Total 53,748 skills, Data Processing has 2766 skills
Showing 12 of 2766 skills
Read data from two tabs in a Google Sheet to compare and identify differences.
通过ASIN获取亚马逊商品详细信息，包括标题、图片、五点描述、规格参数、A+页面、价格、评分评论、变体等。当用户提到亚马逊商品详情、ASIN查询、商品页面数据、Listing分析、五点描述提取、商品图片获取、变体查看、竞品Listing研究、价格查询、评论拆解、商品规格查询、Amazon product details, ASIN lookup, listing analysis, bullet points, variant info, product pricing, ratings and reviews, A+ content, product specifications, product images时触发此技能。即使用户未明确说"商品详情"，只要其需求涉及通过ASIN获取亚马逊商品页面的结构化数据，也应触发此技能。
Macro-economic and cross-asset analysis for crypto market context. Use this skill whenever the user asks about the broader economic environment and its effect on crypto or risk assets. Triggers include: macro outlook, interest rates, Fed policy, rate cut, rate hike, FOMC, yield curve, inverted yield curve, recession risk, inflation, CPI, PCE, jobs data, unemployment, GDP, 10-year yield, 2-year yield, spread, dollar strength, DXY, risk-on, risk-off, gold correlation, BTC vs S&P, cross-asset correlation, global markets, tech earnings impact on crypto, forex rates, euro, yen, China market, A-shares, economic calendar, macro environment.
Stereonet plots for structural geology using matplotlib. Create lower-hemisphere stereographic projections for orientation data. Use when Claude needs to: (1) Create stereonet plots for structural data, (2) Plot planes as great circles or poles, (3) Plot lineations with trend/plunge, (4) Generate density contours for orientations, (5) Calculate mean orientations and statistics, (6) Analyze fold axes with pi-diagrams, (7) Convert between strike/dip and trend/plunge formats.
Process data with custom algorithms
Forecast Generator - Auto-activating skill for Data Analytics. Triggers on: forecast generator, forecast generator Part of the Data Analytics skill category.
Generate reproducible analysis artifacts — SQL queries, Python visualizations, and summary tables — as you work through a BigQuery data analysis. Use when asked to conduct a deep dive, exploratory analysis, or investigation that goes beyond a simple data lookup.
OpenDuck — open-source distributed DuckDB with differential storage, hybrid dual execution, and transparent remote database attach
Use to interpret qualitative feedback, trends, and risks across community channels.
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery: SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
Generate an interactive options payoff curve chart with dynamic parameter controls. Use this skill whenever the user shares an options position screenshot, describes an options strategy, or asks to visualize how an options trade makes or loses money. Triggers include: any mention of butterfly, spread (vertical/calendar/diagonal/ratio), straddle, strangle, condor, covered call, protective put, iron condor, or any multi-leg options structure. Also triggers when a user pastes strike prices, premiums, expiry dates, or says things like "show me the payoff", "draw the P&L curve", "what does this trade look like", or uploads a screenshot from a broker (IBKR, TastyTrade, Robinhood, etc). Always use this skill even if the user only provides partial info — extract what you can and use defaults for the rest.