writing-skills
writing-skills
Use when creating new skills, editing existing skills, or verifying skills work before deployment
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Déploiement et opérations · SKILL.md
model-registry-manager
model-registry-managerManage model registry manager operations. Auto-activating skill for ML Deployment. Triggers on: model registry manager, model registry manager Part of the ML Deployment skill category. Use when working with model registry manager functionality. Trigger with phrases like "model registry manager", "model manager", "model".
Independent GitHub snapshot · Automated classification · Original documentation governs
Documentation de la Skill
GitHubname: "model-registry-manager" description: | Manage model registry manager operations. Auto-activating skill for ML Deployment. Triggers on: model registry manager, model registry manager Part of the ML Deployment skill category. Use when working with model registry manager functionality. Trigger with phrases like "model registry manager", "model manager", "model". allowed-tools: "Read, Write, Edit, Bash(cmd:*), Grep" version: 1.0.0 license: MIT author: "Jeremy Longshore jeremy@intentsolutions.io"
Model Registry Manager
Overview
This skill provides automated assistance for model registry manager tasks within the ML Deployment domain.
When to Use
This skill activates automatically when you:
- Mention "model registry manager" in your request
- Ask about model registry manager 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 model registry manager
- 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 model registry manager" 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