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SkillsMP has collected 18 skills from damionrashford/mlx. Open a skill to review its source and details.
Showing 18 of 18 collected skills.
Comprehensive Claude Code knowledge base — plugins, hooks, skills, agents, MCP, channels, headless mode, permissions, settings, and all extensibility features. Use when building, configuring, debugging, or extending Claude Code.
Use when building a skill, creating a SKILL.md, packaging a workflow, making a slash command, or asked "how do I make a skill". Scaffolds the folder, generates SKILL.md from a template, validates against spec. Produces a complete ready-to-deploy skill folder:…
Statistical analysis, hypothesis testing, A/B testing, cohort analysis, segmentation, trend detection, business metrics, pre-delivery validation, and data visualization. Use when the user asks to "analyze this data", "run a statistical test", "compare…
Autonomous time-budget experiment loop. Modify a training script, train for a fixed wall-clock budget, evaluate, record, repeat. Inspired by karpathy/autoresearch. Use for overnight architecture search, systematic hyperparameter sweeps, or any iterative model…
Context engineering for building production LLM applications: context window management, degradation patterns, optimization strategies, memory system selection, multi-agent architecture, filesystem context patterns, and tool design principles. Use when…
Explore, clean, and engineer datasets end-to-end: statistical profiling, distribution checks, missing value analysis, duplicate detection, outlier removal, type fixing, encoding, create features, encode categories, transform columns, add rolling windows,…
Detect data drift, concept drift, and model performance degradation in production. Uses PSI, KS-test, and chi-squared for statistical drift, plus evidently and nannyml for automated reports. Use when monitoring a deployed model or comparing training vs…
Systematic evaluation of ML models, experiments, and AI system outputs. Multi-dimensional rubrics, LLM-as-judge, bias detection, and structured comparison frameworks. Use when the user asks to "evaluate model performance", "compare models", "build evaluation…
Explain model predictions with SHAP, LIME, integrated gradients, and permutation importance. Generates summary plots, waterfall charts, and force plots. Use when debugging predictions, auditing for bias, or communicating model behavior to stakeholders.
Fine-tune language models with LoRA, QLoRA, or full fine-tuning. Covers unsloth (4x memory reduction), PEFT, trl SFTTrainer, DPO, instruction tuning with chat templates, dataset preparation, and evaluation. Use when fine-tuning any HuggingFace model on custom…
Interactive ML education with 3 university-grade courses (CS229 Stanford, Applied ML Python, ML Engineering), 36+ structured lessons, decision frameworks, and interview prep. Supports study, quiz, explain, design, debug, and progress modes. Use when the user…
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or…
Extract content from YouTube videos and generate podcasts, video overviews, quizzes, flashcards, reports, and slide decks from research papers using Google NotebookLM. Use when the user wants to extract a YouTube transcript, analyze a video, turn a paper into…
On-demand ML/data science library expert. Use when the user asks how to use any function, class, or method from NumPy, Pandas, scikit-learn, Matplotlib, TensorFlow, Keras, PyTorch, Seaborn, SciPy, statsmodels, XGBoost, LightGBM, Hugging Face Transformers,…
Create, clean, organize, optimize, and convert Jupyter notebooks. Build new notebooks from scratch with proper cell structure, cell IDs, and Colab compatibility. Extract reusable functions, add documentation, generate requirements.txt, and convert to scripts.…
Search, fetch, download, and extract ML/AI research papers from 7 free academic sources. Find and download ML datasets from 5 free sources (HuggingFace, OpenML, UCI, Papers with Code, Kaggle). Review a paper, critique methodology, assess reproducibility,…
Compress, deploy, and serve trained ML models in production. Covers model compression (quantization, pruning, distillation, ONNX export), inference APIs, containerization, CI/CD pipelines, monitoring, health endpoints, model versioning, and reproducibility…
Train ML models and iterate systematically with experiment tracking. Full coverage of supervised learning: Naive Bayes, KNN, Discriminant Analysis (LDA/QDA), SVM/SVR, Decision Trees, Ensemble Methods (Random Forest, XGBoost, LightGBM), GLM (Poisson, Gamma,…