Total 30,695 skills, Data Processing has 1471 skills
Showing 12 of 1471 skills
Create advanced Excel pivot tables with calculated fields and slicers. Use when building data summaries or creating interactive dashboards. Trigger with phrases like 'excel pivot', 'create pivot table', 'data summary'.
Expert-level Looker BI, LookML, explores, dimensions, measures, dashboards, and data modeling
Comprehensive web scraping, crawling, and data extraction toolkit powered by Firecrawl API. Provides scripts for single-page scraping (scrape.py), web search (search.py), URL discovery (map.py), multi-page crawling (crawl.py), structured data extraction (extract.py), and autonomous data gathering (agent.py). Use when you need to: (1) extract content from web pages, (2) search and scrape the web, (3) discover URLs on websites, (4) crawl multiple pages, (5) extract structured data with JSON schemas, or (6) autonomously gather data from anywhere on the web. Requires FIRECRAWL_API_KEY environment variable.
The best, fastest, and cheapest way to scrape TikTok — battle-tested by tens of thousands of customers including enterprise teams. Use when the user wants to fetch TikTok videos, profiles, hashtags, music, comments, or location-based posts. Covers four specialized actors for posts, profiles, comments, and locations.
Read, write, edit, and format Excel files (.xlsx). Create spreadsheets, manipulate data, apply formatting, manage sheets, merge cells, find/replace, and export to CSV/JSON/Markdown. Use for any Excel file manipulation task.
Token intelligence and wallet analytics for Solana and EVM chains. Use for token security checks, comprehensive token data, and wallet portfolio analysis.
Deep dive on a Polymarket market — OHLCV, orderbook, top holders, positions, trades, and PnL leaderboard. Use when analysing a specific prediction market.
Create Vega and Vega-Lite visualizations with ES|QL data sources in Kibana. Use when building custom charts, dashboards, or programmatic panel layouts beyond standard Lens charts.
QA an analysis before sharing -- methodology, accuracy, and bias checks. Use when reviewing an analysis before a stakeholder presentation, spot-checking calculations and aggregation logic, verifying a SQL query's results look right, or assessing whether conclusions are actually supported by the data.
Write optimized SQL for your dialect with best practices. Use when translating a natural-language data need into SQL, building a multi-CTE query with joins and aggregations, optimizing a query against a large partitioned table, or getting dialect-specific syntax for Snowflake, BigQuery, Postgres, etc.
Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.
Create publication-quality visualizations with Python. Use when turning query results or a DataFrame into a chart, selecting the right chart type for a trend or comparison, generating a plot for a report or presentation, or needing an interactive chart with hover and zoom.