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Automated Model Deployment

deploying-machine-learning-models

jeremylongshore/claude-code-plugins-plus-skills

Deploying Machine Learning Models automates serving trained models to production by analyzing requirements, generating API and infrastructure code, and orchestrating deployment workflows with validation, error handling, and monitoring baked in.

Hugging Face Jobs

hugging-face-jobs

sickn33/antigravity-awesome-skills

Schedule and run Python workloads on fully managed Hugging Face infrastructure, leveraging CPUs, GPUs, or TPUs for data processing, batch inference, experiments, or scheduled automation while persisting outputs to the Hub.

LangChain Production Deployment

langchain-deploy-integration

jeremylongshore/claude-code-plugins-plus-skills

Guides production deployment of LangChain applications via LangServe, Docker, Cloud Run, and LangSmith observability, covering containerization, API setup, and health checks for reliable production infrastructure.

Production ML Engineering Playbook

sickn33/antigravity-awesome-skills

Guides ML engineers in building production-ready systems with PyTorch 2.x, TensorFlow, and modern tooling, covering serving pipelines, feature engineering, monitoring, and MLOps infrastructure for scalable, reliable deployments.

Modern MLOps Engineering Guide

sickn33/antigravity-awesome-skills

A comprehensive MLOps engineer skill that covers building scalable ML infrastructure, pipelines, experiment tracking, registries, and cloud-native deployments across AWS, Azure, and GCP with best practices for reliable automation.

OpenEvidence Production Readiness

openevidence-prod-checklist

jeremylongshore/claude-code-plugins-plus-skills

Structured production checklist guiding OpenEvidence clinical AI deployments through legal, security, infrastructure, monitoring, resilience, and testing milestones to ensure compliant launches and pre-deployment reviews.

Torchforge RL Training

torchforge-rl-training

Orchestra-Research/AI-Research-SKILLs

Torchforge RL Training documents Meta's PyTorch-native torchforge library, which separates algorithms from infrastructure so researchers can explore GRPO/SFT losses, manage Monarch actors, tap TorchTitan and vLLM for scalable distributed runs, and monitor KL/loss curves.

Vertex AI Terraform Expert

vertex-infra-expert

jeremylongshore/claude-code-plugins-plus-skills

Specializes in provisioning Vertex AI infrastructure with Terraform, covering Model Garden, Gemini endpoints, vector search indexes, ML pipelines, and enterprise-grade production deployments.

Modal Serverless GPU Platform

K-Dense-AI/claude-scientific-skills

Modal provides a Python-first serverless cloud that deploys AI/ML models with GPU compute, persistent storage, scheduled jobs, and web endpoints without managing infrastructure.

AgentMail Email API

sickn33/antigravity-awesome-skills

AgentMail provides RESTful email infrastructure for AI agents, letting them create inboxes, send/receive messages, register webhooks, and monitor karma balances via secure API keys.

Generative Engine Optimization Audit

zubair-trabzada/geo-seo-claude

Performs a GEO (Generative Engine Optimization) audit for any website, fetching homepage data and sitemaps, collecting page-level metrics, and delegating parallel subagents to evaluate AI citability, platform authority, technical infrastructure, content E-E-A-T, and schema before outputting a composite score with prioritized action items.

Terraform Security Audit

auditing-terraform-infrastructure-for-security

mukul975/Anthropic-Cybersecurity-Skills

Automated Terraform infrastructure scanning combines Checkov, tfsec, Terrascan, and OPA/Rego policies to catch misconfigurations such as permissive IAM, public exposure, or missing encryption before deployment, fitting CI/CD and compliance workflows.

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