Skill UI

A powerful tool designed to monitor blockchain mempools, analyzing pending transactions, gas price fluctuations, detecting DEX swaps, and identifying potential MEV opportunities. It is essential for advanced crypto traders, MEV researchers, and protocol developers who need to optimize transaction timing and gas costs across multiple EVM chains (Ethereum, Polygon, Arbitrum, etc.).

A powerful tool designed to monitor blockchain mempools, analyzing pending transactions, gas price fluctuations, detecting DEX swaps, and identifying potential MEV opportunities. It is essential for advanced crypto traders, MEV researchers, and protocol developers who need to optimize transaction timing and gas costs across multiple EVM chains (Ethereum, Polygon, Arbitrum, etc.).

A comprehensive guide for systematic statistical analysis, designed for academic research. This skill assists in selecting the correct hypothesis tests (t-test, ANOVA, regression, chi-square), checking assumptions (normality, homogeneity), performing advanced analyses (Bayesian methods, power analysis), calculating effect sizes, and generating full APA-formatted reports for publication.

A comprehensive toolkit designed for advanced data scraping and automation workflows on X/Twitter. Capabilities include deep tweet search, user profile analysis, bulk follower/media export, automated posting (replies, DMs), real-time account monitoring via webhooks, and integration with multiple AI agent platforms (Hermes, OpenClaw). Ideal for market research, social listening, and content automation.

This is an advanced, structured workflow designed to conduct deep, multi-source investigations. It synthesizes current public evidence with user-provided context to generate highly structured reports. The workflow explicitly differentiates between verifiable facts, user-supplied evidence, logical inferences, and actionable recommendations, making it ideal for complex comparative analysis or continuous monitoring tasks.

This advanced research technique is designed for deep dives into football topics. Instead of relying solely on the original query, it mandates tracking every discovered entity—such as player names, club names, or coaches—from search results. By treating these entities as new leads, the user must formulate follow-up searches using the discovered names and remaining constraints, ensuring nothing relevant is overlooked and building a comprehensive knowledge chain.

DeepChem is a comprehensive Python library designed for applying machine learning to complex fields like chemistry, materials science, and biology. It enables users to perform molecular property prediction (e.g., ADMET, toxicity), train models using specialized graph neural networks (GNNs), and process diverse molecular formats (SMILES, SDF, FASTA). It provides extensive featurization options, making it foundational for drug discovery and computational chemistry research.

Geniml is a comprehensive Python package designed for applying machine learning models to genomic interval data, typically stored in BED files. It provides several unsupervised methods—including Region2Vec and BEDspace—to learn robust embeddings of genomic regions, single cells (scATAC-seq), and associated metadata. Use cases include dimensionality reduction, structural variation analysis, cell-type annotation, and cross-modal querying in genomics research.

Gtars is a high-performance toolkit written in Rust with Python bindings, designed for comprehensive analysis of genomic interval data. It provides specialized modules for overlap detection, generating coverage tracks (WIG/BigWig), tokenization for Machine Learning models, and detailed fragment analysis in single-cell genomics. Use it for robust bioinformatics pipelines and computational genomics research.

NeuroKit2 is a robust, research-grade toolkit designed for building and auditing reproducible workflows for physiological time-series data. It facilitates preprocessing, event/interval analysis, multimodal signal alignment, and feature extraction across various biosignals (e.g., ECG, EEG, EDA). This skill is strictly for educational and research purposes, providing method-aware tools to ensure data integrity and reproducibility, but must not be used for medical diagnosis or device validation.

An end-to-end toolkit designed for analyzing high-density Neuropixels electrophysiology recordings. It implements best practices using SpikeInterface, covering the entire workflow: data loading (SpikeGLX, NWB), preprocessing, motion/drift correction, advanced spike sorting (Kilosort4), quality metric computation, and unit curation (including AI-assisted review). Ideal for neuroscience research.

PathML is a dedicated computational pathology software designed for local, research-only workflows. It enables users to load and tile whole slide images, build comprehensive preprocessing and quality control pipelines, quantify multiplex data, construct spatial graphs, and plan bounded model inference. It is crucial for researchers handling highly sensitive medical images while maintaining strict data privacy and local processing standards.