Total 54,172 skills, Data Processing has 2771 skills
Showing 12 of 2771 skills
Unity Catalog metric views: define, create, query, and manage governed business metrics in YAML. Use when building standardized KPIs, revenue metrics, order analytics, or any reusable business metrics that need consistent definitions across teams and tools.
Use when "Polars", "fast dataframe", "lazy evaluation", "Arrow backend", or asking about "pandas alternative", "parallel dataframe", "large CSV processing", "ETL pipeline", "expression API"
Apply production-ready Databricks SDK patterns for Python and REST API. Use when implementing Databricks integrations, refactoring SDK usage, or establishing team coding standards for Databricks. Trigger with phrases like "databricks SDK patterns", "databricks best practices", "databricks code patterns", "idiomatic databricks".
The practice of collecting, analyzing, and acting on data to drive product decisions. Great analytics isn't about dashboards—it's about insights that lead to action. Every metric should answer a question that changes behavior. This skill covers event tracking, metrics design, dashboards, user behavior analysis, and data-driven decision making. The best analytics teams measure what matters, not what's easy to measure. Use when "analytics, metrics, tracking, dashboard, funnel, cohort, retention, events, KPI, measure, data, insights, conversion, engagement, analytics, metrics, data, dashboards, tracking, funnels, cohorts, KPIs, insights" mentioned.
Process Excel files, supporting reading, analysis, statistics and export of xlsx data
Guide Claude through omicverse's single-cell clustering workflow, covering preprocessing, QC, multimethod clustering, topic modeling, cNMF, and cross-batch integration as demonstrated in t_cluster.ipynb and t_single_batch.ipynb.
Implement, validate, and test JSONLogic rules for portable business logic. Use when working with JSONLogic syntax, creating rules for conditional logic, validating rule structures, testing rules against data, or converting business requirements to JSONLogic. Triggers on requests to "write jsonlogic", "validate jsonlogic", "create a rule", "business logic as JSON", "conditional logic", or any mention of JSONLogic rules.
Analyze whether ClickHouse indexes (PRIMARY KEY, ORDER BY, skipping indexes, projections) are being used effectively for actual query patterns. Use when investigating index effectiveness, ORDER BY key design, query-to-index alignment, or when queries scan more data than expected.
Neo4j Cypher Shell skill. Connect to instances, run Cypher queries, and retrieve full graph schemas via cypher-shell CLI.
Execute read-only SQL queries against PostgreSQL databases. Use when: (1) querying PostgreSQL data, (2) exploring schemas/tables, (3) running SELECT queries for analysis, (4) checking database contents. Supports multiple database connections with descriptions for auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.
Build interactive data applications and dashboards with pure Python - no frontend experience required
Integrate and analyze multiple omics datasets (transcriptomics, proteomics, epigenomics, genomics, metabolomics) for systems biology and precision medicine. Performs cross-omics correlation, multi-omics clustering (MOFA+, NMF), pathway-level integration, and sample matching. Coordinates ToolUniverse skills for expression data (RNA-seq), epigenomics (methylation, ChIP-seq), variants (SNVs, CNVs), protein interactions, and pathway enrichment. Use when analyzing multi-omics datasets, performing integrative analysis, discovering multi-omics biomarkers, studying disease mechanisms across molecular layers, or conducting systems biology research that requires coordinated analysis of transcriptome, genome, epigenome, proteome, and metabolome data.