Skill UI

Arboreto is a powerful Python library designed to infer Gene Regulatory Networks (GRNs) from various gene expression datasets, including bulk and single-cell RNA-seq. It employs scalable algorithms like GRNBoost2 and GENIE3 to identify critical transcription factor-target gene relationships. Featuring support for Dask distributed computing, Arboreto is optimized for large-scale, high-throughput transcriptomic analysis.

DeepTools is a comprehensive command-line toolkit for processing and analyzing high-throughput sequencing data. It supports workflows for ChIP-seq, RNA-seq, ATAC-seq, and other NGS experiments. Core capabilities include converting BAM files to normalized coverage tracks (bigWig), performing quality control (QC), sample correlation analysis, and generating publication-quality visualizations, such as heatmaps and profile plots across genomic regions.

A comprehensive guide for developers to optimize performance when integrating Adobe APIs (Firefly, PDF, Photoshop). Techniques covered include implementing token caching, parallelizing asynchronous job submissions, leveraging response caching (LRU), and optimizing connection handling to minimize latency and maximize throughput.

A comprehensive guide and utility for managing API interactions with BambooHR. It implements robust retry logic (with exponential backoff and jitter) to handle 503 and 429 rate limit errors. It also provides patterns for proactive optimization, such as queue-based throttling, batching data using custom reports, and utilizing incremental sync to maximize API throughput and reliability.

This comprehensive guide details advanced techniques for optimizing Bright Data API scraping performance. It covers implementing connection pooling, robust response caching layers (LRU Cache), and managing concurrent requests with backpressure control. Use this when facing slow API responses or requiring high-throughput, scalable data extraction.

A comprehensive guide for scaling Claude usage in high-throughput, enterprise-level applications. Learn four advanced strategies, including Message Batches for bulk processing, using p-limit for concurrency control, managing rate limit tier upgrades, and selecting the optimal model (e.g., Haiku) to balance speed and quality.

This guide outlines advanced strategies to optimize latency and throughput when integrating Anthropic's Claude API. Learn critical techniques such as streaming responses, prompt caching, model selection (Haiku, Sonnet, Opus), and request parallelization, ensuring your applications provide a low-latency, high-performance user experience.

A comprehensive guide for managing Anthropic API rate limits (RPM, TPM). Learn how to interpret rate limit headers, implement robust backoff strategies (exponential backoff with jitter), and optimize throughput using techniques like message batching and prompt caching to ensure reliable, high-volume API calls.

This guide provides comprehensive techniques for optimizing ElevenLabs Text-to-Speech (TTS) performance. Learn how to reduce latency and maximize throughput by strategically selecting models (e.g., Flash vs. v3), implementing streaming endpoints (HTTP or WebSocket), and choosing optimal audio formats. It is essential for real-time conversational AI, IVR systems, and high-volume content generation.

A robust utility for managing API calls to ElevenLabs, handling complex rate limiting, concurrency queuing, and quota monitoring. It implements plan-aware concurrency controls (managing 'too_many_concurrent_requests') and exponential backoff strategies for system overloads ('system_busy'). This is essential for building reliable, high-throughput Text-to-Speech applications.

Provides robust patterns for handling API rate limits, throttling, and connection errors when integrating with the Framer API. It covers implementing exponential backoff, retry mechanisms, batching large CMS writes, and debouncing plugin operations to ensure reliable and high-throughput API calls.

Provides advanced best practices for optimizing Glean's enterprise search performance and scalability. Covers critical techniques such as implementing effective batch sizing, caching strategies, connection pooling, and robust rate limit management to significantly reduce search latency and maintain high-throughput indexing for massive, diverse datasets.