writing-skills
writing-skills
Use when creating new skills, editing existing skills, or verifying skills work before deployment
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運用・デプロイ · SKILL.md
vertex-ai-deployer
vertex-ai-deployerDeploy vertex ai deployer operations. Auto-activating skill for ML Deployment. Triggers on: vertex ai deployer, vertex ai deployer Part of the ML Deployment skill category. Use when deploying applications or services. Trigger with phrases like "vertex ai deployer", "vertex deployer", "deploy vertex ai er".
Independent GitHub snapshot · Automated classification · Original documentation governs
スキルの説明
GitHubname: vertex-ai-deployer description: 'Deploy vertex ai deployer operations. Auto-activating skill for ML Deployment.
Triggers on: vertex ai deployer, vertex ai deployer
Part of the ML Deployment skill category. Use when deploying applications or services. Trigger with phrases like "vertex ai deployer", "vertex deployer", "deploy vertex ai er".
' allowed-tools: Read, Write, Edit, Bash(cmd:*), Grep version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io tags:
- ai
- mlops compatibility: Designed for Claude Code
Vertex Ai Deployer
Overview
This skill provides automated assistance for vertex ai deployer tasks within the ML Deployment domain.
When to Use
This skill activates automatically when you:
- Mention "vertex ai deployer" in your request
- Ask about vertex ai deployer patterns or best practices
- Need help with machine learning deployment skills covering model serving, mlops pipelines, monitoring, and production optimization.
Instructions
- Provides step-by-step guidance for vertex ai deployer
- Follows industry best practices and patterns
- Generates production-ready code and configurations
- Validates outputs against common standards
Examples
Example: Basic Usage Request: "Help me with vertex ai deployer" Result: Provides step-by-step guidance and generates appropriate configurations
Prerequisites
- Relevant development environment configured
- Access to necessary tools and services
- Basic understanding of ml deployment concepts
Output
- Generated configurations and code
- Best practice recommendations
- Validation results
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Configuration invalid | Missing required fields | Check documentation for required parameters |
| Tool not found | Dependency not installed | Install required tools per prerequisites |
| Permission denied | Insufficient access | Verify credentials and permissions |
Resources
- Official documentation for related tools
- Best practices guides
- Community examples and tutorials
Related Skills
Part of the ML Deployment skill category. Tags: mlops, serving, inference, monitoring, production