Skill UI

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.

This tool provides comprehensive Python solutions for processing batches of text-to-video requests using the Kling AI API. It handles controlled parallelism, rate-limiting, and detailed status tracking. Use this when building robust content pipelines that require generating multiple videos efficiently from a list of prompts, supporting synchronous, asynchronous, and webhook callback methods.

A comprehensive guide detailing how to set up secure API authentication for Kling AI using JSON Web Tokens (JWT). This process involves generating tokens from your Access Key (AK) and Secret Key (SK) credentials, providing code examples for Python and Node.js, and implementing best practices for token management and refreshing to ensure continuous API access.

This skill provides comprehensive monitoring and tracking for asynchronous video generation tasks using the Kling AI API. It supports polling single tasks, managing large batches of jobs, and detecting stalled or failed processes across multiple video endpoints. Ideal for developers building robust workflow dashboards, implementing reliable batch processing, or debugging long-running media generation pipelines.

This guide details how to implement webhooks for Kling AI video generation tasks. Instead of inefficiently polling the API status, you can provide a callback URL when submitting a task. When the task completes (whether successful or failed), Kling AI will automatically send a POST request containing the full result to your designated endpoint. This mechanism eliminates polling overhead and is essential for building robust, event-driven video processing pipelines.

Automates Kraken IO workflows by orchestrating Composio toolkit calls through Rube MCP, covering tool discovery, connection checks, and schema-compliant executions for reliable bulk processing.

This guide provides comprehensive techniques to optimize LangChain applications for production environments. Learn how to measure baseline latency, implement efficient caching strategies (in-memory, Redis, SQLite), utilize batch processing with concurrency control, and maximize perceived speed through streaming. Furthermore, it covers prompt optimization and strategic model selection to ensure high throughput and low costs.

This comprehensive guide outlines essential, production-grade patterns for building robust LangChain applications. Learn best practices for implementing type-safe structured output using Zod, handling provider fallbacks, processing inputs concurrently with batching, optimizing user experience with streaming, and ensuring reliability through caching and retries.

Provides a deep dive and step-by-step scripts for complex Langfuse migrations. Covers comprehensive data export (traces, scores, prompts, datasets) and import processes. This guide is essential for platform transitions, moving between different Langfuse instances, or migrating from observability tools like LangSmith, supporting zero-downtime dual-write patterns.

Provides comprehensive strategies to optimize Langfuse tracing overhead for high-throughput applications. This guide covers benchmarking, tuning batch processing configurations, implementing non-blocking wrappers, and optimizing payload sizes. Use this when scaling Langfuse for production workloads to minimize latency and maximize stability.

LibFuzzer is LLVM’s in-process coverage-guided fuzzer for C/C++ projects compiled with Clang, providing a quick harness-based setup that can later transition to more advanced fuzzers while maintaining reproducible, high-throughput testing.

This guide provides the framework for configuring Continuous Integration and Continuous Deployment (CI/CD) pipelines specifically for Lindy AI agent integrations. It covers setting up automated tests for webhook receivers, validating payload processing, and checking authentication signatures using GitHub Actions and Node.js, ensuring robust and reliable deployment of agent services.