smithery/jeremylongshore

model-versioning-manager

Manage model versioning manager operations. Auto-activating skill for ML Deployment. Triggers on: model versioning manager, model versioning manager Part of the ML Deployment skill category. Use when working with model versioning manager functionality. Trigger with phrases like "model versioning manager", "model manager", "model". '

Installation

$ npx skills add smithery/jeremylongshore --skill model-versioning-manager

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Skill metadata

Parsed from SKILL.md frontmatter.

Version1.0.0
LicenseMIT
CompatibilityDesigned for Claude Code
Allowed toolsRead, Write, Edit, Bash(cmd:*), Grep
Declared agents claude-code

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,299 B
  • docs SUMMARY.md 366 B

History

  1. First recorded snapshot · 0 installs

SKILL.md

Model Versioning Manager

Overview

This skill provides automated assistance for model versioning manager tasks within the ML Deployment domain.

When to Use

This skill activates automatically when you:

  • Mention "model versioning manager" in your request
  • Ask about model versioning manager patterns or best practices
  • Need help with machine learning deployment skills covering model serving, mlops pipelines, monitoring, and production optimization.

Instructions

  1. Provides step-by-step guidance for model versioning manager
  2. Follows industry best practices and patterns
  3. Generates production-ready code and configurations
  4. Validates outputs against common standards

Examples

Example: Basic Usage Request: "Help me with model versioning 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