Skill UI

This guide documents the practical throughput limits and throttling patterns for automating Apple Notes operations (Create, Read, Search, Delete) via scripts like osascript. It provides technical solutions, including TypeScript throttling queues and Bash batch processing examples, to prevent common timeouts (-1712), sync lag, or data loss when performing high-volume, reliable automation on macOS.

This library provides production-ready, type-safe patterns for automating Apple Notes management on macOS. Using JXA and AppleScript wrapped in a Node.js client, it allows developers to easily list, create, search, and perform batch operations on notes, complete with robust error handling and throttling for reliable workflow integration.

A comprehensive solution designed to monitor changes (add, modify, delete) within Apple Notes on macOS. Since Notes lacks a native webhook or event streaming API, this skill provides three robust methods—JXA Polling, FSEvents monitoring, and Shortcuts automation—allowing developers to build reliable, near real-time event systems for integrating Notes data into automated workflows.

This tool provides comprehensive audits for App Store and Google Play listings. It analyzes metadata, visual assets, and user ratings against current ASO best practices. By fetching live data and assessing brand maturity, it generates a prioritized, actionable plan to boost app visibility, improve conversion rates, and enhance overall store ranking.

This guide demonstrates how to set up robust Continuous Integration/Continuous Deployment (CI/CD) pipelines for projects utilizing AssemblyAI's speech-to-text services. It provides detailed GitHub Actions workflows for running both mocked unit tests (free and fast) and live, cost-controlled integration tests. Learn how to safely manage API keys and ensure transcription stability in your automated build process.

A comprehensive guide designed to diagnose and resolve common errors and exceptions encountered when using the AssemblyAI API. It covers critical topics such as authentication failures (401), rate limiting (429), transcription status errors, streaming issues, and LeMUR processing, helping users debug and maintain robust data pipelines.

Execute comprehensive, asynchronous transcription workflows using AssemblyAI. This tool handles file upload, queues jobs, and provides advanced audio intelligence features. Capabilities include speaker diarization, sentiment analysis, named entity recognition (NER), topic categorization (IAB), content safety moderation, and sensitive PII redaction. Ideal for processing meeting recordings, interviews, and large volumes of spoken content.

A comprehensive guide to using AssemblyAI for rich audio processing. This guide covers basic speech-to-text transcription from URLs or local files, along with advanced AI features. Capabilities include speaker diarization, sentiment analysis, key phrase extraction, entity detection, and powerful LLM-powered analysis (LeMUR) for summarization and Q&A. Ideal for integrating deep, multi-faceted audio insights into applications.

This guide provides advanced techniques to optimize AssemblyAI's transcription performance for large-scale applications. Learn how to maximize throughput and minimize latency by implementing caching strategies (Redis/LRU), utilizing webhooks instead of polling, selecting the optimal speech model based on needs, and parallelizing batch processing workloads.

This comprehensive checklist guides the secure and robust deployment of AssemblyAI transcription services to a live production environment. It covers critical pre-deployment checks, health monitoring implementations, webhook-based processing setup, and gradual rollout strategies, ensuring high availability and reliability for mission-critical speech-to-text functions.

This skill provides comprehensive solutions for managing rate limits and throttling when interacting with the AssemblyAI Speech-to-Text API. It implements advanced patterns like exponential backoff with jitter, queue-based concurrency control, and resilient streaming retry logic. Use it when building high-throughput, reliable applications that process large volumes of audio data.

This guide provides production-ready architectural patterns for integrating the AssemblyAI SDK in TypeScript and Python. It details best practices including implementing Singleton clients, robust service wrappers, advanced error handling (like exponential backoff retries), and multi-tenant client management. Use this when building highly scalable and reliable speech-to-text applications.