manages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB model context protocol (MCP) tools for automated database operations.
Manages Cloud Run services, jobs, and worker pools. Use when you need to deploy applications responding to HTTP requests (services), run event-triggered or scheduled tasks (jobs), or handle always-on pull-based background processing (worker pools).
This file generates or explains Cloud SQL resources. Use this file when the user asks to create a Cloud SQL instance or database for MySQL, PostgreSQL, or SQL Server. Cloud SQL manages third-party MySQL, PostgreSQL, and SQL Server instances as resources in Cloud SQL. For example, when Cloud SQL creates an open-source MySQL instance, the resulting resource is a Cloud SQL for MySQL instance that Google Cloud manages. Cloud SQL handles backups, high availability, and secure connectivity for relational database workloads.
Use this skill whenever you are working on a project that uses Firebase products or services, especially for mobile or web apps.
Use when the user asks about using Gemini in an enterprise environment or explicitly mentions Vertex AI, Google Cloud, or Agent Platform. Guides the usage of the Gemini API on Agent Platform with the Google Gen AI SDK. Covers SDK usage (Python, JS/TS, Go, Java, C#), capabilities like multimodal inputs, tools, media generation, caching, batch prediction, and Live API.
Guides the usage of Gemini Interactions API on Gemini Enterprise Agent Platform. Use when the user wants to use the stateful, server-managed Interactions API for multi-turn conversations, background execution, streaming, structured output, and function calling on the Agent Platform.
Plan, create, and configure production-ready Google Kubernetes Engine (GKE) clusters using the golden path Autopilot configuration. Covers Day-0 checklist, Autopilot vs Standard, networking (private clusters, VPC-native, Gateway API), security (Workload Identity, Secret Manager, RBAC hardening), observability, scaling, cost optimization, and AI/ML inference. WHEN: create GKE cluster, provision GKE environment, design GKE networking, secure GKE, optimize GKE cost, GKE autoscaling, GKE inference, GKE upgrade, GKE observability, GKE multi-tenancy, GKE batch, GKE HPC, GKE compute class.
Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs (including cost estimation), NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics. Don't use for generic VM management or non-observability tasks.
Provides expert guidance on authenticating and authorizing to Google Cloud services and APIs, covering human users, service identities, Application Default Credentials (ADC), and best practices for secure access.
Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs, NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics.
Guides a developer's first steps on Google Cloud, covering account creation, billing setup, project management, and deploying a first resource. Use when a new developer wants to initialize their first Google Cloud project, configure billing, and verify deployment. Don't use for enterprise organization setup (use Google Cloud Setup guided flow for that instead). Don't use for complex multi-project architectures.
Generates cost optimization guidance for Google Cloud workloads based on the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify cost requirements and constraints, and provide actionable recommendations for build, deploy, and manage the workload cost-efficiently in Google Cloud.
Generates operations-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Operational Excellence pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify operational requirements, and provide actionable recommendations for deployment, monitoring, and incident management.
Generates performance-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Performance Optimization pillar of the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify performance requirements, and provide actionable recommendations for resource allocation, modular design, and elasticity.
Generates reliability-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework. Use this skill to evaluate a workload, identify reliability requirements, and provide actionable recommendations for build, deploy, and manage the workload reliably in Google Cloud.
Generates security-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify security requirements, and provide actionable recommendations for IAM, network security, data protection, and operational security.
Generates sustainability-focused guidance for Google Cloud workloads based on the design principles and recommendations in the Google Cloud Well-Architected Framework (WAF). Use this skill to evaluate a workload, identify environmental impact requirements, and provide actionable recommendations to build, deploy, and manage the workload sustainably in Google Cloud.
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.