machine-learning plugins

by Hugging Face

Inspect models, datasets, Spaces, and research

by Claude Code Plugins

Train and optimize machine learning models with automated workflows

by Claude Code Plugins

Train and optimize machine learning models with automated workflows

by Claude Code Plugins

Train and optimize machine learning models with automated workflows

by Jananthan Paramsothy

Data-first ML discipline: forces coding agents through a strict 16-step pipeline — inspect and understand the raw data, define the prediction problem, clean, engineer, and split it BEFORE any model training — with explicit user-permission gates, marimo notebooks, and matplotlib visuals at every step.

by Amazon Web Services

Build, train, and deploy AI models with deep AWS AI/ML expertise brought directly into your coding assistants, covering the surface area of Amazon SageMaker AI.

by shepsci

Independent, unofficial Kaggle.com workflow plugin: CLI, MCP, competition reports, datasets, models, notebooks, forums, writeups, submissions, and badge collection. Not affiliated with, endorsed by, or sponsored by Kaggle or Google.

by aiopshwang

Evidence-first AI agent skills for reliable data analysis, machine learning, and reproducible model validation.

by Matt Corbett

ML-engineering (MLOps) team — agents (ml-platform-architect, training-pipeline-engineer, model-serving-engineer, ml-monitoring-engineer) for the PRODUCTION lifecycle of ML models: the platform/architecture (build-vs-buy, the stack, the train->register->serve->monitor loop), reproducible training pipelines + experiment tracking + a model registry, feature stores and train/serve consistency (avoiding training-serving skew and leakage), model serving (online vs batch, shadow/canary), monitoring (data + concept drift, decay, retraining triggers), and computer-vision MLOps (task->architecture + edge-vs-cloud inference). skills, a decision-tree knowledge bank (serving-pattern + retraining + computer-vision trees + a dated 2026 map), best-practices, templates, commands, an advisory hook. Seams: significance -> applied-statistics, data pipelines -> data-platform/data-streaming, LLM/agent apps -> claude-app-engineering, deploy -> devops-cicd/cloud-native-kubernetes. Requires ravenclaude-core@>=0.7.0.

by Sistemas IGB

Nueve skills para Claude Code que trabajan con criterio empresarial: arranque de proyectos con interrogatorio de requisitos, Docker + NGINX, CI/CD, SonarQube, pruebas de carga con K6, automatizaciones en n8n, IA generativa (LLM, RAG, agentes), modelos predictivos y MLOps. En español, agnósticas de stack.