#1
Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
43K starsJun 16, 2026 pushml-automationPythonMachine Learning
$ npx skills add ray-project/rayAlternatives
Autolabel alternatives for AI agents.
Compare similar skills by workflow fit, trust score, quality, GitHub adoption, maintenance, and install readiness.
Current skill
Autolabel
Label, clean and enrich text datasets with LLMs.
74
Quality
81
Trust
2.3K
Stars
#2
Machine Learning Engineering Open Book
18K starsMay 18, 2026 pushml-automationPythonMachine Learning
$ npx skills add stas00/ml-engineering#3
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
80K starsFeb 5, 2026 pushml-automationMachine Learning
$ npx skills add mlabonne/llm-course#4
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama model family and using them on various provider services
18K starsMay 19, 2026 pushml-automationJupyter NotebookMachine Learning
$ npx skills add meta-llama/llama-cookbook#5
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
162K starsJun 16, 2026 pushml-automationPythonMachine Learning
$ npx skills add huggingface/transformers#6
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
31K starsJun 10, 2026 pushml-automationPythonMachine Learning
$ npx skills add Lightning-AI/pytorch-lightning#7
Deep Learning for humans
64K starsJun 12, 2026 pushml-automationPythonMachine Learning
$ npx skills add keras-team/keras#8
💫 Industrial-strength Natural Language Processing (NLP) in Python
34K starsMay 19, 2026 pushml-automationPythonMachine Learning
$ npx skills add explosion/spaCy#9
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
11K starsJul 23, 2026 pushml-automationPythonMachine Learning
$ npx skills add wandb/wandb#10
Low-code framework for building custom LLMs, neural networks, and other AI models
12K starsJun 15, 2026 pushml-automationPythonMachine Learning
$ npx skills add ludwig-ai/ludwig#11
Fast and Accurate ML in 3 Lines of Code
10K starsJun 15, 2026 pushml-automationPythonMachine Learning
$ npx skills add autogluon/autogluon#12
A Python library for anomaly detection across tabular, time series, graph, text, and image data. 60+ detectors, benchmark-backed ADEngine orchestration, and an agentic workflow for AI agents.
9.9K starsJun 5, 2026 pushml-automationPythonMachine Learning
$ npx skills add yzhao062/pyod#13
A python library for user-friendly forecasting and anomaly detection on time series.
9.4K starsJun 6, 2026 pushml-automationPythonMachine Learning
$ npx skills add unit8co/darts#14
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
8.2K starsJun 16, 2026 pushml-automationPythonMachine Learning
$ npx skills add py-why/dowhy#15
A unified framework for machine learning with time series
9.8K starsJun 11, 2026 pushml-automationPythonMachine Learning
$ npx skills add sktime/sktime#16
An open source python library for automated feature engineering
7.7K starsJul 27, 2026 pushml-automationPythonMachine Learning
$ npx skills add alteryx/featuretoolsHow to choose
When should you switch?
Use an alternative when it has a clearer install path, higher trust score, fresher maintenance, or better platform fit for your current agent stack. Keep Autolabel if it already passes your workflow test and repository review.
Next step
Compare top candidates side by side
Open the compare page, test the install commands in a sandbox, and check each repository before using a skill in production.