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Found 63 Skills
Pydantic models and validation. Use when: (1) Defining schemas, (2) Validating input/output, (3) Generating JSON schema.
Answer data questions -- from quick lookups to full analyses. Use when looking up a single metric, investigating what's driving a trend or drop, comparing segments over time, or preparing a formal data report for stakeholders.
Clean and transform messy data in Stata with reproducible workflows
pytest, data validation, Great Expectations, and quality assurance for data systems
Search and extract contact information for people or companies including names, phone numbers, emails, job titles, and LinkedIn profiles. Aggregates data from multiple sources and provides enriched contact details. Use when users need to find contact information, build prospect lists, or enrich existing contact data.
Comprehensive HPK (proprietary healthcare message format) parser and explainer. Supports 100+ message types across patient administration (ID, MV, CV), supply chain (PR, FO, MA, CO, LI, RO, FA), inventory (SO, IM), organizational structure (ST, UT), and financial operations (RD, DD). Uses @erp-pas/hpk-dictionary as source of truth. Validates structure, extracts fields, explains business context, maps to HL7 v2.5/IHE PAM, and troubleshoots integration issues.
Implement Syncfusion SfNumericTextBox for numeric input with formatting, validation, and customization in Windows Forms. Use when creating numeric input controls with currency formatting, percent values, number validation, or decimal formatting. Covers numeric formatting options, value range validation, and formatted numeric data entry with validation capabilities.
Implement data quality checks, validation rules, and monitoring. Use when ensuring data quality, validating data pipelines, or implementing data governance.
Zod schema validation patterns and type inference. Auto-loads when validating schemas, parsing data, validating forms, checking types at runtime, or using z.object/z.string/z.infer in TypeScript.
Creates dbt models following project conventions. Use when working with dbt models for: (1) Creating new models (any layer - discovers project's naming conventions first) (2) Task mentions "create", "build", "add", "write", "new", or "implement" with model, table, or SQL (3) Modifying existing model logic, columns, joins, or transformations (4) Implementing a model from schema.yml specs or expected output requirements Discovers project conventions before writing. Runs dbt build (not just compile) to verify.
Automatically discover data pipeline and ETL skills when working with ETL, data pipelines, streaming, batch processing, data validation, or pipeline orchestration. Activates for data development tasks.
Use when invalid data causes failures deep in execution - validates at every layer data passes through to make bugs structurally impossible rather than temporarily fixed