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Deployment Rollback Manager

managing-deployment-rollbacks

jeremylongshore/claude-code-plugins-plus-skills

Manages and automates deployment rollbacks with safety checks, ensuring rapid recovery from failed releases by reverting to known-good versions and verifying system health.

Environment Configuration Management

managing-environment-configurations

jeremylongshore/claude-code-plugins-plus-skills

Manages environment configurations and secrets across development, staging, and production deployments by invoking the environment-config-manager plugin to generate, update, or retrieve files, keeping DevOps workflows secure and consistent.

Microservices Architecture Advisor

microservices-architect

Jeffallan/claude-skills

Guides distributed system architecture by decomposing monoliths, defining bounded contexts, selecting communication protocols, and recommending resilience, observability, and deployment strategies for cloud-native microservices.

Enterprise RL Model Training

miles-rl-training

Orchestra-Research/AI-Research-SKILLs

Guides production-grade miles RL training for massive MoE models, covering FP8/INT4 low precision, train-inference alignment, and speculative rollout workflows to maximize throughput in enterprise deployments.

Mistral CI Pipeline Guard

mistral-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

Sets up GitHub Actions workflows to run Mistral AI prompt regression suites, validate model references, and append cost estimations so teams can gate deployments and monitor prompt quality in CI.

Mistral Deployment Guide

mistral-deploy-integration

jeremylongshore/claude-code-plugins-plus-skills

Guides deploying Mistral AI applications to Vercel, Docker, and Cloud Run with proper secret management, runtime configs, health checks, and handler examples for production-ready pipelines.

Mistral Environment Setup

mistral-multi-env-setup

jeremylongshore/claude-code-plugins-plus-skills

Defines how to configure Mistral AI per development, staging, and production environments, covering base configs, overrides, environment detection, and secret-management approaches for deployments.

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.

Production ML Pipeline

Jeffallan/claude-skills

Implements production-grade ML pipeline infrastructure that wires experiment tracking (MLflow/W&B), Kubeflow or Airflow DAGs, feature stores, model registries, and automated retraining/validation workflows to orchestrate reproducible training and deployment.

MLOps Pipeline Orchestration

ml-pipeline-workflow

sickn33/antigravity-awesome-skills

Guides engineers through end-to-end MLOps pipeline orchestration, covering data ingestion, preparation, training, validation, deployment, and monitoring with DAG-based automation and best practices.

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.

Model Drift Detector

model-drift-detector

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance on identifying and responding to model drift within ML deployment workflows, offering best practices, production-ready code, and validation support so you can keep inference systems stable.

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