Total 50,473 skills, Data Processing has 2559 skills
Showing 12 of 2559 skills
Optimize provider selection, routing, and credit usage across 150+ enrichment sources for company/contact intelligence.
Side-by-side stat comparisons with context. Adjust for era, pace of play, league differences. Advanced metrics explained in plain English.
R programming for data analysis, visualization, and statistical workflows. Use when working with R scripts (.R), Quarto documents (.qmd), RMarkdown (.Rmd), or R projects. Covers tidyverse workflows, ggplot2 visualizations, statistical analysis, epidemiological methods, and reproducible research practices.
Use when "improving image quality", "enhancing screenshots", "upscaling images", "sharpening photos", or asking about "image optimization", "screenshot quality", "resolution improvement"
Synthesize multiple media analyses into cross-source patterns and insights. Use when you need to cross-reference analyses, find patterns across sources, or perform meta-analysis of media content.
Production ETL patterns orchestrator. Routes to core reliability patterns and incremental load strategies.
Quantifies market breadth health using TraderMonty's public CSV data. Generates a 0-100 composite score across 6 components (100 = healthy). No API key required. Use when user asks about market breadth, participation rate, advance-decline health, whether the rally is broad-based, or general market health assessment.
Python DAG workflow orchestration using Apache Airflow for data pipelines, ETL processes, and scheduled task automation
Use BEFORE and AFTER running trading engine simulations. Helps with: (1) SETUP - choosing configs, selecting segments via segment collections, batch sizing (recommend 2,000-3,000 runs); (2) EXECUTION - running batch simulations with --collection; (3) ANALYSIS - comprehensive diagnostics after runs. Triggers on: 'run simulations', 'test configs', 'batch simulation', 'analyze sim results', 'which configs to test', 'how many segments', 'simulation setup'.
Detect and analyze adverse drug event signals using FDA FAERS data, drug labels, disproportionality analysis (PRR, ROR, IC), and biomedical evidence. Generates quantitative safety signal scores (0-100) with evidence grading. Use for post-market surveillance, pharmacovigilance, drug safety assessment, adverse event investigation, and regulatory decision support.
Data Quality Checker - Auto-activating skill for Data Pipelines. Triggers on: data quality checker, data quality checker Part of the Data Pipelines skill category.
Migrates JSON Schemas between draft versions for use with z-schema. Use when the user wants to upgrade schemas from draft-04 to draft-2020-12, convert between draft formats, update deprecated keywords, replace id with $id, convert definitions to $defs, migrate items to prefixItems, replace dependencies with dependentRequired or dependentSchemas, adopt unevaluatedProperties or unevaluatedItems, or adapt schemas to newer JSON Schema features.