Skill UI

A comprehensive skill for analyzing Non-Fungible Tokens (NFTs). It fetches collection metadata from APIs (like OpenSea), parses trait attributes, and calculates advanced rarity scores using multiple algorithms (e.g., rarity_score, information entropy). Use this tool to rank tokens by uniqueness, compare specific NFTs, or analyze the distribution of traits within an entire collection, outputting results in JSON or CSV format. Ideal for crypto research and market due diligence.

Configure automated Continuous Integration and Continuous Deployment (CI/CD) pipelines for Juicebox AI data analysis integrations. This setup utilizes GitHub Actions to run comprehensive unit tests (with mocked datasets) and integration tests (connecting to live APIs) on every PR and merge to main. It ensures that data upload logic, analysis execution, and result parsing workflows are robust and reliable before deployment.

Datamol wraps RDKit with Pythonic APIs tailored for drug discovery, offering SMILES/SELFIES/InChI parsing, standardization, descriptor and fingerprint calculation, clustering, 3D conformer workflows, and parallel/cloud-aware batch I/O while returning native rdkit.Chem.Mol objects.

Query the NHGRI-EBI GWAS Catalog for SNP-trait associations, fetch study metadata, variant and trait details, and download summary statistics via the web or REST APIs to support genetic epidemiology and polygenic risk modeling.

Probabilistic deep-learning framework built on PyTorch for single-cell omics; handles batch correction, integration, multimodal and spatial data, differential expression, and transfer learning with unified APIs.

Query UCSF’s ZINC22 repository of 230M+ purchasable compounds via the web UI or CartBlanche APIs to fetch IDs/SMILES, run similarity, supplier, or random sampling, and assemble docking-ready libraries for virtual screening.

Xquik unlocks Twitter data via REST APIs, MCP tools, and webhooks, covering tweet/user lookups, follower extraction, giveaway draws, trending topics, and accurate account monitoring for real-time automation.

A comprehensive tool designed to search, analyze, and summarize academic research papers from multiple sources. It integrates data from local PDF libraries, Zotero, Obsidian, and major academic web APIs (e.g., Semantic Scholar, DeepXiv). Use this skill when conducting literature reviews, needing related work suggestions, or requiring a deep understanding of a specific research topic.

Datamol wraps RDKit with Pythonic APIs tailored for drug discovery, offering SMILES/SELFIES/InChI parsing, standardization, descriptor and fingerprint calculation, clustering, 3D conformer workflows, and parallel/cloud-aware batch I/O while returning native rdkit.Chem.Mol objects.

RDKit is a comprehensive cheminformatics library providing powerful Python APIs for molecular analysis and manipulation. It supports reading/writing diverse molecular formats (SMILES, SDF, InChI), calculating physicochemical descriptors (MW, LogP, TPSA), performing substructure searching, and analyzing stereochemistry. This tool is essential for drug discovery, computational chemistry, and advanced cheminformatics research workflows.

Probabilistic deep-learning framework built on PyTorch for single-cell omics; handles batch correction, integration, multimodal and spatial data, differential expression, and transfer learning with unified APIs.

Access and query 10 major academic paper databases (including PubMed, arXiv, OpenAlex, Crossref, etc.) via REST APIs. This tool facilitates comprehensive scholarly literature searches, allowing users to find abstracts, check citation details (DOI/PMID), track open access status, retrieve preprints, and analyze citation graphs across diverse fields like biomedicine and physics.