Total 55,449 skills, Data Processing has 2834 skills
Showing 12 of 2834 skills
Build and deploy Streamlit apps natively in Snowflake. Covers snowflake.yml scaffolding, Snowpark sessions, multi-page structure, Marketplace publishing as Native Apps, and caller's rights connections (v1.53.0+). Use when building data apps on Snowflake, deploying SiS, fixing package channel errors, authentication issues, cache key bugs, or path resolution errors.
Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.
SQLite file format, B-trees, pages, cells, overflow, freelist that is used in tursodb
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
Query CZ CELLxGENE Census (61M+ cells). Filter by cell type/tissue/disease, retrieve expression data, integrate with scanpy/PyTorch, for population-scale single-cell analysis.
Data analysis and statistical computation. Use when user needs "数据分析/统计/计算指标/数据洞察". Supports general analysis, financial data (stocks, returns), business data (sales, users), and scientific research. Uses pandas/numpy/scikit-learn for processing. Automatically activates data-base for data acquisition.
Create interactive maps with markers, heatmaps, routes, and choropleth layers. Use when visualizing geographic data, plotting locations, or creating map-based reports.
Expert in web scraping and data extraction with Python tools
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
Analyze Dividend Aristocrats (25+ years of consecutive dividend increases) for income reliability and total return. Use when the user asks to evaluate dividend aristocrats, calculate dividend reinvestment returns, assess dividend sustainability, compare income stocks, build a dividend growth portfolio, analyze payout ratios and free cash flow coverage, or rank stocks by dividend reliability and long-term total return.
Identify non-obvious signals, hidden patterns, and clever correlations in datasets using investigative data analysis techniques. Use when analyzing social media exports, user data, behavioral datasets, or any structured data where deeper insights are desired. Pairs with personality-profiler for enhanced signal extraction. Triggers on requests like "what patterns do you see", "find hidden signals", "correlate these datasets", "what am I missing in this data", "analyze across datasets", "find non-obvious insights", or when users want to go beyond surface-level analysis. Also use proactively when you notice interesting anomalies or correlations during any data analysis task.
Creates and maintains dlt (data load tool) pipelines from APIs, databases, and other sources. Use when the user wants to build or debug pipelines; use verified sources (e.g. Salesforce, GitHub, Stripe) or declarative REST API or custom Python; configure destinations (e.g. DuckDB, BigQuery, Snowflake); implement incremental loading; or edit .dlt config and secrets. Use when the user mentions data ingestion, dlt pipeline, dlt init, rest_api_source, incremental load, or pipeline dashboard.