Total 54,373 skills, Data Processing has 2785 skills
Showing 12 of 2785 skills
This skill should be used when the user asks to "use NumPy", "write NumPy code", "optimize NumPy arrays", "vectorize with NumPy", or needs guidance on NumPy best practices, array operations, broadcasting, memory management, or scientific computing with Python.
Use this skill when building dbt models, designing semantic layers, defining metrics, creating self-serve analytics, or structuring a data warehouse for analyst consumption. Triggers on dbt project setup, model layering (staging, intermediate, marts), ref() and source() usage, YAML schema definitions, metrics definitions, semantic layer configuration, dimensional modeling, slowly changing dimensions, data testing, and any task requiring analytics engineering best practices.
Use this skill when building, auditing, or optimizing spreadsheet models in Excel or Google Sheets. Triggers on formula writing, pivot table creation, dashboard design, data validation, conditional formatting, macro/VBA scripting, Apps Script automation, financial modeling, what-if analysis, XLOOKUP/INDEX-MATCH lookups, array formulas, and workbook architecture. Covers advanced Excel and Google Sheets for analysts, finance professionals, and operations teams.
Time series visualization and diagnostic plotting utilities
Use for creating publication-quality charts and multi-panel analysis summaries. Triggers when tasks involve visualizing data, plotting results, creating charts, or producing visual reports from analysis output.
Full token research workflow using Messari x402 API. Fetches asset fundamentals, price history, sentiment signals, and news, then synthesizes a research brief via Messari AI. Total cost ~$1.00–$1.50 USDC per run.
Use when writing SQL queries, building analytics dashboards, tracking metrics, designing data pipelines, or analyzing user behavior and product usage
Query, browse, and analyze Spanish legislation stored as Markdown files with full Git history in the legalize-es repository.
Use when optimizing multi-factor systems with limited experimental budget, screening many variables to find the vital few, discovering interactions between parameters, mapping response surfaces for peak performance, validating robustness to noise factors, or when users mention factorial designs, A/B/n testing, parameter tuning, process optimization, or experimental efficiency.
Create and manage Infrahub transforms. Use when building data transformations, config generation, or any workflow that converts Infrahub data into a different format (JSON, text, CSV, device configs) using Python or Jinja2 templates.
Read any data file (CSV, JSON, Parquet, Avro, Excel, spatial, SQLite) or remote URL (S3, HTTPS). Use when user references a data file, asks "what's in this file", or wants to preview/profile a dataset. Not for source code.
Holistics integration. Manage data, records, and automate workflows. Use when the user wants to interact with Holistics data.