Gcp Pipeline Resource Provisioning
作者 gemini-cli-extensions2df10e25bbf7Apache-2.0215 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫今天更新
Automates declarative resource creation and provisioning for data pipelines, supporting BigQuery, Dataform, Dataproc, BigQuery Data Transfer Service (DTS), and other resources. It manages environment-specific configurations (dev, staging, prod) through a deployment.yaml file. Use when: - Modifying or creating deployment.yaml for deployment settings. - Resolving environment-specific variables (e.g., Project IDs, Regions) for deployment. - Provisioning supported infrastructure like BigQuery datasets/tables, Dataform resources, or DTS resources via deployment.yaml. Do not use when: - Resources already exist. - Managing resources not supported by `gcloud beta orchestration-pipelines resource-types list`. - Managing general cloud infrastructure (VMs, networks, Kubernetes, IAM policies), which are better suited for Terraform. - Infrastructure spans multiple cloud providers (AWS, Azure, etc.). - Already uses Terraform for the target resources.
- 2df10e25bbf7目前提交 2df10e2發布於 2026年10月8日
來源與署名
來源：gemini-cli-extensions/data-agent-kit-starter-pack位於skills/gcp-pipeline-resource-provisioning提交2df10e2
授權條款： Apache-2.0
內容歸原作者所有。SourceWeft 從公開儲存庫中收錄這些內容。
更多來自 gemini-cli-extensions/data-agent-kit-starter-pack 的技能
Schema Mapping
gemini-cli-extensions
為 ETL、ELT 或資料整合任務規劃來源到目標的結構描述對應，產出文件化的對應宣言。
Resolving Mcp Region Configs
gemini-cli-extensions
修復區域性 Google Cloud MCP 伺服器設定中未取代的區域佔位符，讓缺少的 MCP 工具得以註冊。
Notebook Guidance
gemini-cli-extensions
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.
Ml Best Practices
gemini-cli-extensions
為機器學習筆記本提供逐步方案，涵蓋分群、預測、分類、迴歸與模型比較。
Managing Python Dependencies
gemini-cli-extensions
指導代理偵測 Python 專案的相依性管理器並正確安裝套件，而不是使用全域 pip。
Google Cloud Storage Fuse
gemini-cli-extensions
Mounts Cloud Storage buckets as a POSIX file system with Cloud Storage FUSE (gcsfuse). Use when you need to interact with gcsfuse — decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size the file, stat, and list caches, tune mount flags or config-file settings, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or GCS bill spikes on existing mounts with gcsfuse metrics. Covers mount semantics, the gcsfuse CLI and config file, the GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration or data management without a mount (google-cloud-storage-basics) or for fully POSIX-compliant shared file systems (Filestore, Managed Lustre).
更多DevOps & Cloud技能
M5 Onboard
anthropics
透過 USB 偵測 M5Stack ESP32 開發板，燒錄 UIFlow 2.0 韌體並安裝 MicroPython 應用程式套件。
Runbook
anthropics
為重複性任務建立或更新逐步操作手冊，包含疑難排解、回復與升級流程。
Incident Response
anthropics
引導事故回應流程：嚴重程度分級、狀態更新、緩解追蹤，以及無責事後檢討。
Deploy Checklist
anthropics
產生部署前就緒檢查清單，涵蓋部署前、部署、部署後與回復觸發條件。
Spanner Basics
指導 Google Cloud Spanner 的執行個體與資料庫管理、結構定義設計、查詢與效能診斷。
Secops Cases
透過 MCP 工具管理 Google Security Operations SOAR 案件的完整生命週期。