Total 53,555 skills, Data Processing has 2763 skills
Showing 12 of 2763 skills
Normalize messy creator campaign metrics from multiple sources into a single clean table with standardized field names ready to merge into your master tracker. This skill should be used when cleaning up influencer metrics, standardizing campaign data from multiple platforms, normalizing creator performance numbers, merging metrics from Instagram and TikTok and YouTube into one sheet, formatting messy analytics exports, preparing campaign data for a master spreadsheet, converting raw platform stats into a consistent format, combining metrics from different reporting tools, deduplicating creator data from multiple sources, fixing inconsistent column names across exports, or cleaning up a metrics dump before reporting. For calculating engagement rates, see engagement-rate-calculator-benchmarker. For full campaign reports, see campaign-roi-calculator. For parsing a single Story screenshot, see story-metrics-screenshot-parser.
Retrieve Chinese financial market data (A-shares, Hong Kong stocks, US stocks, funds, futures, bonds). Supports over 220 Tushare Pro interfaces: stock quotes, financial statements, macroeconomic indicators. Use this when users request stock price data, financial analysis, index quotes, macro data such as GDP/CPI, etc.
MPSTATS Ozon 俄罗斯站按俄语类目路径下钻该类目全部商品。返回每个 SKU 的销量、销售额、价格、评分、库存、周转、损失销售额等完整指标，支持多维数值筛选、排序、货币换算。用于类目爆款挖掘、蓝海洞察、类目排名分析、品牌格局观察。当用户提到 Ozon 类目下钻、Ozon 类目商品、Ozon 蓝海挖掘、Ozon 品类爆款、Ozon 类目排名、Ozon 子类目结构、Ozon 赛道 SKU、MPSTATS category, Ozon category drill-down, Russian marketplace niche, Ozon niche mining, Ozon subcategory bestseller 时触发此技能。即使用户未明确说"MPSTATS"，只要意图是按 Ozon 类目路径查看该类目下所有商品的销量/价格/排名表现，也应触发此技能。
What DeFi positions does a wallet hold? Protocol-by-protocol breakdown of assets, debts, and rewards across chains.
Guide for Using RQData Data API. Used when you need to query RQData data interfaces and obtain financial data. Supports data queries for markets such as A-shares, Hong Kong stocks, futures, options, indices, funds, and convertible bonds, including HTTP API and Python API documentation.
Apply event study methodology to measure abnormal returns and cumulative abnormal returns (CAR) around corporate or market events. Use this skill when the user needs to quantify the market impact of announcements, design event and estimation windows, or when they ask 'did this event affect stock price', 'how do I calculate abnormal returns', or 'what is the market reaction to this announcement'.
Databricks integration. Manage Workspaces. Use when the user wants to interact with Databricks data.
End-to-end epidemiological data analysis — from research question to statistical report. Covers study design assessment, dataset discovery and download, data wrangling, confounder adjustment, regression modeling, sensitivity analysis, visualization, and biological interpretation. Integrates ToolUniverse tools for dataset discovery, literature search, and biological context with Python code execution for data analysis. Use whenever users ask to analyze health data, study disease risk factors, assess exposure-outcome relationships, or conduct observational epidemiology. Also use when users want to run regression on clinical/survey data, calculate odds ratios or hazard ratios from a dataset, adjust for confounders, or produce a Table 1. If the task involves downloading a health dataset and running statistical analysis on it, this is the right skill.
DTC Data Dashboard & Health Check Engine — Full-link data analysis, KPI tracking, industry benchmarking, data health assessment, market trend monitoring. Use when user mentions: data health check, data audit, KPI, dashboard, metrics tracking, metrics, baseline, benchmark, data analysis, revenue report, channel data, advertising data, ROAS tracking.
Read data from two tabs in a Google Sheet to compare and identify differences.
Transform raw data from CSVs, Google Sheets, or databases into executive-ready reports with visualizations, key metrics, trend analysis, and actionable recommendations. Creates data-driven narratives for leadership. Use when users need to turn spreadsheets into executive summaries or board reports.
Write SQL, TypeScript, and dynamic table transforms for Goldsky Turbo pipelines. Use this skill for: decoding EVM event logs with _gs_log_decode (requires ABI) or transaction inputs with _gs_tx_decode, filtering and casting blockchain data in SQL, combining multiple decoded event types into one table with UNION ALL, writing TypeScript/WASM transforms using the invoke(data) function signature, setting up dynamic lookup tables to filter transfers by a wallet list you update at runtime (dynamic_table_check), chaining SQL and TypeScript steps together, or debugging null values in decoded fields. For full pipeline YAML structure, use /turbo-pipelines instead. For building an entire pipeline end-to-end, use /turbo-builder instead.