菜单
Generate production-ready Google Cloud code examples from official repositories including ADK samples, Genkit templates, Vertex AI notebooks, and Gemini patterns. Use when asked to "show ADK example" or "provide GCP starter kit". Trigger with relevant phrases based on skill purpose.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/ComeOnOliver/skillshub --skill gcp-examples-expert命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Review product and feature risk before an AI coding agent starts implementation.
Use Xquik for X data and confirmation-gated X actions: tweet search, user lookup, follower export, media download, monitors, webhooks, MCP, and SDK workflows.
Canton Network open-source ecosystem guide covering DAML SDK, Canton runtime, and Splice applications. Use when working with Canton Network, DAML smart contracts, or building decentralized applications.
基于 SOC 职业分类
正在显示 SKILL.md
| name | gcp-examples-expert |
| description | Generate production-ready Google Cloud code examples from official repositories including ADK samples, Genkit templates, Vertex AI notebooks, and Gemini patterns. Use when asked to "show ADK example" or "provide GCP starter kit". Trigger with relevant phrases based on skill purpose. |
| allowed-tools | Read, Write, Edit, Grep, Glob, Bash(cmd:*) |
| version | 2.1.0 |
| author | Jeremy Longshore <jeremy@intentsolutions.io> |
| license | MIT |
| effort | medium |
| argument-hint | [framework or use-case] |
| compatible-with | claude-code, codex, openclaw |
| tags | ["ai","gcp","gcp-examples"] |
Generate production-ready Google Cloud Platform code examples sourced from official repositories including ADK samples, Agent Starter Pack, Firebase Genkit, Vertex AI samples, Generative AI examples, and AgentSmithy. This skill maps user requirements to the appropriate GCP framework and delivers working code with security, monitoring, and deployment best practices baked in.
gcloud CLI authenticated with appropriate IAM roles (Vertex AI User, Cloud Run Developer)npm install -g firebase-tools)${CLAUDE_SKILL_DIR}/references/code-example-categories.mdSee ${CLAUDE_SKILL_DIR}/references/workflow.md for the phased workflow and ${CLAUDE_SKILL_DIR}/references/best-practices-applied.md for the full best-practices checklist.
| Error | Cause | Solution |
|---|---|---|
| Invalid GCP project or API not enabled | Vertex AI API disabled or project ID misconfigured | Run gcloud services enable aiplatform.googleapis.com; verify project ID in gcloud config list |
| Permission denied on Vertex AI resources | Service account missing required IAM roles | Grant roles/aiplatform.user and roles/run.developer; check VPC-SC perimeter allows access |
| Model not available in region | Requested Gemini model not deployed in specified location | Use us-central1 or europe-west4 where Gemini models are available; check regional availability docs |
| Quota exceeded for API calls | Rate limit hit on Vertex AI prediction endpoint | Request quota increase via Cloud Console; implement exponential backoff with jitter |
| Dependency version conflict | Incompatible versions of AI SDK, Genkit, or provider packages | Pin versions in package.json or requirements.txt; use lockfile to ensure reproducibility |
See ${CLAUDE_SKILL_DIR}/references/errors.md for additional error scenarios.
Scenario 1: ADK Agent with Code Execution -- Create a production ADK agent using google/adk-samples patterns. Enable Code Execution Sandbox with 14-day state TTL, configure Memory Bank for persistent context, apply VPC Service Controls and IAM least-privilege. Deploy to Vertex AI Agent Engine.
Scenario 2: Genkit RAG Flow -- Implement a retrieval-augmented generation system using Firebase Genkit. Define a retriever with text-embedding-gecko embeddings, connect to a vector database, build a RAG flow with Zod-validated input/output schemas. Deploy to Cloud Run with auto-scaling (2-10 instances).
Scenario 3: Gemini Multimodal Analysis -- Analyze video content using the generative-ai repository patterns. Create a multimodal prompt combining video URIs with text questions using Gemini 2.5 Pro. Include safety filter configuration, token counting for cost estimation, and structured output parsing.
See ${CLAUDE_SKILL_DIR}/references/example-interactions.md for detailed interaction examples.