Skill UI

Transforms messy invoices, receipts, and bank docs into structured folders by reading files, extracting vendors, dates, amounts, and descriptions, renaming them consistently, and sorting copies ready for tax prep or accountant handoff.

Automate every aspect of your Jotform workflows—list/search forms, inspect account usage, browse activity logs, organize labels, and compare plan limits—just by issuing natural language commands through the Rube MCP integration.

This architecture provides a scalable, production-ready framework for integrating AI candidate analysis with Juicebox. It manages automated data ingestion from job descriptions, ranks candidates using intelligent scoring, handles result caching, and facilitates seamless data export to major ATS platforms like Greenhouse and Lever. Designed for high-volume, complex hiring pipelines requiring data freshness, deduplication, and robust scaling.

This is a production-grade reference architecture for building scalable video generation platforms powered by Kling AI. It details the complete pipeline, from the API gateway and job queuing (Redis/SQS) to the worker pool, object storage, and CDN delivery. Use this guide when designing high-throughput, resilient, and scalable AI video systems.

This skill addresses the challenge of handling temporary CDN links for videos generated by Kling AI. It provides a comprehensive, multi-cloud solution to download the video asset and persistently upload it to major cloud storage platforms, including AWS S3, Google Cloud Storage (GCS), and Azure Blob Storage. It facilitates building robust, long-term media pipelines and ensures metadata preservation.

Provides deep expertise in modern Kotlin patterns, covering structured concurrency via Coroutines and Flow, Multiplatform architecture (KMP), declarative UI using Compose, and server implementation with Ktor. Ideal for building robust, type-safe, and highly maintainable applications across various platforms.

This skill provides a comprehensive guide for deploying LangChain chains and agents into production-grade APIs. It supports multiple development stacks, including Python (LangServe/FastAPI) and Node.js (Express). Topics covered include containerization using Docker, setting up robust health checks, and deploying to major cloud platforms like Google Cloud Run and AWS Lambda. Learn how to build scalable, observable, and enterprise-grade API endpoints for advanced LLM applications.

This skill provides comprehensive methods for managing the entire data lifecycle of LLM observability platforms like Langfuse. Functions include exporting detailed traces and scores, configuring retention policies (e.g., automatic data cleanup), handling GDPR subject requests (access and erasure), and anonymizing Personally Identifiable Information (PII) for secure analytics. Essential for maintaining regulatory compliance in AI applications.

This comprehensive guide provides step-by-step instructions for integrating Langfuse LLM observability into various cloud and serverless environments. It covers best practices for deployment on platforms such as Vercel, AWS Lambda, Google Cloud Run, and self-hosted Docker, ensuring robust tracing and monitoring of entire AI workflows.

A comprehensive guide for deploying the application layer that interacts with Lindy AI agents. This includes setting up webhook receivers, managing callback handlers, and ensuring production readiness across multiple platforms like Docker, Vercel, and AWS. It covers prerequisites, multi-step deployment instructions, post-deployment verification scripts, and rollback plans for robust MLOps practices.

A comprehensive guide detailing advanced strategies for migrating complex, rule-based automations from platforms like Zapier, Make, n8n, or custom codebases to Lindy AI. It teaches users how to redesign workflows, leveraging AI agents to replace rigid conditional logic with adaptable reasoning, ensuring a smooth and intelligent transition across multiple phases.

Provides a deep dive into migrating issue tracking data from major platforms like Jira, Asana, and GitHub to Linear. This guide covers the entire lifecycle, including initial workflow assessment, status and priority mapping, data export utilizing API examples, and detailed transformation logic required to ensure data integrity and smooth adoption within Linear.