vertex-infra-expert
について
このスキルは、Geminiのデプロイメント、Model Garden、ベクター検索を含むVertex AIインフラストラクチャのプロビジョニングに関するTerraformの専門知識を提供します。開発者がVertex AIのTerraform構成やGeminiインフラストラクチャについて質問した際に起動します。Google Cloud上での本番環境対応のAIサービスデプロイメントを自動化するためにご利用ください。
クイックインストール
Claude Code
推奨/plugin add https://github.com/jeremylongshore/claude-code-plugins-plusgit clone https://github.com/jeremylongshore/claude-code-plugins-plus.git ~/.claude/skills/vertex-infra-expertこのコマンドをClaude Codeにコピー&ペーストしてスキルをインストールします
ドキュメント
What This Skill Does
Expert in provisioning Vertex AI infrastructure including Model Garden, Gemini endpoints, vector search, ML pipelines, and production AI services.
When This Skill Activates
Triggers: "vertex ai terraform", "deploy gemini terraform", "model garden infrastructure", "vertex ai endpoints terraform", "vector search terraform"
Core Terraform Modules
Gemini Model Endpoint
resource "google_vertex_ai_endpoint" "gemini_endpoint" {
name = "gemini-25-flash-endpoint"
display_name = "Gemini 2.5 Flash Production"
location = var.region
encryption_spec {
kms_key_name = google_kms_crypto_key.vertex_key.id
}
}
resource "google_vertex_ai_deployed_model" "gemini_deployment" {
endpoint = google_vertex_ai_endpoint.gemini_endpoint.id
model = "publishers/google/models/gemini-2.5-flash"
dedicated_resources {
min_replica_count = 1
max_replica_count = 10
machine_spec {
machine_type = "n1-standard-4"
}
}
automatic_resources {
min_replica_count = 1
max_replica_count = 5
}
}
Vector Search Index
resource "google_vertex_ai_index" "embeddings_index" {
display_name = "production-embeddings"
location = var.region
metadata {
contents_delta_uri = "gs://${google_storage_bucket.embeddings.name}/index"
config {
dimensions = 768
approximate_neighbors_count = 150
distance_measure_type = "DOT_PRODUCT_DISTANCE"
algorithm_config {
tree_ah_config {
leaf_node_embedding_count = 1000
leaf_nodes_to_search_percent = 10
}
}
}
}
}
Tool Permissions
Read, Write, Edit, Grep, Glob, Bash - AI infrastructure provisioning
References
GitHub リポジトリ
関連スキル
evaluating-llms-harness
テストThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
sglang
メタSGLang is a high-performance LLM serving framework that specializes in fast, structured generation for JSON, regex, and agentic workflows using its RadixAttention prefix caching. It delivers significantly faster inference, especially for tasks with repeated prefixes, making it ideal for complex, structured outputs and multi-turn conversations. Choose SGLang over alternatives like vLLM when you need constrained decoding or are building applications with extensive prefix sharing.
cloudflare-turnstile
メタThis skill provides comprehensive guidance for implementing Cloudflare Turnstile as a CAPTCHA-alternative bot protection system. It covers integration for forms, login pages, API endpoints, and frameworks like React/Next.js/Hono, while handling invisible challenges that maintain user experience. Use it when migrating from reCAPTCHA, debugging error codes, or implementing token validation and E2E tests.
langchain
メタLangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.
