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Azure ML SDK Python

sickn33/antigravity-awesome-skills

Client library to manage Azure Machine Learning resources—workspaces, datasets, compute, jobs, models, environments, and pipelines—directly from Python with MLClient authentication, configuration, and best practices for reproducible workflows.

Azure ML SDK Python

sickn33/antigravity-awesome-skills

Client library to manage Azure Machine Learning resources—workspaces, datasets, compute, jobs, models, environments, and pipelines—directly from Python with MLClient authentication, configuration, and best practices for reproducible workflows.

Claude Reflect Learning

jeremylongshore/claude-code-plugins-plus-skills

Captures user corrections during Claude sessions, queues them for review, and syncs accepted learnings into CLAUDE.md so future runs use the verified tools and conventions; run `/reflect` whenever you complete meaningful work or explicitly ask Claude to remember something.

Session Learning Patterns

continuous-learning

affaan-m/everything-claude-code

Automatically evaluates Claude Code sessions at the Stop hook, identifies reusable patterns, and stores them as learned skills for future reuse, helping you capture troubleshooting steps, debugging techniques, and project conventions without manual effort.

Instinct Learning Engine

continuous-learning-v2

affaan-m/everything-claude-code

Observes Claude sessions via deterministic hooks, distills atomic instincts with confidence scoring, and evolves them into reusable skills, commands, or agents for automated workflows.

Automated Model Deployment

deploying-machine-learning-models

jeremylongshore/claude-code-plugins-plus-skills

Deploying Machine Learning Models automates serving trained models to production by analyzing requirements, generating API and infrastructure code, and orchestrating deployment workflows with validation, error handling, and monitoring baked in.

Feature Engineering Toolkit

engineering-features-for-machine-learning

jeremylongshore/claude-code-plugins-plus-skills

Feature Engineering Toolkit automates creating, selecting, and transforming dataset features to improve machine learning models; use it whenever you need new interactions, automatic selection, scaling, or encoding to boost accuracy and interpretability.

Explaining Machine Learning Models

explaining-machine-learning-models

jeremylongshore/claude-code-plugins-plus-skills

Provides interpretable explanations for ML predictions using SHAP, LIME, and other techniques, helping debug model behavior, highlight feature importance, and communicate fairness insights to stakeholders.

Flask ML API Creator

flask-ml-api-creator

jeremylongshore/claude-code-plugins-plus-skills

Automates Flask-based machine learning API deployment by guiding you through best practices, generating production code/configurations, and validating outputs for serving, monitoring, and MLOps pipelines.

Gamma Hello World

gamma-hello-world

jeremylongshore/claude-code-plugins-plus-skills

Minimal Gamma integration sample showing how to initialize the SDK, create a simple three-slide presentation, and handle common setup errors; ideal for smoke testing new Gamma projects or learning basics.

Langfuse Trace Starter

langfuse-hello-world

jeremylongshore/claude-code-plugins-plus-skills

Provides a minimal Langfuse trace example that initializes the client, records spans and generations, flushes data, and logs a trace URL—ideal when starting integrations, validating setup, or learning trace patterns.

Learning Rate Scheduler

learning-rate-scheduler

jeremylongshore/claude-code-plugins-plus-skills

Provides automated guidance for configuring learning rate schedulers during ML training, covering best practices, setup patterns, and framework-ready code snippets for PyTorch, TensorFlow, and Scikit-learn while validating against common standards.

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