Loading...
Loading...
Found 217 Skills
Retrieve analysts' price target summary for any stock using Octagon MCP. Use when evaluating analyst sentiment, upside/downside potential, consensus expectations, and tracking target trends over time.
Analyze Management Discussion and Analysis (MD&A) sections from SEC filings using Octagon MCP. Use when extracting strategic initiatives, financial performance commentary, macroeconomic challenges, and forward-looking statements from 10-K and 10-Q filings.
Open-source Python package for exploring, visualizing, and analyzing human neurophysiological data including EEG, MEG, sEEG, and ECoG.
Expert in Python development with best practices across web, data science, and automation
Retrieve year-over-year growth in income statement items including Revenue, Gross Profit, Operating Income, Net Income, and EPS Diluted. Use when analyzing company financial growth trends, comparing fiscal year performance, or identifying profitability inflection points.
Analyze cost reduction initiatives and operational efficiency measures from earnings transcripts, including headcount actions, facility consolidation, and productivity improvements.
Retrieve detailed revenue breakdown by geographic segment for public companies. Use when analyzing regional exposure, geographic concentration, international expansion, or currency risk assessment.
Retrieve historical market capitalization data for any stock using Octagon MCP. Use when tracking market cap changes over time, analyzing valuation trends, identifying peak and trough valuations, and comparing historical size classifications.
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。
A Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Great for exploring relationships between variables and visualizing distributions. Use for statistical data visualization, exploratory data analysis (EDA), relationship plots, distribution plots, categorical comparisons, regression visualization, heatmaps, cluster maps, and creating publication-quality statistical graphics from Pandas DataFrames.
Translate A/B test lift percentages into annualized dollar projections. Shows conservative and optimistic revenue impact, break-even analysis, and opportunity cost of waiting.
Extract and analyze data from invoices, receipts, bank statements, and financial documents. Categorize expenses, track recurring charges, and generate expense reports. Use when user provides financial PDFs or images.