Skill UI

Real-time research lookup that auto-selects between Parallel Chat API and Perplexity sonar-pro-search to gather papers, facts, market data, and cited findings.

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 the STRING REST API to retrieve protein-protein interaction networks, map identifiers, visualize evidence-weighted edges, and run GO/KEGG/Pfam enrichment for systems biology analysis.

This skill provides zero-shot time series forecasting using Google's TimesFM foundation model. It forecasts any univariate time series (like sales, sensor data, or weather) without requiring custom model training. The model delivers point forecasts along with calibrated prediction intervals (quantiles), offering a robust and probabilistic view of future trends. It is ideal for rapid, reliable forecasting across diverse time-based datasets.

Direct REST API access to UniProt for searching proteins, fetching FASTA sequences, mapping identifiers, selecting annotations, and streaming large datasets within Python workflows.

Provides open REST access to the U.S. Treasury Fiscal Data API so analysts can pull national debt, spending, revenue, interest rates, exchange rates, auctions, and savings bond statistics without an API key; useful for financial dashboards, debt tracking, and research involving federal fiscal metrics.

Provides access to USPTO PatentSearch, PEDS, TSDR, and related APIs for patent and trademark searches, examination history, assignments, citations, and litigation data, with helper scripts for Python-based IP research and portfolio insights.

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.

A robust Python client wrapper for interacting with the Zotero Web API v3. This skill allows users to programmatically manage academic references, including retrieving, creating, updating, and deleting items, collections, tags, and attachments. Ideal for academic researchers building automated workflows, bulk exporting citations (e.g., BibTeX), or integrating Zotero data into larger research applications.

This guide provides a comprehensive walkthrough for developing and deploying Apify Actors. Actors are serverless, containerized applications designed for powerful tasks like web scraping, data processing, and complex automation. Learn the mandatory setup, template selection (Python, JS, TS), step-by-step development workflow, and crucial security best practices for building robust, production-ready data pipelines.

Apify Actorization is a process that transforms existing, standalone software or scripts (like CLI tools) into standardized, reusable serverless applications called Actors. Actors are packaged as Docker images, accepting structured JSON inputs and producing structured JSON outputs. This skill guides developers on migrating projects to the Apify platform, ensuring compatibility, and enabling scalable, event-driven data processing pipelines.

This comprehensive tool utilizes specialized Apify Actors to analyze audience demographics, engagement patterns, and behavior data across major social platforms, including Facebook, Instagram, YouTube, and TikTok. It provides structured data extraction and a summarized interpretation of key audience insights, making it invaluable for marketing, content strategy, and market research.