Skill UI

Latch is a Python framework designed for building, deploying, and managing complex, serverless bioinformatics pipelines. It allows users to define workflows using decorators for native Python, Nextflow, or Snakemake, while providing robust tools for cloud data management (LatchFile/LatchDir) and precise resource allocation (GPU/CPU). Ideal for running large-scale analyses such as RNA-seq and protein structure prediction (AlphaFold).

Modal delivers cloud-native serverless Python with on-demand GPUs, persistent storage, and web endpoints to deploy ML workloads, batch jobs, and APIs without writing Dockerfiles.

polars-bio accelerates genomic interval math and bioinformatics I/O by combining Polars, Arrow, and DataFusion to handle BED/VCF/BAM/GFF/FASTA streams, SQL queries, and cloud storage with overlap, coverage, and nearest APIs plus streaming fallbacks.

PufferLib is a high-performance library designed for advanced reinforcement learning simulations and training. It excels in parallel environment simulation, offering optimized vectorization and native support for multi-agent systems. Utilizing algorithms like PuffeRL (optimized PPO+LSTM), it achieves millions of steps per second, significantly accelerating research and deployment across single-agent and multi-agent environments.

Qiskit is the world's leading open-source framework for building, optimizing, and executing quantum circuits. It provides comprehensive capabilities for quantum error mitigation, running algorithms on diverse hardware backends (including IBM Quantum and simulators), and supporting applications in chemistry and machine learning. Essential for researchers entering the quantum computing field.

This skill provides comprehensive, real-time research information lookup capabilities. It features intelligent backend routing, automatically selecting between fast general web search (primary), deep multi-source synthesis (for exhaustive research), or specialized academic database searches (for papers and scholarly articles). Ideal for literature verification, academic background research, and gathering market intelligence.

Conversational research ideation assistant that helps scientists generate novel hypotheses, explore interdisciplinary analogies, challenge assumptions, and outline experiment pathways during early-stage planning.

Stable Baselines3 is a robust, PyTorch-based library providing production-ready implementations of core reinforcement learning algorithms (PPO, SAC, DQN, etc.). It facilitates the training of single-agent RL models, supports the creation of custom Gymnasium environments, and handles advanced parallel training via vectorized environments. It is ideal for rapid prototyping and rigorous RL experiments.

This skill automates the process of transforming high-level product specifications, requirements, and design documents into a detailed, actionable, and executable task plan. It follows a multi-stage workflow, including context loading, parallel research, and two rigorous review gates (Task Plan Review and Task-Graph Sanity Review). The output provides properly sized (1-3 hours), logically sequenced tasks, ensuring every requirement is covered and dependencies are explicitly managed, dramatically improving project planning accuracy.

This skill provides an interactive, comprehensive quality review for technical design documents. It systematically validates a feature's readiness for implementation by assessing critical issues, identifying strengths, and providing a clear GO/NO-GO decision with actionable next steps. Ideal for ensuring architectural soundness and catching risks early in the development lifecycle.

This comprehensive guide provides minimal working examples for key Adobe APIs: Firefly AI image generation, PDF content extraction, and Photoshop background removal. It serves as a practical quickstart for developers starting new Adobe integrations, testing API connectivity, or learning basic API calling patterns using TypeScript and Node.js.

This comprehensive tutorial guides developers through setting up and utilizing Algolia for advanced search functionality. It covers fundamental operations such as indexing records using `saveObjects`, performing targeted searches, and configuring index settings (like custom ranking and faceting). The guide also demonstrates advanced use cases, including multi-index federation and browsing all records, making it ideal for developers building or optimizing a search backend.