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Retell AI Voice Agent Integration

retellai-deploy-integration

jeremylongshore/claude-code-plugins-plus-skills

This skill provides implementation patterns for integrating Retell AI into voice agents and telephony platforms. It enables the automation of complex phone calls and voice interactions using the Retell SDK, making it essential for building sophisticated AI customer service systems and automated call centers.

Retell AI Voice Agent Checklist

retellai-prod-checklist

jeremylongshore/claude-code-plugins-plus-skills

This guide provides a comprehensive production checklist and SDK pattern for integrating Retell AI into voice agents and telephony systems. It covers the necessary steps, error handling, and implementation patterns for deploying reliable, AI-powered phone call automation and voice interactions in a production environment.

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.

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.

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.

SGLang Structured Inference Service

Orchestra-Research/AI-Research-SKILLs

SGLang is a high-performance serving framework for LLMs and VLMs that uses RadixAttention prefix caching to deliver structured outputs (JSON/regex/grammar) and agentic workflows with function calls, providing up to 5× faster inference than vLLM across multi-GPU production deployments.

Streaming Inference Setup

streaming-inference-setup

jeremylongshore/claude-code-plugins-plus-skills

Automates guidance for streaming inference setup, covering best practices in ML deployment, serving, monitoring, and production-ready configurations whenever you ask about streaming inference patterns or implementation.

Tensorflow SavedModel Creator

tensorflow-savedmodel-creator

jeremylongshore/claude-code-plugins-plus-skills

Automates TensorFlow SavedModel deployment support with step-by-step guidance, best-practice patterns, production-ready code/config generation, and validation for serving, monitoring, and optimization scenarios.

TensorRT LLM Optimizer

Orchestra-Research/AI-Research-SKILLs

Optimizes large language model inference on NVIDIA GPUs with TensorRT, delivering 10-100× faster throughput, quantized precision (FP8/INT4), multi-GPU scaling, and serving-ready tooling for production deployments.

Torchscript Exporter

torchscript-exporter

jeremylongshore/claude-code-plugins-plus-skills

Assists with torchscript exporter tasks for ML deployment, offering automated guidance, code/config generation, and validation against industry standards whenever torchscript exporters are mentioned.

Torchserve Config Generator

torchserve-config-generator

jeremylongshore/claude-code-plugins-plus-skills

Automates generating TorchServe deployment configurations with guided best practices for model serving, MLOps pipelines, inference, and production monitoring so ML deployment workflows stay consistent and reliable.

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