Skill UI

A comprehensive guide and implementation framework for managing real-time data flow using Clay's event-driven architecture. This skill covers both inbound webhooks (POSTing data into Clay tables) and outbound HTTP API callbacks (receiving enriched data from Clay). It provides production-ready TypeScript code for setup, signature verification, and idempotent processing, enabling robust, event-driven integrations.

Automated guidance for configuring and applying early stopping callbacks across ML training workflows, covering best practices, code generation, and validation for PyTorch, TensorFlow, and scikit-learn experiments.

This skill guides developers on building scalable, event-driven pipelines around Ideogram's synchronous API. It demonstrates implementing robust async patterns, including using job queues (like BullMQ) for batch generation, handling webhook callbacks for post-processing, and setting up full asset modification pipelines. Ideal for production-grade marketing and content workflows.

This skill provides a comprehensive pattern for building robust, scalable, and fault-tolerant asynchronous video generation pipelines using Kling AI. It implements advanced software engineering patterns, including Redis queues (Producer/Consumer), State Machines, and event-driven callbacks. It is designed for managing complex, multi-step AI workflows—such as generate, extend, and download—ensuring reliable background task processing.

A comprehensive guide to setting up production-grade observability for LangChain applications. It integrates LangSmith tracing for zero-code debugging, custom callbacks for detailed metric collection (latency, token usage, error rate), and Prometheus exporters for robust alerting. This allows developers to monitor LLM pipelines, track performance metrics, and manage operational health in real-time.

This skill provides comprehensive reference architectures for integrating Lindy AI agents into production applications. It covers various complex patterns, including simple webhook callbacks, sophisticated event-driven pipelines, multi-agent delegation systems, scheduled workflows, and RAG-powered customer chat interfaces. Essential for designing and building robust, scalable, and reliable AI-powered systems.

This guide provides expert patterns for implementing secure, full-stack authentication in Next.js using Supabase. It covers essential architectural components, including setting up separate browser and server clients, utilizing middleware for route protection and session refreshing, handling OAuth callbacks, and implementing auth operations safely via Server Actions and Server Components. Essential knowledge for building secure, modern web applications.

Automates oauth callback handler tasks for API integrations, offering best-practice guidance, production-ready code/config, and validation when you mention oauth callbacks, integrations, or request implementation help.

PyTorch Lightning is a high-level framework built on PyTorch that significantly reduces boilerplate code in deep learning training loops. It automatically handles device management, distributed training (DDP, FSDP, DeepSpeed), mixed precision, and advanced callbacks. It allows researchers to scale models from local machines to supercomputers with minimal code changes, enabling clean, scalable, and best-practice deep learning implementations.

PyTorch Lightning Toolkit streamlines PyTorch model training by organizing LightningModules, LightningDataModules, Trainer configs, callbacks, logging, and distributed strategies for scalable multi-GPU/TPU runs in research or production workflows.

This comprehensive guide provides patterns and strategies for migrating existing imperative TypeScript code to functional programming patterns using fp-ts. Learn how to replace traditional structures like try/catch blocks, null checks, and callbacks with robust, type-safe functional equivalents (e.g., Either, Option, Task). It is essential for developers aiming to increase code reliability and maintainability in large TypeScript codebases.

This script performs statistical analysis on Zeek connection logs (conn.log) to detect Command and Control (C2) beaconing patterns. It leverages the ZAT library to load data into Pandas DataFrames, calculates inter-arrival time statistics, and flags connections with low standard deviation relative to the mean. This technique is crucial for network threat hunting and security incident investigation when identifying malicious, periodic callbacks.