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Databricks MLflow Workflow

databricks-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

Guides ML engineers through the Databricks secondary workflow that links Feature Store engineering, MLflow experiment tracking, model registry versioning, and serving so production-grade models can be trained and deployed.

Feature Engineering Helper

feature-engineering-helper

jeremylongshore/claude-code-plugins-plus-skills

Automatically activates when you mention feature engineering helper, offering guidance on data preparation, model training, hyperparameter tuning, and experiment tracking for ML projects.

Hyperparameter Tuner Assistant

hyperparameter-tuner

jeremylongshore/claude-code-plugins-plus-skills

Automated guidance for hyperparameter tuning within ML training workflows, offering best practices, production-ready code, and validation across modeling stages like data prep, training, and experiment tracking.

Langfuse LLM Evaluation And Benchmarking

langfuse-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive workflow for implementing robust LLM evaluation using Langfuse. This skill guides users through key processes including scoring traces (numeric, categorical, boolean), collecting user feedback, managing versioned prompts, creating structured datasets, running automated experiments, and implementing LLM-as-a-Judge patterns for advanced quality assurance and system benchmarking.

Mixed Precision Trainer

mixed-precision-trainer

jeremylongshore/claude-code-plugins-plus-skills

Automates mixed precision ML training workflows by surfacing best practices, generating runnable code/configurations, and validating setups across data prep, model tuning, and experiment tracking.

Mlflow Tracking Setup

mlflow-tracking-setup

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance for configuring mlflow experiment tracking, covering best practices, code generation, and validation to support ML training workflows.

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 Checkpoint Manager

model-checkpoint-manager

jeremylongshore/claude-code-plugins-plus-skills

Automated guidance for managing model checkpoints in ML training workflows, offering best practices, sample code, and validation to keep experiments stable and reproducible.

Model Explainability Assistant

model-explainability-tool

jeremylongshore/claude-code-plugins-plus-skills

Automated guidance for model explainability tooling within ML training workflows, giving step-by-step best practices, production-ready code, and validation for experiments whenever users mention explainability tool needs.

Mergekit Model Fusion

Orchestra-Research/AI-Research-SKILLs

Combines fine-tuned models with Mergekit to blend math, coding, and chat expertise without retraining, covering SLERP, TIES, DARE, Task Arithmetic, and linear merges for rapid experimentation and deployment.

OpenRouter Model Routing

openrouter-model-routing

jeremylongshore/claude-code-plugins-plus-skills

Implements advanced OpenRouter model routing with A/B testing, gradual rollouts, and performance-aware selection to optimize multi-model deployments and experiments across environments.

Phoenix AI Observability

phoenix-observability

Orchestra-Research/AI-Research-SKILLs

Phoenix is an open-source observability platform for tracing, evaluating, and monitoring LLM applications; use it to debug prompts, run dataset evaluations, build experiment pipelines, and watch production inference in real time.

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