Skill UI

This skill provides robust strategies and Python implementations for dealing with API rate limiting (HTTP 429 errors) when interacting with services like Vast.ai. It includes automatic retry logic, exponential backoff algorithms, and optimized techniques for batch operations and polling, ensuring stable and efficient API throughput.

A collection of production-ready Python patterns for interacting with the Vast.ai cloud API. This includes typed search query builders, context-managed instance lifecycle (guaranteeing auto-cleanup), offer scoring algorithms (cost, reliability, performance), robust retry mechanisms with backoff, and secure SSH command execution. Essential for building reliable, complex cloud workflow applications.

This skill provides AI-assisted performance profiling within the Windsurf IDE. It analyzes collected profiling data to pinpoint performance bottlenecks, suggest specific optimizations, and predict the impact of code changes. Use it when optimizing application speed, refining complex algorithms, or general performance tuning in a coding environment.

Arboreto infers gene regulatory networks from bulk or single-cell RNA-seq data using scalable GRNBoost2 and GENIE3 algorithms, letting researchers filter transcription-factor targets, run distributed Dask jobs, and export ranked links for downstream analysis.

Comprehensive Python toolkit for building, analyzing, and visualizing complex networks; supports algorithms, I/O, generation, and community detection across social, biological, transportation, and citation data.

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.

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.

SciPy provides open-source algorithms and utilities for scientific computing, including integration, optimization, statistics, and signal processing, enabling researchers and engineers to build reliable numerical applications.

A comprehensive content audit tool that assesses content quality, E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness), and adherence to modern search engine guidelines. It checks structure, readability metrics, AI readiness, and source citation practices to ensure content is helpful, unique, and optimized for Google's core algorithms and AI search interfaces.

A comprehensive guide for analyzing PCAP files captured during malware execution. It demonstrates advanced techniques using Wireshark, Zeek, Suricata, and Python (scapy) to identify Command and Control (C2) communications, data exfiltration channels, Domain Generation Algorithms (DGA), and periodic beaconing patterns. Essential for malware reverse engineering and network threat detection.

This skill analyzes the cryptographic algorithms, key management systems, and file encryption routines employed by various ransomware families. It covers hybrid schemes like AES-RSA and modern ciphers such as ChaCha20. Use this when assessing data recovery feasibility, reverse engineering ransomware binaries, identifying implementation weaknesses (e.g., IV reuse, key derivation flaws), or developing custom decryptor tools.