Total 50,474 skills, Data Processing has 2559 skills
Showing 12 of 2559 skills
Use when writing or reading GenVarLoader (gvl) datasets — preparing VCF/PGEN/SVAR variant sources with bcftools/plink2, calling gvl.write, configuring gvl.Dataset for haplotype/reference/annotated/variants output modes, attaching BigWig or Table tracks, setting up spliced haplotypes from a GTF, choosing track insertion-fill strategies for indels, or filtering variants by allele frequency.
Systematic stock screening and investment idea sourcing. Combines quantitative screens, thematic research, and pattern recognition to surface new long and short ideas. Use when looking for new ideas, running screens, or conducting thematic sweeps. Triggers on "idea generation", "stock screen", "find ideas", "what looks interesting", "screen for", "new ideas", or "pitch me something".
Analyze portfolio allocation drift and generate rebalancing trade recommendations across accounts. Considers tax implications, transaction costs, and wash sale rules. Triggers on "rebalance", "portfolio drift", "allocation check", "rebalancing trades", or "my portfolio is out of balance".
Firecrawl v2.5 API for web scraping/crawling to LLM-ready markdown. Use for site extraction, dynamic content, or encountering JavaScript rendering, bot detection, content loading errors.
Build ETL pipelines and analytics dashboards for Harvard Art Museums API data using Python, SQL, and Streamlit
End-to-end data engineering and analytics application using Harvard Art Museums API with ETL pipelines, SQL analytics, and Streamlit visualization
Builds trade area and catchment analysis workflows in CARTO. Triggers when the user mentions trade area, catchment area, isochrone, site selection, where to open, best location, billboard, OOH, audience targeting, drive time, walk time, coverage area, commercial hotspot, site scoring, location ranking, or wants to generate isochrones, score candidate locations, or identify the best sites for retail, advertising, or services.
Quickly judge the current valuation level, historical quantile, relative position among peers, and revaluation conditions of individual stocks. Suitable for scenarios such as initially judging "whether it's expensive", screening valuation positions, discussing odds and expectation requirements.
Translate TradingView PineScript strategies into vectorized Python strategies suitable for Optuna optimization and walk-forward analysis.
Act as a Renaissance Tech-level quantitative systems engineer. Build unified feature engines instead of isolated strategies, rigorously test predictive variables, and assemble scoring models.
Obtain securities and financial information such as JoinQuant A-share market quotes, historical K-lines, financial data, and indicator data; Use this when users mention JoinQuant, jqdata, jqdatasdk and need to obtain A-share data
End-to-end ETL pipeline for Harvard Art Museums API with SQL analytics and Streamlit visualization