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Found 86 Skills
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).
Analyze CSV files, generate summary statistics, and create visualizations using Python and pandas. Use when the user uploads, attaches, or references a CSV file, asks to summarize or analyze tabular data, requests insights from CSV data, or wants to understand data structure and quality.
Use when "Dask", "parallel computing", "distributed computing", "larger than memory", or asking about "parallel pandas", "parallel numpy", "out-of-core", "multi-file processing", "cluster computing", "lazy evaluation dataframe"
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.
Best practices for doing quick exploratory data analysis with minimal code and a Pandas .plot like API using HoloViews hvPlot.
Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
Executive-grade data analysis with pandas/polars and McKinsey-quality visualizations. Use when analyzing data, building dashboards, creating investor presentations, or calculating SaaS metrics.
Interact with Excel files (.xlsx, .xlsm, .xlsb, .xls, .ods) using the agent-xlsx CLI for data extraction, analysis, writing, formatting, visual capture, VBA analysis, and sheet management. Use when the user asks to: (1) Read, analyse, or search data in spreadsheets, (2) Write values or formulas to cells, (3) Inspect formatting, formulas, charts, or metadata, (4) Take screenshots or visual captures of sheets, (5) Export sheets to CSV/JSON/Markdown, (6) Manage sheets (create, rename, delete, copy, hide), (7) Analyse or execute VBA macros, (8) List/export embedded objects (charts, shapes, pictures), (9) Check for formula errors, or (10) Any task involving Excel file interaction. Prefer over openpyxl/pandas scripts — faster, structured JSON optimised for AI.
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, edit, audit, and extract Excel spreadsheets (.xlsx): generate reports/exports, apply formulas/formatting/charts/data validation, parse existing workbooks, and avoid spreadsheet risks (formula injection, broken links, hidden rows). Supports ExcelJS, openpyxl, pandas, XlsxWriter, and SheetJS.
An analytical in-process SQL database management system. Designed for fast analytical queries (OLAP). Highly interoperable with Python's data ecosystem (Pandas, NumPy, Arrow, Polars). Supports querying files (CSV, Parquet, JSON) directly without an ingestion step. Use for complex SQL queries on Pandas/Polars data, querying large Parquet/CSV files directly, joining data from different sources, analytical pipelines, local datasets too big for Excel, intermediate data storage and feature engineering for ML.