Total 50,396 skills, Data Processing has 2557 skills
Showing 12 of 2557 skills
Use when asked to convert between KML and GeoJSON formats, or convert geo data for mapping applications.
Should be used when doing clickhouse analysis and diagnostics review before any altinity-expert-clickhouse skill to test clickhouse connection and set general rules
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.
Get real-time market quotes and technical analysis data for A-shares, funds, and Hong Kong stocks
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.
Expert in automating Excel workflows using Node.js (ExcelJS, SheetJS) and Python (pandas, openpyxl).
Best practices for Pandas data manipulation, analysis, and DataFrame operations in Python
Comprehensive guide for FinLab quantitative trading package for Taiwan stock market (台股). Use when working with trading strategies, backtesting, Taiwan stock data, FinLabDataFrame, factor analysis, stock selection, or when the user mentions FinLab, trading, 回測, 策略, 台股, quant trading, or stock market analysis. Includes data access, strategy development, backtesting workflows, and best practices.
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.
Pre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.