Total 30,483 skills, Data Processing has 1462 skills
Showing 12 of 1462 skills
Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.
DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.
Parse FCS (Flow Cytometry Standard) files v2.0-3.1. Extract events as NumPy arrays, read metadata/channels, convert to CSV/DataFrame, for flow cytometry data preprocessing.
Universal AI-powered web scraper for any platform. Scrape data from Instagram, Facebook, TikTok, YouTube, Google Maps, Google Search, Google Trends, Booking.com, and TripAdvisor. Use for lead generation, brand monitoring, competitor analysis, influencer discovery, trend research, content analytics, audience analysis, or any data extraction task.
Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.
Scrape e-commerce data for pricing intelligence, customer reviews, and seller discovery across Amazon, Walmart, eBay, IKEA, and 50+ marketplaces. Use when user asks to monitor prices, track competitors, analyze reviews, research products, or find sellers.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
US stock market sentiment monitoring and position recommendation system. Evaluates market sentiment by tracking 5 core indicators (NAAIM Exposure Index, Institutional Equity Allocation, Retail Net Buying, S&P 500 Forward P/E Ratio, Hedge Fund Leverage) and outputs sentiment ratings and position recommendations. This skill should be used when the user mentions topics such as US stock sentiment, market overheating, greed/fear indicators, NAAIM, institutional positioning, retail sentiment, P/E valuation bubbles, hedge fund leverage, whether to reduce positions, market risk assessment, position management advice, market top/bottom signals, etc. Even if the user simply asks "Is the US stock market risky right now?" or "Should I reduce my positions?", this skill should be triggered to provide a structured analytical framework.
Macro liquidity monitoring and risk early-warning system. By tracking 4 core indicators (Fed Net Liquidity, SOFR Overnight Financing Rate, MOVE Treasury Volatility Index, Yen Carry Trade Signals), it provides real-time assessment of liquidity conditions in the global financial system, outputting liquidity ratings and risk response recommendations. When users mention topics such as liquidity, Fed balance sheet reduction (QT), TGA account, reverse repo ON RRP, SOFR rate, MOVE index, Treasury volatility, yen carry trade, USDJPY and interest rate differentials, impact of QT on markets, whether money is tight, liquidity inflection points, tightening financial conditions, etc., this skill should be used. Even if users ask broadly "how is liquidity right now" or "is the Fed draining or injecting liquidity," this skill should be triggered to provide a structured analytical framework.