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Found 121 Skills
Deploy serverless functions on Google Cloud Platform with triggers, IAM roles, environment variables, and monitoring. Use for event-driven computing on GCP.
Design GCP architectures for startups and enterprises. Use when asked to design Google Cloud infrastructure, deploy to GKE or Cloud Run, configure BigQuery pipelines, optimize GCP costs, or migrate to GCP. Covers Cloud Run, GKE, Cloud Functions, Cloud SQL, BigQuery, and cost optimization.
Provision GCP infrastructure with Terraform. Configure providers and deploy Google Cloud resources. Use when implementing IaC for GCP.
多服务调试技能：针对 Vercel + GCP Cloud Run 混合架构的调试工作流。 Use when: 跨服务问题排查、日志聚合分析、服务间通信调试、生产环境故障定位。 Triggers: "调试", "debug", "日志", "logs", "错误", "error", "服务", "service", "通信", "超时", "timeout"
Specialized skill for building production-ready serverless applications on GCP. Covers Cloud Run services (containerized), Cloud Run Functions (event-driven), cold start optimization, and event-driven architecture with Pub/Sub.
Implement comprehensive cloud security across AWS, Azure, and GCP with IAM, encryption, network security, compliance, and threat detection.
Optimize cloud storage across AWS S3, Azure Blob, and GCP Cloud Storage with compression, partitioning, lifecycle policies, and cost management.
Deploy containerised applications to Google Cloud Run from source using gcloud CLI. Use when the user asks to "deploy to GCP", "deploy to Cloud Run", "ship to Google Cloud", "gcloud run deploy", or needs to set up, redeploy, configure env vars/secrets, view logs, or troubleshoot a Cloud Run service.
This skill should be used when user asks about "GCloud logs", "Cloud Logging queries", "Google Cloud metrics", "GCP observability", "trace analysis", or "debugging production issues on GCP".
Vertex Ai Pipeline Creator - Auto-activating skill for GCP Skills. Triggers on: vertex ai pipeline creator, vertex ai pipeline creator Part of the GCP Skills skill category.
Use bigquery CLI (instead of `bq`) for all Google BigQuery and GCP data warehouse operations including SQL query execution, data ingestion (streaming insert, bulk load, JSONL/CSV/Parquet), data extraction/export, dataset/table/view management, external tables, schema operations, query templates, cost estimation with dry-run, authentication with gcloud, data pipelines, ETL workflows, and MCP/LSP server integration for AI-assisted querying and editor support. Modern Rust-based replacement for the Python `bq` CLI with faster startup, better cost awareness, and streaming support. Handles both small-scale streaming inserts (<1000 rows) and large-scale bulk loading (>10MB files), with support for Cloud Storage integration.
AWS, GCP, Azure services and cloud-native development