Discover tools machine learning skills, compare source repositories and read the instructions for each workflow.
Set up AI inference servers on cloud GPUs. Create private LLM APIs (vLLM, TGI), image generation endpoints, embedding services, and more. All with OpenAI-compatible interfaces that work with existing tools.
Specialized skill for ML training workflows on cloud GPUs. Fine-tune LLMs with LoRA/QLoRA, train image LoRAs, build classifiers, and run custom training jobs. Generates production-ready training pipelines with...
Expert guidance for using the Pal MCP Server (zen-pal-nas). This skill should be used when working with multi-model AI orchestration, tool workflows (chat, thinkdeep, planner, consensus, debug, codereview, prec...
Evaluate skills by executing them across sonnet, opus, and haiku models using sub-agents. Use when testing if a skill works correctly, comparing model performance, or finding the cheapest compatible model. Retu...
Debug Scikit-learn issues systematically. Use when encountering model errors like NotFittedError, shape mismatches between train and test data, NaN/infinity value errors, pipeline configuration issues, converge...
Debug TensorFlow and Keras issues systematically. This skill helps diagnose and resolve machine learning problems including tensor shape mismatches, GPU/CUDA detection failures, out-of-memory errors, NaN/Inf va...
Refactor Scikit-learn and machine learning code to improve maintainability, reproducibility, and adherence to best practices. This skill transforms working ML code into production-ready pipelines that prevent d...
This skill should be used when users ask about which AI model to use for coding, mentions 'cost', 'batch', 'API', 'configure analysis', wants to process multiple documents, or needs to understand model capabili...
Generates a rigorous experiment design given a hypothesis. Use when asked to design experiments, plan experiments, create an experimental setup, or figure out how to test a research hypothesis. Covers controls,...
Guides you through implementing a research paper step-by-step from scratch. Use when asked to implement a paper, code up a paper, reproduce research results, or build a model from a paper. Focuses on building u...
Create, configure, and deploy Hugging Face Spaces for showcasing ML models. Supports Gradio, Streamlit, and Docker SDKs with templates for common use cases like chat interfaces, image generation, and model comp...
LLM fine-tuning and prompt-tuning techniques
LLM evaluation frameworks, benchmarks, and quality metrics for production systems.
LLM architecture, tokenization, transformers, and inference optimization. Use for understanding and working with language models.
LLM fine-tuning with LoRA, QLoRA, and instruction tuning for domain adaptation.
Prompt design, optimization, few-shot learning, and chain of thought techniques for LLM applications.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch jobs, scheduling tasks, serving APIs with GPU acceleration, or scaling compute-intensi...
Master statistical analysis with hypothesis testing, A/B testing, regression, and statistical methods for data-driven decisions.
Use SAP-RPT-1-OSS open source tabular foundation model for predictive analytics on SAP business data. Handles classification and regression tasks including customer churn prediction, delivery delay forecasting,...
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Elite AI/ML Senior Engineer with 20+ years experience. Transforms Claude into a world-class AI researcher and engineer capable of building production-grade ML systems, LLMs, transformers, and computer vision so...
Use this skill for multi-step development tasks. Latent Chain Mode reduces token usage by 70-80% through hidden-state reasoning with 4-phase workflow: analysis → plan → impl → review.
Implement agent evaluation and safety gates using MLflow 3.x. Use for creating LLM-as-Judge scorers, evaluation datasets, quality gates, tracing, and continuous evaluation. Triggers on "evaluate agent", "MLflow...
Optimizes outputs by interpreting underspecified prompts before execution. Analyzes user input, infers missing context and specifications, then executes an enhanced version. Produces better results directly rat...
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.