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Segment Anything Model

segment-anything-model

Orchestra-Research/AI-Research-SKILLs

Provides Meta AI’s Segment Anything Model for zero-shot image segmentation via interactive prompts or automatic mask generation, enabling rapid dataset creation and integration in vision pipelines.

Production Computer Vision Engineering Pipeline

senior-computer-vision

alirezarezvani/claude-skills

Comprehensive skill set covering the full lifecycle of advanced visual AI systems. Expertise includes object detection (YOLO, Faster R-CNN), instance and semantic segmentation (Mask R-CNN, SAM), model training using PyTorch, and production deployment optimization using ONNX and TensorRT for edge or cloud targets.

Advanced Data Pipeline Engineering Guide

senior-data-engineer

alirezarezvani/claude-skills

This comprehensive guide details the expertise needed for a Senior Data Engineer. It covers building, optimizing, and governing scalable data pipelines and robust ETL/ELT systems using modern tools like Spark, Kafka, Airflow, and dbt. Includes decision frameworks for architecting data solutions (Batch vs Streaming, Lakehouse vs Warehouse) and implementing advanced data modeling patterns.

Senior Data Scientist Skill

senior-data-scientist

alirezarezvani/claude-skills

Guides senior data scientists through designing controlled experiments, engineering structured features, training ML models, and translating results into decisions using Python, R, SQL, and tools like Scikit-learn, XGBoost, and MLflow.

Production ML Engineering

senior-ml-engineer

alirezarezvani/claude-skills

Guides production ML engineering with patterns for deploying models, building MLOps pipelines, monitoring drift, integrating LLMs and RAG systems, and enforcing feature-store, retraining, and cost controls in live environments.

Advanced Security Engineering Toolkit

senior-security

alirezarezvani/claude-skills

A comprehensive toolkit for professional security engineers. It covers the entire security lifecycle, including systematic threat modeling using STRIDE, designing secure architectures based on Zero Trust and Defense-in-Depth, conducting detailed vulnerability assessments (SAST/DAST), and implementing secure coding practices. Ideal for security audits, compliance reviews, and building resilient systems.

Sentence Embedding Framework

sentence-transformers

Orchestra-Research/AI-Research-SKILLs

Sentence Transformers provides 5000+ pre-trained models that generate sentence, text, and image embeddings for RAG, semantic search, clustering, and multilingual production workloads without relying on external APIs.

Sequelize Model Creator

sequelize-model-creator

jeremylongshore/claude-code-plugins-plus-skills

Automates Sequelize model creation for backend development, offering step-by-step guidance, best practices, and production-ready code/config validation when working with Node.js, Python, Go, databases, caching, messaging, and microservices.

SIEM Rule Generator

siem-rule-generator

jeremylongshore/claude-code-plugins-plus-skills

Automates SIEM rule generation within advanced security workflows, guiding through industry best practices, compliance checks, and threat modeling to deliver production-ready configurations verified against common standards.

Simple Preference Optimization for LLM Alignment

Orchestra-Research/AI-Research-SKILLs

SimPO (Simple Preference Optimization) is a state-of-the-art, reference-free method designed for aligning Large Language Models (LLMs) using human preference data. It is an efficient alternative to DPO and PPO, notably outperforming DPO without requiring a separate reference model. It is ideal for practitioners who need faster, simpler, and more resource-efficient fine-tuning for preference alignment.

Sklearn Pipeline Builder

sklearn-pipeline-builder

jeremylongshore/claude-code-plugins-plus-skills

Automates sklearn pipeline builder tasks within ML training, activating on relevant triggers to guide data prep, model training, hyperparameter tuning, and experiment tracking with production-ready code and standards validation.

Sparse Autoencoders for Model Interpretability

sparse-autoencoder-training

Orchestra-Research/AI-Research-SKILLs

SAELens provides a framework for training and analyzing Sparse Autoencoders (SAEs). SAEs decompose the dense, often polysemantic activations of large language models into sparse, monosemantic features. Use this when you need to discover the discrete, interpretable concepts a model has learned, study feature superposition, or analyze specific safety-relevant behaviors (like bias or deception) within deep neural networks.

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