Claude Skills by VectorSpaceLab

"Use this sub-skill for MLAlgorithms clustering, Gaussian mixtures,

"Route ML-From-Scratch educational supervised, unsupervised,

"Assemble and debug ML-From-Scratch NeuralNetwork, layers,

"Operate ML-From-Scratch DeepQNetwork CartPole workflows, model

"Select, fit, predict, and debug ML-From-Scratch supervised

"Run and debug ML-From-Scratch unsupervised PCA, clustering,

"Routes self-contained ML Glossary knowledge for machine-learning

"Explains ML Glossary foundations including glossary terms,

"Explains ML Glossary classical algorithm families including

"Explains ML Glossary neural-network concepts, forward and

"Use this skill for MLflow repository work: experiment tracking,

"Use for MLflow GenAI observability work: tracing, trace

"Use this sub-skill for MLflow Models, pyfunc, flavor APIs,

"Use when working with MLflow CLI commands, tracking servers,

"Use this MLflow sub-skill for experiment tracking, runs, params,

"Use MLJAR AutoML for tabular classification, regression,

"Generate, inspect, serve locally, and publish Mercury prediction

"Use mljar-supervised AutoML artifacts, reports, structured

"Prepare MLJAR-Supervised data, targets, preprocessing choices, and

Configure and inspect fairness-aware AutoML workflows in mljar-supervised.

"Train, configure, evaluate, and use supervised.AutoML for tabular

"Use MLX Audio for local TTS, STT, speech enhancement, VAD,

"Use MLX Audio for the API server, realtime WebSockets, Studio UI

"Use MLX Audio for speech enhancement, source separation, VAD,

"Use MLX Audio STT workflows for transcription, streaming ASR,

"Use MLX Audio TTS workflows for generation, voice cloning,

"Use mlxtend machine-learning extension utilities for estimator

"Use mlxtend sklearn-style classifiers, regressors, Kmeans, voting

"Use mlxtend.evaluate metrics, validation splitters, resampling

"Use mlxtend feature selection, feature extraction, preprocessing,

"Mine frequent itemsets and association rules from transaction data

"Use mlxtend plotting helpers, packaged data loaders, file IO,

"Use MMAction2 for video understanding inference, datasets/configs,

"Prepare and validate MMAction2 data annotations, pipelines, config

"Use MMAction2 inference APIs, inferencers, labels, visualization

"Understand MMAction2 model families, registries, customization,

"Plan MMAction2 training, testing, distributed launch, evaluation,

"Route MMAudio video-to-audio, text-to-audio, data preparation,

Prepare MMAudio audio/video feature manifests, memmaps, and extraction plans.

"Operate MMAudio batch evaluation, evaluation dataset wiring, and

"Operate MMAudio inference for text-to-audio, video-to-audio,

"Route MMAudio DDP training, smoke runs, checkpoint resume, EMA

"Use MMCV 2.2.0 for computer-vision media utilities, transform

"Build CPU-safe PyTorch CNN layers and model helpers with MMCV

"Compose, customize, inspect, and troubleshoot MMCV dict-style data

"Use MMCV image, video, optical-flow, visualization, and array

"Choose MMCV package variants, inspect compiled ops availability,

"Route MMDeploy model deployment, backend setup, SDK runtime,

"Select, install, check, and troubleshoot MMDeploy inference

"Convert OpenMMLab/PyTorch models with MMDeploy, including IR