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Found 68 Skills
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).
Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.
Compare two datasets to find differences, added/removed rows, changed values. Use for data validation, ETL verification, or tracking changes.
Expert for developing Streamlit data apps for Keboola deployment. Activates when building, modifying, or debugging Keboola data apps, Streamlit dashboards, adding filters, creating pages, or fixing data app issues. Validates data structures using Keboola MCP before writing code, tests implementations with Playwright browser automation, and follows SQL-first architecture patterns.
Use this for SQL queries, database schema design, ETL pipelines, data transformations (pandas/Spark), and data validation.
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.
Validate, format, and convert between JSON, YAML, and TOML. Parse and query structured data files. No API key required.
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.
Expert in data pipelines, ETL processes, and data infrastructure
Validate and audit CSV data for quality, consistency, and completeness. Use when you need to check CSV files for data issues, missing values, or format inconsistencies.
Chapter 2 데이터 수집 품질 기준 및 검증 방법