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Retell AI Voice Agent Migration Patterns

retellai-upgrade-migration

jeremylongshore/claude-code-plugins-plus-skills

This skill provides comprehensive implementation patterns and detailed steps for migrating and upgrading Retell AI integrations. It is designed for developers building advanced AI voice agents, phone call automation, and complex telephony workflows using the Retell SDK. It covers setup procedures, best practices, and standard error handling for robust deployment.

Rev AI Workflow Automation

rev-ai-automation

ComposioHQ/awesome-claude-skills

Guide to automating Rev AI tasks through Composio’s Rube MCP toolkit, covering setup, mandatory tool discovery, connection checks, and schema-driven execution best practices for reliable results.

RWKV Hybrid Architecture

rwkv-architecture

Orchestra-Research/AI-Research-SKILLs

RWKV is an RNN+Transformer hybrid architecture with linear-time inference, infinite context, and GPT-style training. It delivers constant-memory, streaming generation on CUDA and CPU, ideal for long-context, memory-constrained deployments.

Sagemaker Endpoint Deployer

sagemaker-endpoint-deployer

jeremylongshore/claude-code-plugins-plus-skills

Auto-activating assistant that guides AWS Sagemaker endpoint deployment, from best practices to production-ready code, configurations, and validation for inference serving and monitoring workflows.

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.

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.

Similarity Search Patterns Implementation

similarity-search-patterns

sickn33/antigravity-awesome-skills

This pattern provides established best practices for implementing efficient similarity search in production systems. It is crucial for building semantic search engines, implementing Retrieval-Augmented Generation (RAG), creating advanced recommendation systems, and optimizing retrieval performance when scaling to millions of vectors.

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.

Automated Documentation to AI Skills

sickn33/antigravity-awesome-skills

This skill provides an automated solution for transforming diverse, unstructured technical data sources—including documentation websites, GitHub repositories, and PDFs—into structured, actionable 'Claude AI skills.' It significantly streamlines the process of knowledge base creation, data structuring, and preparing large volumes of technical information for advanced AI applications.

Skyfire Automation Guide

skyfire-automation

ComposioHQ/awesome-claude-skills

Documented steps to automate Skyfire operations through Composio’s Rube MCP endpoints, covering tool discovery, connection checks, execution patterns, and schema compliance for reliable workflows.

SLIME RL Training

slime-rl-training

Orchestra-Research/AI-Research-SKILLs

Guides post-training of GLM-class LLMs with Megatron-LM and SGLang, focusing on GRPO rollout generation, custom data buffers, and tight RL scaling workflows for research and production use.

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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