Total 50,368 skills, Data Processing has 2557 skills
Showing 12 of 2557 skills
Automatically generate Excel reports from data sources including CSV, databases, or Python data structures. Supports data analysis reports, business reports, data export, and template-based report generation using pandas and openpyxl. Activate when users mention Excel, spreadsheet, report generation, data export, or business reporting.
Statistics, probability, linear algebra, and mathematical foundations for data science
Guidance for extracting and processing data from ELF (Executable and Linkable Format) binary files. This skill should be used when tasks involve parsing ELF headers, reading program segments, extracting memory contents, or converting binary data to structured formats like JSON. Applicable to reverse engineering, binary analysis, and memory dump extraction tasks.
Expert SQL query writing, optimization, and database schema design with support for PostgreSQL, MySQL, SQLite, and SQL Server. Use when working with databases for: (1) Writing complex SQL queries with joins, subqueries, and window functions, (2) Optimizing slow queries and analyzing execution plans, (3) Designing database schemas with proper normalization, (4) Creating indexes and improving query performance, (5) Writing migrations and handling schema changes, (6) Debugging SQL errors and query issues
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.
Comprehensive personal finance management system for analyzing transaction data, generating insights, creating visualizations, and providing actionable financial recommendations. Use when users need to analyze spending patterns, track budgets, visualize financial data, extract transactions from PDFs, calculate savings rates, identify spending trends, generate financial reports, or receive personalized budget recommendations. Triggers include requests like "analyze my finances", "track my spending", "create a financial report", "extract transactions from PDF", "visualize my budget", "where is my money going", "financial insights", "spending breakdown", or any finance-related analysis tasks.
Publication-ready matplotlib figures for Nature/high-impact journals and academic papers. Covers bar charts, grouped bars, heatmaps, line/trend plots, forest plots, microscopy-style image panels, schematic + quantitative composites, radar plots, and multi-panel layouts with Nature-style typography (Arial/sans-serif), restrained color systems, and SVG/PDF export conventions. Use when creating scientific figures that must match Nature publication standards. Do NOT use for interactive dashboards (Plotly, Bokeh) or Illustrator/Figma-first infographic workflows.
MUST USE when designing ClickHouse architectures, selecting between ingestion or modeling patterns, or translating best practices into workload-specific system designs. Complements clickhouse-best-practices with decision frameworks and explicit provenance labels.
Use when normal web_fetch cannot read a page, when a site blocks basic fetching, or when the user needs YouTube content in AI-ready form. Guides fallback use of Firecrawl for single-page web scraping and SerpApi for YouTube search/video metadata/transcripts only.
Run a fully self-contained Xiaohongshu workflow with cookie auth and bundled JS signing assets. Use for note search, note text/image extraction, image download, user/profile data, comments, message center data, homefeed data, creator posted-note data, no-watermark URL conversion, and Excel/media export without depending on the original Spider_XHS repository.
Server-side quantitative indicator runner via Longbridge Securities — execute Pine Script v6 syntax subset against historical K-line data on Longbridge servers without a local Python environment. Supports built-in indicators (MACD, RSI, Bollinger Bands, EMA, SMA, etc.) and custom calculation logic; results returned as JSON. Triggers: "量化指标", "Pine Script", "指标计算", "MACD计算", "RSI计算", "服务端指标", "指标脚本", "量化脚本", "技术指标运行", "量化指標", "指標計算", "MACD計算", "RSI計算", "服務端指標", "指標腳本", "quant indicator", "Pine Script", "indicator calculation", "run indicator", "server-side quant", "MACD script", "RSI calculation", "technical indicator runner", "quant run".
국가데이터처가 운영하는 KOSIS(국가통계포털, kosis.kr) Open API로 한국 공식 통계표를 검색하고 메타데이터·데이터·대용량 자료를 조회한다. Use when the user asks for 한국 공식 통계 (인구, 가구, 물가, 고용 등) 수치 조회, not for analysis or visualization.