About
This skill helps developers design and implement ML model deployments with production-ready features like A/B testing, monitoring, and retraining pipelines. Use it when you need to operationalize a model within existing system constraints and infrastructure. It provides structured guidance from scoping to producing implementation plans and validation criteria.
Quick Install
Claude Code
Recommendednpx skills add cornmanwtf/ABANG-COLEK -a claude-code/plugin add https://github.com/cornmanwtf/ABANG-COLEKgit clone https://github.com/cornmanwtf/ABANG-COLEK.git ~/.claude/skills/ml-model-deploymentCopy and paste this command in Claude Code to install this skill
GitHub Repository
Frequently asked questions
What is the ml-model-deployment skill?
ml-model-deployment is a Claude Skill by cornmanwtf. Skills package instructions and resources that Claude loads on demand, so Claude can perform ml-model-deployment-related tasks without extra prompting.
How do I install ml-model-deployment?
Use the install commands on this page: add ml-model-deployment to Claude Code as a plugin, or clone its repository into your skills directory, then restart Claude so it picks up the skill.
What category does ml-model-deployment belong to?
ml-model-deployment is in the data-analytics category, tagged ai and testing.
Is ml-model-deployment free to use?
Yes. ml-model-deployment is listed on AIMCP and free to install. It runs inside Claude, so no separate service account is required to use the skill itself.
Related Skills
This skill designs dimensional models and fact tables for data warehouse projects. It clarifies requirements, reviews system constraints, and selects appropriate architectural patterns. The outputs include implementation plans, specifications, and validation steps for developers.
The data-catalog-creator skill helps developers design and plan systems for managing metadata, data lineage, and discovery. It generates implementation plans, architectural specs, and required artifacts based on your stack and constraints. Use this skill when you need to establish or improve data governance, compliance, and discoverability within your infrastructure.
The data-pipeline-builder skill designs and plans orchestration pipelines with a focus on idempotency. It's used when you need to create data workflows, producing artifacts like specs, configs, and validation steps. Developers should use it after confirming requirements and necessary approvals.
This skill helps developers implement data quality checks through validation, profiling, and anomaly detection. Use it when you need to design or plan a data quality system within a given architecture and stack. It guides you from clarifying requirements to producing implementation artifacts and acceptance criteria.