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Found 75 Skills
View social media analytics and insights. Use when the user wants to check post performance, engagement metrics, best posting times, follower stats, content decay, posting frequency, or any analytics data from their connected platforms.
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
Guide for modernizing legacy Python 2 scientific computing code to Python 3 with modern libraries. This skill should be used when migrating scientific scripts involving data processing, numerical computation, or analysis from Python 2 to Python 3, or when updating deprecated scientific computing patterns to modern equivalents (pandas, numpy, pathlib).
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.
Use when tasks involve creating, editing, analyzing, or formatting spreadsheets (`.xlsx`, `.csv`, `.tsv`) using Python (`openpyxl`, `pandas`), especially when formulas, references, and formatting need to be preserved and verified. Originally from OpenAI's curated skills catalog.
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.