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Found 70 Skills
Data validation using Great Expectations. Expectation suites, checkpoints, and data docs for pipeline monitoring.
Validate at every layer data passes through to make bugs impossible. Use when invalid data causes failures deep in execution, requiring validation at multiple system layers.
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
Plan a migration onto MotherDuck. Use when moving from Snowflake, Redshift, PostgreSQL, dbt-heavy stacks, or lakehouse tooling and the key decisions are target pattern, cutover slices, validation, rollback, and native-versus-DuckLake posture.
Use to define schemas, topic tags, and lineage metadata for enriched signals.
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.
Validates JSON data against JSON Schema using the z-schema library. Use when the user needs to validate JSON, check data against a schema, handle validation errors, use custom format validators, work with JSON Schema drafts 04 through 2020-12, set up z-schema in a project, compile schemas with cross-references, resolve remote $ref, configure validation options, or inspect error details. Covers sync/async modes, safe error handling, schema pre-compilation, remote references, TypeScript types, and browser/UMD usage.
Run a comprehensive data quality assessment and produce a scorecard across 6 dimensions: completeness, uniqueness, consistency, timeliness, accuracy, validity. Use when the user asks about data quality, mentions data issues, wants to audit a table, is onboarding a new data source, or needs to validate pipeline output.
Python data validation using type hints and runtime type checking with Pydantic v2's Rust-powered core for high-performance validation in FastAPI, Django, and configuration management.
Use this for SQL queries, database schema design, ETL pipelines, data transformations (pandas/Spark), and data validation.
OmniStudio Data Mapper (formerly DataRaptor) creation and validation with 100-point scoring. Use when building Extract, Transform, Load, or Turbo Extract Data Mappers, mapping Salesforce object fields, or reviewing existing Data Mapper configurations. TRIGGER when: user creates Data Mappers, configures field mappings, works with OmniDataTransform metadata, or asks about DataRaptor/Data Mapper patterns. DO NOT TRIGGER when: building Integration Procedures (use sf-industry-commoncore-integration-procedure), authoring OmniScripts (use sf-industry-commoncore-omniscript), or analyzing cross-component dependencies (use sf-industry-commoncore-omnistudio-analyze).
Guidance for counting tokens in datasets, particularly from HuggingFace or similar sources. This skill should be used when tasks involve counting tokens in datasets, understanding dataset schemas, filtering by categories/domains, or working with tokenizers. It helps avoid common pitfalls like incomplete field identification and ambiguous terminology interpretation.