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Found 86 Skills
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.
Navigate the Valibot repository structure. Use when looking for files, understanding the codebase layout, finding schema/action/method implementations, locating tests, API docs, or guide pages. Covers monorepo layout, library architecture, file naming conventions, and quick lookups.
Multi-layer validation pattern - validates data at EVERY layer it passes through to make bugs structurally impossible, not just caught.
Use when preparing ANY app for App Store submission - enforces pre-flight checklist, rejection prevention, privacy compliance, and metadata completeness to prevent common App Store rejections
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
Implements WPF data validation using ValidationRule, IDataErrorInfo, and INotifyDataErrorInfo. Use when building forms, validating user input, or displaying validation errors in UI.
This skill should be used when the user asks to "validate data with pydantic", "create a pydantic model", "use pydantic best practices", "write pydantic validators", or needs guidance on pydantic v2 patterns, serialization, configuration, or performance optimization.
The drum sounds. Bear and Bloodhound gather for safe data movement. Use when migrating data that requires both careful movement and codebase understanding.
Use this for SQL queries, database schema design, ETL pipelines, data transformations (pandas/Spark), and data validation.
Use when validating data with Standard Schema-compatible schemas or handling ValidationError results.
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.