Total 50,503 skills, Data Processing has 2560 skills
Showing 12 of 2560 skills
Comprehensive market analyst skill that orchestrates all Octagon stock performance and market data skills. Use when conducting stock analysis, creating market reports, evaluating valuations, comparing sectors, or performing technical and sentiment analysis.
Retrieve a snapshot of market sector performance using Octagon MCP. Use when analyzing sector-wide metrics including revenue, EBITDA, net income, market cap, and enterprise value for companies within a specific sector and exchange.
全面的电子表格创建、编辑与分析工具，支持公式、格式设置、数据分析和可视化。当需要处理电子表格（如 .xlsx、.xlsm、.csv、.tsv 等）时使用，包括：(1) 创建包含公式和格式的新电子表格，(2) 读取或分析数据，(3) 在保留公式的情况下修改现有电子表格，(4) 在电子表格中进行数据分析和可视化，或 (5) 重新计算公式。
Comprehensive guide for MDAnalysis - the Python library for analyzing molecular dynamics trajectories. Use for trajectory loading, RMSD/RMSF calculations, distance/angle/dihedral analysis, atom selections, hydrogen bonds, solvent accessible surface area, protein structure analysis, membrane analysis, and integration with Biopython. Essential for MD simulation analysis.
Complete survival analysis library in Python. Handles right-censored data, Kaplan-Meier curves, and Cox regression. Standard for clinical trial analysis and epidemiology.
This skill provides an advanced financial modeling suite with DCF analysis, sensitivity testing, Monte Carlo simulations, and scenario planning for investment decisions
Create and manipulate Microsoft Excel workbooks programmatically. Build spreadsheets with formulas, charts, conditional formatting, and pivot tables. Handle large datasets efficiently with streaming mode.
Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.
Migration monitoring, CDC, and observability infrastructure
Guide for creating GreptimeDB Pipeline, by which user can add a process layer to GreptimeDB between ingestion and storage, to transform data.
Systematic multi-factor stock screening using formal factor models to identify stocks with favorable factor exposures. Use when the user asks about factor investing, multi-factor screening, value/momentum/quality factor analysis, factor scoring, factor timing, smart beta strategies, quantitative stock screening, or systematic equity selection based on academic factors.
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.