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Found 86 Skills
Compute technical indicators like RSI, MACD, Bollinger Bands, SMA, EMA for a stock. Use when user asks about technical analysis, indicators, RSI, MACD, moving averages, overbought/oversold, or chart analysis.
Create publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.
Parse, transform, and analyze CSV files with advanced data manipulation capabilities.
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。
Create and manipulate Microsoft Excel workbooks programmatically. Build spreadsheets with formulas, charts, conditional formatting, and pivot tables. Handle large datasets efficiently with streaming mode.
Handle spreadsheet operations (Excel/CSV) with high-fidelity modeling, financial analysis, and visual verification. Use for budget models, data dashboards, and complex formula-heavy sheets. Use proactively when zero formula errors and professional standards are required. Examples: - user: "Build an LBO model" -> create Excel with banking-standard formatting - user: "Analyze this data and create a dashboard" -> use openpyxl + artifact_tool - user: "Verify formulas in this spreadsheet" -> run recalc.py to check for errors
Handle messy CSVs with encoding detection, delimiter inference, and malformed row recovery.
Excel spreadsheet toolkit for creating, reading, and manipulating .xlsx files. Supports formulas, formatting, charts, and financial modeling with industry-standard conventions. Use for data analysis, financial models, reports, and spreadsheet automation.
Profile datasets to understand schema, quality, and characteristics. Use when analyzing data files (CSV, JSON, Parquet), discovering dataset properties, assessing data quality, or when user mentions data profiling, schema detection, data analysis, or quality metrics. Provides basic and intermediate profiling including distributions, uniqueness, and pattern detection.
QUERY LENGTH LIMIT EXCEEDED. MAX ALLOWED QUERY : 500 CHARS
Validate and audit CSV data for quality, consistency, and completeness. Use when you need to check CSV files for data issues, missing values, or format inconsistencies.
Use this skill when spreadsheet files are the primary input or output. This means the user wants to: open, read, edit, or repair existing .xlsx, .xlsm, .csv, or .tsv files (e.g., add columns, calculate formulas, format, create charts, clean messy data); create new spreadsheets from scratch or from other data sources; or convert between spreadsheet file formats. Trigger this especially when the user references a spreadsheet file by name or path—even casually (such as "the xlsx in my downloads")—and wants to process it or generate content from it. It's also used to clean or reorganize messy tabular data files (rows with incorrect formatting, misaligned headers, garbage data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do not trigger this when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.