Total 54,367 skills, Data Processing has 2782 skills
Showing 12 of 2782 skills
Analyze the bond futures basis by pricing futures, identifying the cheapest-to-deliver, and comparing with yield curves to assess delivery option value and basis trading opportunities. Use when analyzing bond futures, computing the basis, identifying CTD bonds, calculating implied repo rates, or evaluating basis trades.
K-line candlestick pattern recognition for stocks listed in HK / US / A-share / Singapore via Longbridge Securities. Identifies 15 classic patterns (hammer, hanging man, engulfing, doji, morning/evening star, three white soldiers/black crows, shooting star, etc.) from OHLCV data and generates a composite bullish/bearish/neutral signal. Triggers: "K线形态", "蜡烛图形态", "锤子线", "吞没形态", "十字星", "早晨之星", "暮色之星", "三白兵", "三黑鸦", "吊颈线", "射击之星", "K線形態", "蠟燭圖形態", "錘子線", "吞沒形態", "早晨之星", "暮色之星", "candlestick pattern", "hammer", "engulfing", "doji", "morning star", "evening star", "three white soldiers", "shooting star", "K-line pattern".
Market microstructure analysis via Longbridge Securities — bid-ask spread, order-flow toxicity (large-order pressure), liquidity depth, price impact, and institutional order direction. Covers A-share call-auction analysis and HK block-trade mechanics. Triggers: "盘口分析", "微观结构", "订单流", "大单分析", "买卖价差", "逐笔分析", "买卖盘深度", "挂单墙", "主力动向", "集合竞价", "盤口分析", "微觀結構", "訂單流", "大單分析", "買賣價差", "逐筆分析", "買賣盤深度", "掛單牆", "主力動向", "market microstructure", "order flow", "bid-ask spread", "depth analysis", "large order", "order book imbalance", "price impact", "auction analysis", "institutional order flow".
Fundamental factor stock screening — filter value or growth stocks using PE, PB, ROE, revenue growth, net-profit growth, and dividend yield across A-share, HK, and US markets. Outputs a candidate table ranked by composite factor score. Triggers: "基本面筛选", "因子选股", "价值选股", "成长选股", "低PE选股", "高ROE", "股息筛选", "PE筛选", "PB筛选", "多条件选股", "基本面因子", "量化选股", "基本面篩選", "因子選股", "價值選股", "成長選股", "低PE選股", "股息篩選", "factor screening", "value screen", "growth screen", "low PE filter", "high ROE screen", "dividend screen", "fundamental factor", "multi-factor stock screen".
Octoparse integration. Manage data, records, and automate workflows. Use when the user wants to interact with Octoparse data.
Analyze year-over-year growth in income statement items and financial metrics using Octagon MCP. Use when retrieving YoY Revenue Growth, Cost of Revenue Growth, Gross Profit Growth, Operating Income Growth, Net Income Growth, or comparing financial performance across fiscal periods for any public company.
Given event_candidates YAML (output from discover-event-surfaces), generates a concrete instrumentation plan for priority-3 (critical) events. Acts as a Software Architect: discovers existing analytics patterns in the codebase, reads the hinted files to determine what variables are in scope, designs minimal chart-useful properties, and identifies the exact insertion point for each tracking call. Outputs a structured JSON trackingPlan. Use this as step 3 of the analytics instrumentation workflow, after discover-event-surfaces. Trigger whenever a user has event_candidates and wants to generate tracking code, asks "instrument these events", "generate tracking plan", "add analytics for these events", "where should I put the tracking calls", or any request to turn event candidates into concrete implementation guidance.
Guides an end-to-end data-warehouse migration to Amazon Redshift — discovery, schema/SQL/stored-procedure/macro/script conversion, data migration, validation, performance comparison, and reporting. Source-routed via `references/<source>/`; Teradata (Vantage) is the supported source; additional sources are added as their own `references/<source>/` sets. Text-only knowledge (no executable code) — the AI generates all execution at runtime. Applies when a user wants to migrate Teradata to Amazon Redshift, convert Teradata DDL/SQL/stored procedures/macros/BTEQ to Redshift/RSQL, or assess Teradata-to-Redshift migration complexity. Applies only to migrations targeting Amazon Redshift; migrations to other platforms (Snowflake, BigQuery, Databricks, etc.) are out of scope regardless of source. Does not cover general Redshift administration, performance tuning, or troubleshooting of existing Redshift clusters (no migration involved), or sources not listed under references/.
LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.
End-to-end retail ETL pipeline using Medallion Architecture (Bronze/Silver/Gold) with TSQL, PySpark, and Airflow for inventory, sales, and supplier data processing
Use when working with Lightdash YAML files, dbt models with Lightdash metadata, the lightdash CLI (deploy, upload, download, preview, lint, warehouse-catalog, sql, set-warehouse), or managing charts, dashboards, spaces and access, AI agents, scheduled content, users, groups, custom roles, metrics, and dimensions as code
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.